Computer und IT
Large Language Models selbst programmieren
LLMs selbst erstellen und von Grund auf verstehen!Der Bestseller aus den USA ist jetzt endlich in deutscher Übersetzung erhältlich und bietet den idealen Einstieg in die Welt der Large Language Models (LLMs). Auf dem eigenen Laptop entwickeln, trainieren und optimieren Sie ein LLM, das mit GPT-2 vergleichbar ist – ganz ohne vorgefertigte Bibliotheken. Bestsellerautor Sebastian Raschka erklärt die Grundlagen und Vorgehensweise Schritt für Schritt und leicht verständlich.Das Buch nimmt Sie mit auf eine spannende Reise in die Blackbox der generativen KI: Sie programmieren ein LLM-Basismodell, bauen daraus einen Textklassifikator und schließlich einen eigenen Chatbot, den Sie als persönlichen KI-Assistenten nutzen können. Dabei lernen Sie nicht nur die technischen Details kennen, sondern auch, wie Sie Datensätze vorbereiten, Modelle mit eigenen Daten verfeinern und mithilfe von menschlichem Feedback verbessern. Ein praxisnaher Leitfaden für alle, die wirklich verstehen wollen, wie LLMs funktionieren – weil sie ihren eigenen gebaut haben.Über den Autor:Sebastian Raschka, PhD, arbeitet sehr mehr als einem Jahrzehnt im Bereich Machine Learning und KI. Er ist Staff Research Engineer bei Lightning AI, wo er LLM-Forschung betreibt und Open-Source-Software entwickelt. Sebastian ist nicht nur Forscher, sondern hat auch eine große Leidenschaft für die Vermittlung von Wissen. Bekannt ist er für seine Bestseller zu Machine Learning mit Python und seine Beiträge zu Open Source.Richtet sich an:Data ScientistsPython-Programmierer*innen, die sich tiefer in die Funktionsweise von LLMs einarbeiten möchten und ML-Grundkenntnisse haben
IT-Security - Der praktische Leitfaden
Umsetzbare Sicherheitsstrategien – auch für Unternehmen und Organisationen mit kleinen Budgets - Das komplexe Thema »Informationssicherheit« zugänglich und praxisnah aufbereitet. - Umfassend und kompakt: praktische Anleitungen zum Aufbau eines Informationssicherheitsmanagementsystems (ISMS) - komprimierte Alternative zum IT-Grundschutz Obwohl die Zahl der spektakulären Hacks, Datenleaks und Ransomware-Angriffe zugenommen hat, haben viele Unternehmen immer noch kein ausreichendes Budget für Informationssicherheit. Dieser pragmatische Leitfaden unterstützt Sie dabei, effektive Sicherheitsstrategien zu implementieren – auch wenn Ihre Ressourcen finanziell und personell beschränkt sind. Kompakt beschreibt dieses Handbuch Schritte, Werkzeuge, Prozesse und Ideen, mit denen Sie Ihre Sicherheit ohne hohe Kosten verbessern. Jedes Kapitel enthält Schritt-für-Schritt-Anleitungen zu typischen Security-Themen wie Sicherheitsvorfällen, Netzwerkinfrastruktur, Schwachstellenanalyse, Penetrationstests, Passwortmanagement und mehr. Netzwerk techniker, Systemadministratoren und Sicherheitsexpertinnen lernen, wie sie Frameworks, Tools und Techniken nutzen können, um ein Cybersicherheitsprogramm aufzubauen und zu verbessern. Dieses Buch unterstützt Sie dabei: - Incident Response, Disaster Recovery und physische Sicherheit zu planen und umzusetzen - grundlegende Konzepte für Penetrationstests durch Purple Teaming zu verstehen und anzuwenden - Schwachstellenmanagement mit automatisierten Prozessen und Tools durchzuführen - IDS, IPS, SOC, Logging und Monitoring einzusetzen - Microsoft- und Unix-Systeme, Netzwerkinfrastruktur und Passwortverwaltung besser zu sichern - Ihr Netzwerk mit Segmentierungspraktiken in sicherheitsrelevante Zonen zu unterteilen - Schwachstellen durch sichere Code-Entwicklung zu reduzieren
Einstieg in die Datenanalyse mit Excel
Daten analysieren und Prozesse automatisieren mit Excel - Automatisieren Sie Ihre Datenbereinigung in Excel mit Power Query. - Strukturieren Sie Ihre Daten mit Power Pivot. - Nutzen Sie Python für automatisierte Analysen und Berichterstattung. In diesem Praxisbuch führt George Mount Daten- und Business-Analyst*innen durch zwei leistungsstarke in Excel integrierte Tools für die Datenanalyse: Power Pivot und Power Query. Er zeigt Ihnen, wie Sie mit Power Query Workflows zur Datenbereinigung erstellen und mit Power Pivot relationale Datenmodelle entwerfen. Darüber hinaus lernen Sie nützliche Funktionen wie dynamische Array-Formeln, KI-gestützte Methoden zur Aufdeckung von Trends und Mustern sowie die Integration von Python zur Automatisierung von Analysen und Berichten kennen. George Mount zeigt Ihnen, wie Sie Ihre Excel-Kenntnisse weiter ausbauen und das Programm effektiv für die moderne Datenanalyse nutzen. Das Buch gibt einen klaren Überblick über das volle Potenzial von Excel und hilft Ihnen, auch ohne Power BI oder anderer Zusatzsoftware Daten effektiv aufzubereiten und zu analysieren. Viele Übungen zur Vertiefung des Gelernten runden das Buch ab. Dieses Buch zeigt Ihnen, wie Sie: - wiederholbare Datenbereinigungsprozesse für Excel mit Power Query erstellen - relationale Datenmodelle und Analysen mit Power Pivot entwickeln - Daten schnell mit dynamischen Arrays abrufen - KI nutzen, um Muster und Trends in Excel aufzudecken - Python-Funktionen in Excel für automatisierte Analysen und Berichte integrieren
Generative KI im Kontext der Wirtschaftsinformatik
Generative KI stellt eine neue wichtige Technologie dar, die in der Lage ist, betriebliche Prozesse und Anwendungen zu automatisieren und zu optimieren. Inwieweit sich Generative KI wie ChatGPT für den Einsatz im Kontext der Wirtschaftsinformatik eignet, wird im Rahmen dieses Herausgeber-Bandes thematisiert. Dazu untersuchen einige Autoren-Teams den Einsatz von ChatGPT anhand verschiedener Beispiele. Die Beispiele umfassen sowohl Fragestellungen zum Lernen an der Hochschule wie zum Beispiel zur Texterstellung und zur Mathematik als auch zum betrieblichen Einsatz in der unternehmerischen Praxis in verschiedenen betriebswirtschaftlichen Anwendungsfeldern. Das Buch bietet wichtige Informationen, die für Praktiker ebenso relevant sind wie für Studierende und Lehrende..- Teil 1 – Einleitung..- 1 Generative KI im Kontext digitaler Ressourcen, Lehre, Innovation und Kundenbeziehung..- Teil 2 – Digitale Ressourcen und Lehre..- 2 Versionsverwaltung von Dokumenten in Zeiten von Cloud, Cybersecurity und KI..- 3 "Versteht" ein System wie ChatGPT seine eigenen Texte zur Mathematik?..- 4 Ein Vergleich von Wirtschaftsinformatik-Lehrmaterialien mit generierten Erklärungstexten..- Teil 3 – Innovationsmanagement..- 5 Die Zukunft des Technologiescoutings: Wie ein digitales Transfertool von KI-basierter Contentgenerierung profitiert..- 6 Zukunftsszenarien Szenarioanalysen mit ChatGPT – Potenziale und Grenzen KI-gestützter Vorausschau..- Teil 4 – Kundenanalyse und Kundeninteraktion..- 7 Data Science und KI – made by Data Science und KI..- 8 Wie Produktivitätsgewinne in der Kundenbetreuung durch KI-basierte Textgenerierung erzielt werden – heutige und zukünftige Einsatzmöglichkeiten..- 9 Multimodales Fenster in die Vergangenheit der ehemaligen Vauban-Festung Saarlouis mittels ChatGPT.
Emerging Smart Agricultural Practices Using Artificial Intelligence
BRING THE LATEST TECHNOLOGY TO BEAR IN THE FIGHT FOR SUSTAINABLE AGRICULTURE WITH THIS TIMELY VOLUMEArtificial intelligence (AI) has the potential to revolutionize virtually every area of research and scientific practice, including agriculture. With AI solutions emerging to drive higher yields, produce increased resource efficiency, and foster sustainability, there is an urgent need for a volume outlining this progress and charting its future course. Emerging Smart Agricultural Practices Using Artificial Intelligence meets this need with a deep dive into the rapidly developing intersection of agriculture and artificial intelligence. Taking an interdisciplinary approach which applies data science, computer science, and engineering techniques, the book provides cutting-edge insights on the latest advancements in AI-driven agricultural practices. The result is an absolutely critical tool in the ongoing fight to develop sustainable world agriculture. In addition, this book provides:* Case studies and real-world applications of new techniques throughout* Detailed discussion of agricultural applications for AI-driven technologies such as machine learning, computer vision, and data analytics * A regional approach showcasing international best practices and addressing the varying needs of farmers worldwideEmerging Smart Agricultural Practices Using Artificial Intelligence is ideal for agricultural professionals and scientists, as well as data scientists, technologists, and agricultural policymakers. ASHISH KUMAR, PHD, is an Associate Professor with Bennett University, Greater Noida, U.P. India. He has published widely on subjects including object tracking, image processing, artificial intelligence, and medical imaging analysis, and is a member of the IEEE. JAI PRAKASH VERMA, PHD, is an Associate Professor in the Department of Computer Science and Engineering, Nirma University, Ahmedabad, India. He offers customized training on big data analytics to the Indian Navy, SAC-ISRO scientists in Ahmedabad, and other experts from industry and academia. RACHNA JAIN, PHD, is an Associate Professor in the Department of Information Technology Bhagwan Parshuram Institute of Technology. She has 18+ years of academic/research experience with more than 100+ publications in various international conferences and international journals (Scopus/ISI/SCI) of high repute.
Shell Script Programmierung
Dein Weg zum Shell-Script-MeisterShell Scripting zu lernen ist wie das Meistern einer Kampfkunst – es erfordert Übung, Struktur und ein solides Fundament. Dieses Buch richtet sich an Systemadministratoren, Entwickler und Studierende, die Shell Scripting von Grund auf lernen und praktisch anwenden möchten. Jedes Kapitel vermittelt praxisnahe Techniken, mit denen du deine Fähigkeiten systematisch aufbaust.Trainingsplan für die PraxisDu startest mit den Grundlagen der Syntax und den wichtigsten Terminal-Befehlen. Danach lernst du den Umgang mit Variablen, Kontrollstrukturen, Funktionen und der Verarbeitung von Dateien. Fortgeschrittene Themen wie Prozesssteuerung, zeitgesteuerte Aufgaben mit Cron und andere Automatisierungstechniken runden dein Training ab.Mit Übungen und Prüfungen zum schwarzen GürtelNach jedem Kapitel stellst du dich einer Gürtelprüfung – einer herausfordernden Übungsaufgabe, um dein Wissen zu festigen. So erarbeitest du dir Schritt für Schritt alle Grundlagen der Shell-Script-Programmierung, die im Admin- und DevOps-Alltag unverzichtbar sind.
Maschinelles Lernen lernen
Maschinelles Lernen prägt zunehmend unseren Alltag, doch fehlt bislang eine Fachdidaktik, die das Thema als Lerngegenstand für Schulen und Hochschulen systematisch untersucht. In der vorliegenden Arbeit werden Methoden der fachdidaktische Entwicklungsforschung genutzt, um unter Einbezug verschiedener Naturwissenschaftsdidaktiken eine Lehr-Lernumgebung zu maschinellem Lernen für Ingenieurstudierende zu entwickeln. Die qualitative und quantitative empirische Untersuchung des entwickelten Materials in Design-Experimenten liefert Einsichten in die Lernprozesse der Studierenden, um den didaktischen Diskurs zu bereichern und Hinweise für die Lehrpraxis abzuleiten.Einleitung.- Maschinelles Lernen.- Spezifizierung und Strukturierung der Lerngegenstände.- Design(weiter)entwicklung.- Designentwicklung und Datenerhebung.- Methoden der Datenanalyse.- Die Wirksamkeitsanalyse.- Evaluation der Mikrozyklen.- Erstellung und Nutzung von ML-Modellen.- Das individuelle Modellkonzept.- Die Weiterentwicklung des Designs und der Wirksamkeitszyklus.- Zusammenfassung und Diskussion.- Ausblick.
Herman Hollerith Conference 2024
Die digitale Transformation verändert viele Organisationen durch den Einfluss von künstlicher Intelligenz (KI) und anderen zukunftsweisenden digitalen Technologien, die bestehende Organisationen und Volkswirtschaften tiefgreifend verändern. Der Kern der intelligenten Digitalisierung bietet neue Perspektiven für hochautomatisierte intelligente Systeme, Dienstleistungen, Produkte und Anwendungen sowie deren menschenzentrierten Kontext bis hin zu ganzen betroffenen Volkswirtschaften und Gesellschaften. Das Potenzial von KI und Kognition in Verbindung mit intelligenter Datenanalyse für die nächste Stufe der Digitalisierung in Bereichen wie maschinelles Lernen, Verarbeitung natürlicher Sprache, generative KI, Computer Vision und Augmented Reality fördert die digitale Innovation und bietet eine wesentliche Orientierungshilfe für intelligente digitale Strategien, fortschrittliche, auf Stakeholder ausgerichtete Geschäftsmodelle und digitale Architekturen, die die Grundlage für verbesserte Wertangebote und echteArtificial Intelligence / Künstliche Intelligenz.- Enterprise- & IT-Architektur.- Digital Business.- Cloud Computing.- Deployment Automatisierung.- Software-Defined Vehicles.- Software-Defined Enterprises.
KI Kann ich: Zwischen Prompt und Purpose
KI kann ich: Zwischen Prompt und PurposeWie wir mit KI sinnvoller denken, arbeiten und wirkenIn einer Ära der digitalen Renaissance öffnet das Buch "KI kann ich: Zwischen Prompt und Purpose" von Roger Basler de Roca den Leser:Innen die Tür zu einem reflektierten und praxisnahen Umgang mit Künstlicher Intelligenz (KI).Statt sich auf rein technische Erklärungen zu beschränken, beleuchtet Basler de Roca die tiefere Beziehung zwischen Mensch und Maschine: Wie beeinflusst KI unser Denken, Handeln und Zusammenleben? Der Autor zeigt auf, dass KI nicht wirklich "intelligent" im menschlichen Sinne ist, sondern eine leistungsstarke Form der Pseudointelligenz darstellt - Systeme, die Muster erkennen, aber nicht verstehen.Im Zentrum des Buches steht das Konzept des Promptings: Die gezielte Eingabe von Informationen in KI-Systeme wird hier als essenzielle neue Schlüsselkompetenz vorgestellt. Basler de Roca erläutert klar und strukturiert, wie durch bewusstes Prompt-Design qualitativ hochwertige Ergebnisse erzielt werden können und warum dabei Höflichkeit und präzise Kontextsetzung entscheidend sind.Neben den Chancen stellt das Buch auch die Schattenseiten der KI offen dar: Datenschutzprobleme, algorithmische Verzerrungen und ethische Dilemmata werden kritisch, aber lösungsorientiert diskutiert. Ein besonderes Augenmerk liegt auf der Frage, wie wir KI verantwortungsvoll einsetzen können, ohne menschliche Werte und Autonomie zu gefährden.Basler de Roca entwickelt einen Leitfaden für Unternehmen und Einzelpersonen: 10 Regeln für einen verantwortungsvollen KI-Einsatz. Dazu betont er die Rolle von KI-Botschafter:Innen in Organisationen, um Wissen zu verbreiten und die transformative Kraft der KI aktiv zu gestalten.Das Werk ist nicht nur ein fundierter Einstieg in das Thema KI, sondern auch ein inspirierender Appell: Künstliche Intelligenz soll nicht unser Denken ersetzen, sondern es erweitern. Zwischen Prompt und Purpose liegt die Zukunft - und die Verantwortung dafür tragen wir selbst.Roger Basler de Roca:Roger Basler de Roca ist Betriebsökonom FH und Master of Science in Digital Business. Er gilt als einer der führenden Digitalunternehmer und Top Keynote Speaker im deutschsprachigen Raum.Mit jahrzehntelanger Erfahrung in der Umsetzung digitaler Geschäftsmodelle, Plattformökonomien und Anwendungen von Künstlicher Intelligenz unterstützt Basler de Roca Unternehmen, Behörden und Bildungseinrichtungen bei der erfolgreichen digitalen Transformation.Sein besonderer Fokus liegt auf der intelligenten Verzahnung von Technologie, Strategie und menschlicher Kreativität. In mehr als zehn Jahren Auslandserfahrung - unter anderem in China, Kanada, Spanien und den USA - hat er ein ausgeprägtes interkulturelles Verständnis für die Chancen und Herausforderungen globaler Digitalisierung entwickelt.Roger Basler de Roca hält jährlich über 100 Vorträge und Workshops. Seine Arbeit ist geprägt von einem klaren Ziel: Menschen und Organisationen zu befähigen, KI nicht nur als Werkzeug, sondern als strategischen Hebel für nachhaltiges Wachstum und gesellschaftlichen Fortschritt zu nutzen.Weitere Informationen über ihn und seine Projekte finden sich auf: www.rogerbasler.ch
AWS Certified Machine Learning Engineer Study Guide
PREPARE FOR THE AWS MACHINE LEARNING ENGINEER EXAM SMARTER AND FASTER AND GET JOB-READY WITH THIS EFFICIENT AND AUTHORITATIVE RESOURCEIn AWS Certified Machine Learning Engineer Study Guide: Associate (MLA-C01) Exam, veteran AWS Practice Director at Trace3—a leading IT consultancy offering AI, data, cloud and cybersecurity solutions for clients across industries—Dario Cabianca delivers a practical and up-to-date roadmap to preparing for the MLA-C01 exam. You'll learn the skills you need to succeed on the exam as well as those you need to hit the ground running at your first AI-related tech job.You'll learn how to prepare data for machine learning models on Amazon Web Services, build, train, refine models, evaluate model performance, deploy and secure your machine learning applications against bad actors.INSIDE THE BOOK:* Complimentary access to the Sybex online test bank, which includes an assessment test, chapter review questions, practice exam, flashcards, and a searchable key term glossary* Strategies for selecting and justifying an appropriate machine learning approach for specific business problems and identifying the most efficient AWS solutions for those problems* Practical techniques you can implement immediately in an artificial intelligence and machine learning (AI/ML) development or data science rolePerfect for everyone preparing for the AWS Certified Machine Learning Engineer -- Associate exam, AWS Certified Machine Learning Engineer Study Guide is also an invaluable resource for those preparing for their first role in AI or data science, as well as junior-level practicing professionals seeking to review the fundamentals with a convenient desk reference.ABOUT THE AUTHORDARIO CABIANCA is the AWS Practice Director at Trace3—a leading IT consultancy and AWS Advanced Consulting Partner—offering AI, data, cloud and cybersecurity solutions. He is the author of Google Cloud Platform (GCP) Professional Cloud Security Engineer Certification Companion and Google Cloud Platform (GCP) Professional Cloud Network Engineer Certification Companion. Dario has collaborated with leading global consulting firms and enterprises for over 20 years, delivering impactful solutions in enterprise architecture, cloud computing, cybersecurity, and artificial intelligence. ContentsChapter 1Introduction to Machine Learning1Understanding Artificial Intelligence2Data, Information, Knowledge3Data3Information4Knowledge5Understanding Machine Learning6ML Lifecycle6Define ML Problem6Collect Data8Process Data8Choose Algorithm8Train Model9Evaluate Model9Deploy Model9Derive Inference11Monitor Model11ML Concepts11Features11Target Variable12Optimization Problem12Objective Function13ML Algorithms vs. ML Models13Differences Between ML and AI14Understanding Deep Learning16Introduction to Neural Networks16Structure of a Neural Network16Neuron16Input Layer18Hidden Layers18Output Layer18How Neural Networks Work18Neural Networks Types19Artificial Neural Networks20Deep Neural Networks20Convolutional Neural Networks20Recurrent Neural Networks20Differences Between DL and ML21Case Studies21Case Study 1: Mobileye’s Autonomous Driving Technology21Case Study 2: Leidos’ Healthcare ML Applications21Summary22Exam Essentials23Review Questions24Chapter 2Data Ingestion and Storage27Introducing Ingestion and Storage28Ingesting and Storing Data28Data Formats and Ingestion Techniques31Choosing AWS Ingestion Services34Amazon Data Firehose35Amazon Kinesis Data Streams35Amazon Managed Streaming for Apache Kafka (MSK)36Amazon Managed Service for Apache Flink38AWS DataSync39AWS Glue40Choosing AWS Storage Services41Amazon Simple Storage Service (S3)42Amazon Elastic File System (EFS)45Amazon FSx for Lustre47Amazon FSx for NetApp ONTAP49Amazon FSx for Windows File Server50Amazon FSx for OpenZFS51Amazon Elastic Block Storage (EBS)51Amazon Relational Database Service (RDS)52Amazon DynamoDB52Troubleshooting53Summary54Exam Essentials55Review Questions57Chapter 4Model Selection61Understanding AWS AI Services63Vision64Amazon Rekognition64Amazon Textract65Speech66Amazon Polly66Amazon Transcribe67Language67Amazon Translate67Amazon Comprehend68Chatbot69Amazon Lex69Recommendation70Amazon Personalize70Generative AI71Amazon Bedrock71Developing Models with Amazon SageMaker Built-in Algorithms81Supervised ML Algorithms81General Regression and Classification Algorithms83Recommendation102Forecasting104Unsupervised ML Algorithms105Clustering105Dimensionality Reduction113Topic Modeling119Anomaly Detection121Textual Analysis123BlazingText124Sequence-to-Sequence126Image Processing127Image Classification127Object Detection128Semantic Segmentation130Criteria for Model Selection131Summary132Exam Essentials133Review Questions136Chapter 5Model Training and Evaluation141Training143Local Training144Remote Training145Distributed Training146Monitoring Training Jobs147Debugging Training Jobs148Hyperparameter Tuning149Model Parameter and Hyperparameter151Exploring the Hyperparameter Space with Amazon SageMaker AI Automatic Model Tuning152Evaluation Metrics154Classification Problem Metrics154Regression Problem Metrics160Hyperparameter Tuning Techniques164Manual Search164Grid Search165Random Search165Bayesian Search165Multi-algorithm Optimization166Managing Bias and Variance Trade-Off166Addressing Overfitting and Underfitting168Underfitting168Overfitting170Regularization170Advanced Techniques173Model Performance Evaluation173Performance Evaluation Methods173K-Fold Cross-Validation174Random Train-Test Split175Holdout Set176Bootstrap176Evaluating Foundation Models177Automatic Evaluations177Human Evaluations177LLM-as-a-Judge177Programmatic Evaluations177Knowledge Base Evaluations177Deep Dive Model Tuning Example177Summary185Exam Essentials187Review Questions190Chapter 6Model Deployment and Orchestration193AWS Model Deployment Services194Deploying AI Services195Amazon Rekognition196Amazon Textract197Amazon Polly197Amazon Transcribe198Amazon Comprehend198Amazon Lex199Amazon Personalize199Amazon Bedrock200Deploying Your Model201Infrastructure Selection Considerations202Managed Model Deployments203Unmanaged Model Deployments211Optimizing ML Models for Edge Devices216Advanced Model Deployment Techniques218Autoscaling Endpoints218Deployment and Testing Strategies221Blue/Green Deployment221Orchestrating ML Workflows227Introducing Amazon SageMaker Pipelines228Code Repository and Version Control228Introducing Amazon SageMaker Model Registry229CI/CD230MLOps Orchestration230AWS Step Functions231Amazon Managed Workflows for Apache Airflow232Choosing an Orchestration Tool232Automating Model Building and Deployment233Define the Workflow Steps234Create and Configure Pipeline Steps234Define the Pipeline237Set Up Triggers and Schedules237Execute the Pipeline238Key Considerations238Deep-Dive Model Deployment Example238Summary247Exam Essentials248Review Questions250Chapter 7Model Monitoring and Cost Optimization253Monitoring Model Inference255Drifts in Models256Techniques to Monitor Data Quality and Model Performance257Monitoring Workflow259Design Principles for Monitoring261Operational Excellence Pillar261Security Pillar262Reliability Pillar263Performance Efficiency Pillar264Cost Optimization Pillar266Sustainability Pillar269Monitoring Infrastructure and Cost270Monitoring and Observability Services271Amazon CloudWatch Logs Insights272Amazon EventBridge273AWS CloudTrail274AWS X-Ray274Amazon GuardDuty275Amazon Inspector276AWS Security Hub277Cost Tracking and Optimization Services278AWS Cost Explorer278AWS Cost and Usage Reports279AWS Trusted Advisor280AWS Budgets280Pricing Models281Summary283Exam Essentials284Review Questions286Chapter 8Model Security289Security Design Principles290Implement a Strong Identity Foundation290Apply Security at all Layers291Enable Traceability292Protect Your Data (At-Rest, In-Use, and In-Transit)293Automate Security Processes294Prepare for Security Events295Securing AWS Services295Securing Identities with IAM296Identities296Access Policies302Securing Infrastructure and Data305Network Isolation with VPC305Private Connectivity306Data Protection306Monitoring and Auditing307Ensuring Compliance307Summary308Exam Essentials309Review Questions311
IT-Security - Der praktische Leitfaden
Umsetzbare Sicherheitsstrategien – auch für Unternehmen und Organisationen mit kleinen BudgetsDas komplexe Thema »Informationssicherheit« wird in diesem praxisnahen Leitfaden verständlich und kompakt aufbereitet. Das Buch bietet eine umfassende, aber schlanke Alternative zum IT-Grundschutz und liefert praktische Anleitungen für den Aufbau eines Informationssicherheitsmanagementsystems (ISMS). Es richtet sich an Netzwerktechniker, Systemadministratoren und Sicherheitsexpertinnen, die trotz begrenzter Ressourcen effektive Sicherheitsstrategien umsetzen möchten. Die kompakten Kapitel behandeln alle relevanten Bereiche – von Logging, Monitoring und Intrusion Detection über Schwachstellenmanagement und Penetrationstests bis hin zu Disaster Recovery, physischer Sicherheit und Mitarbeitendenschulungen – und liefern direkt umsetzbare Lösungen für den Arbeitsalltag.Über die Autoren:Amanda Berlin ist Sicherheitsexpertin und Referentin. Sie ist Principal Detection und Product Manager bei Blumira und leitet ein Forschungs- und Entwicklungsteam, das daran arbeitet, Sicherheitsprobleme schneller zu identifizieren und die Sicherheitslandschaft insgesamt zu verbessern.Lee Brotherston ist gründender Security Engineer bei OpsHelm. Lee hat fast zwei Jahrzehnte im Bereich der Informationssicherheit gearbeitet und war als interner Sicherheitsbeauftragter in vielen Branchen tätig - in verschiedenen Funktionen vom Engineer bis zum IT-Sicherheitsmanager.William F. Reyor III ist Sicherheitsexperte und Director of Security bei Modus Create. Er bekleidete Schlüsselpositionen bei The Walt Disney Company und Raytheon Technologies und war Chief Information Officer an der Fairfield University. Er ist einer der Hauptorganisatoren der Security BSides Connecticut.Richtet sich an: Netzwerktechniker*innenSystemadministrator*innenSicherheitsexpert*innen
96 Common Challenges in Power Query
This comprehensive guide is designed to address the most frequent and challenging issues faced by users of Power Query, a powerful data transformation tool integrated into Excel, Power BI, and Microsoft Azure. By tackling 96 real-world problems with practical, step-by-step solutions, this book is an essential resource for data analysts, Excel enthusiasts, and Power BI professionals. It aims to enhance your data transformation skills and improve efficiency in handling complex data sets.Structured into 12 chapters, the book covers specific areas of Power Query such as data extraction, referencing, column splitting and merging, sorting and filtering, and pivoting and unpivoting tables. You will learn to combine data from Excel files with varying column names, handle multi-row headers, perform advanced filtering, and manage missing values using techniques such as linear interpolation and K-nearest neighbors (K-NN) imputation. The book also dives into advanced Power Query functions such as Table.Group, List.Accumulate, and List.Generate, explored through practical examples such as calculating running totals and implementing complex grouping and iterative processes. Additionally, it covers crucial topics such as error-handling strategies, custom function creation, and the integration of Python and R with Power Query.In addition to providing explanations on the use of functions and the M language for solving real-world challenges, this book discusses optimization techniques for data cleaning processes and improving computational speed. It also compares the execution time of functions across different patterns and proposes the optimal approach based on these comparisons.In today’s data-driven world, mastering Power Query is crucial for accurate and efficient data processing. But as data complexity grows, so do the challenges and pitfalls that users face. This book serves as your guide through the noise and your key to unlocking the full potential of Power Query. You’ll quickly learn to navigate and resolve common issues, enabling you to transform raw data into actionable insights with confidence and precision.WHAT YOU WILL LEARN* Master data extraction and transformation techniques for various Excel file structures* Apply advanced filtering, sorting, and grouping methods to organize and analyze data* Leverage powerful functions such as Table.Group, List.Accumulate, and List.Generate for complex transformations* Optimize queries to execute faster* Create and utilize custom functions to handle iterative processes and advanced list transformation* Implement effective error-handling strategies, including removing erroneous rows and extracting error reasons* Customize Power Query solutions to meet specific business needs and share custom functions across filesWHO THIS BOOK IS FORAspiring and developing data professionals using Power Query in Excel or Power BI who seek practical solutions to enhance their skills and streamline complex data transformation workflowsOMID MOTAMEDISEDEH is a seasoned data analyst, educator, and author with extensive experience in business intelligence and data visualization. Based in Australia, he specializes in leveraging Microsoft tools to provide practical solutions for complex data challenges. Omid is the author of eight books, seven written in Persian and one in English, covering a wide range of topics, including Excel, data visualization, and Power Query. His works are known for their clear explanations and actionable insights, catering to data professionals, students, and educators alike. Holding a PhD in Industrial Engineering, Omid combines academic depth with over a decade of hands-on experience in the IT industry, where he has served as a consultant and manager. His professional journey includes implementing data-driven strategies, optimizing processes, and mentoring teams to achieve their analytical goals. Omid is deeply committed to education and knowledge sharing. Beyond writing, he conducts workshops, creates YouTube tutorials, and mentors aspiring data analysts.Chapter 1: Data Extraction from Files.- Chapter 2: Referencing.- Chapter 3: Sorting & Filtering.- Chapter 4: Column Splitting & Merging.- Chapter 5: Pivoting & Unpivoting Tables.- Chapter 6: Grouping Rows with Table.Group.- Chapter 7: Merging & Appending Tables.- Chapter 8: Handling Missing Values.- Chapter 9: Looping in Power Query.- Chapter 10: Leveraging Scripting and External Integrations in Power Query.- Chapter 11: Error Handling Strategies.- Chapter 12: Custom Functions.
Software Engineering Made Easy
Learn how to write good code for humans. This user-friendly book is a comprehensive guide to writing clear and bug-free code. It integrates established programming principles and outlines expert-driven rules to prevent you from over-complicating your code.You’ll take a practical approach to programming, applicable to any programming language and explore useful advice and concrete examples in a concise and compact form. Sections on Single Responsibility Principle, naming, levels of abstraction, testing, logic (if/else), interfaces, and more, reinforce how to effectively write low-complexity code. While many of the principles addressed in this book are well-established, it offers you a single resource._Software Engineering Made Easy_ modernizes classic software programming principles with quick tips relevant to real-world applications. Most importantly, it is written with a keen awareness of how humans think. The end-result is human-readable code that improves maintenance, collaboration, and debugging—critical for software engineers working together to make purposeful impacts in the world.WHAT YOU WILL LEARN* Understand the essence of software engineering.* Simplify your code using expert techniques across multiple languages.* See how to structure classes.* Manage the complexity of your code by using level abstractions.* Review test functions and explore various types of testing.WHO THIS BOOK IS FORIntermediate programmers who have a basic understanding of coding but are relatively new to the workforce. Applicable to any programming language, but proficiency in C++ or Python is preferred. Advanced programmers may also benefit from learning how to deprogram bad habits and de-complicate their code.MARCO GÄHLER began his career studying physics at ETH Zurich before transitioning to software engineering. In 2018, he joined Zurich Instruments, where he developed electronic devices used in quantum computing. Throughout his career, Marco has observed the pitfalls in code written by self-taught developers, for example PhD students, and recognized the need for clear, practical guidance on simple programming practices. This book reflects his preference for clear, short functions, and minimal class usage, aiming to make good programming practices accessible to all.Chapter 1: Fundamentals of Software Engineering.- Chapter 2: Components of Code.- Chapter 3: Classes.- Chapter 4:Testing.- Chapter 5: Design Principles.- Chapter 6: Programming.- Chapter 7: High-Level Design.- Chapter 8: Refactoring.- Chapter 9: Other Common Topics.- Chapter 10: Collaborating.- Glossary.
Pro Cloud-Native Java EE Apps
Learn how to build and deploy Java-based cloud native apps with Jakarta EE with the MicroProfile framework and Kubernetes. This revamped Second Edition reflects the latest updates in Jakarta EE 11, including enhanced support for creating web APIs with Jakarta REST, concurrency management with Jakarta Concurrency, and data persistence with Jakarta Persistence, while incorporating key changes introduced by MicroProfile 7.0.After a quick overview of Jakarta EE and MicroProfile, Pro Cloud Native Java EE Apps starts you on your way by walking you through a cloud-native Jakarta EE-based application case study that will be forged piece-by-piece over the course of the book. Next, you'll interject dependencies and data persistence capabilities as microservices to go with the case study app that you are building. Then, you will dive into migrating a monolith to become a production-ready cloud-native app.Finally, you will look ahead to the future of Jakarta EE with a NEW chapter on artificial intelligence and large language models, exploring potential use cases for how AI-integration can enhance Jakarta EE capabilities. This revised new edition ensures you are equipped with the most current tools and techniques to develop forward-looking, cloud-native apps.WHAT YOU WILL LEARN● Build and deploy a production-ready cloud-native Java app using MicroProfile, Jakarta EE and Kubernetes● Migrate a monolith app to become a cloud-native app● Employ Jakarta EE APIs such as Persistence, CDI and more● Leverage the MicroProfile framework● Explore configurations, resilience, metrics, health, security, and more for your cloud-native apps● Discover how Jakarta EE integrates with AI and LLMs.WHO THIS BOOK IS FOR:Those software developers and programmers with at least some prior experience using Jakarta EE, MicroProfile. At least some prior Java experience is expected.LUQMAN SAEED is Senior Java Developer with 7+ years of experience building scalable cloud-native applications using Spring Boot, Quarkus, and Jakarta EE. Proficient in diverse architectural styles including microservices, monoliths, and moduliths. Skilled in RESTful API design and CI/CD implementation. He is also a published author and creator of the largest Enterprise Java course on Udemy, reaching over 20,000 developers globally. Technical writer and conference speaker dedicated to making complex concepts accessible. Committed to driving innovation in Enterprise Java development and sharing knowledge with the community.GHAZY ABDALLAH is a passionate enterprise Java developer, founder of the Sudan Java User Group (SudanJUG), and a tinkerer. He enjoys the use of modern cloud infrastructure to deliver enterprise applications and site reliability engineering. Hespends his time learning and sharing the latest in cloud-native development.1. Jakarta EE Foundations: Theory and Core Concepts.- 2. Jakarta EE, Microservices, and Cloud-Native Synergy.- 3. Architecting Enterprise Applications with Jakarta EE.- 4. Decoupling with Jakarta CDI: Dependency Injection Mastery.- 5. Data Persistence with Jakarta Data: Modern Persistence Patterns.- 6. Building RESTful Web Services with Jakarta REST.- 7. Jakarta EE Configuration: Flexibility and Best Practices.- 8. Fault Tolerance and Resilience with Jakarta EE.- 9. Metrics for Jakarta EE Applications: Monitoring and Observability.- 10. Health Checks for Cloud-Native Jakarta EE Applications.- 11. Securing Jakarta EE with JWT: Cloud-Native Authentication.- 12. Jakarta EE Testing with Testcontainers: Streamlined Integration Testing.- 13. Cloud-Native Deployments with Jakarta EE: Kubernetes and Beyond.- 14. Monoliths, Moduliths, and Microservices with Jakarta EE: Architectural Patterns.- 15. AI, LLMs, and Jakarta EE: The Next Frontier.
Microsoft Power Platform Solution Architect Certification Companion
This comprehensive guide book equips you with the knowledge and confidence needed to prep for the exam and thrive as a Power Platform Solution Architect.The book starts with a foundation for successful solution architecture, emphasizing essential skills such as requirements gathering, governance, and security. You will learn to navigate customer discovery, translate business needs into technical requirements, and design solutions that address both functional and non-functional needs. The second part of the book delves into the Microsoft Power Platform ecosystem, offering an in-depth look at its core components—Power Apps, Power Automate, Power BI, Microsoft Copilot, and Robotic Process Automation (RPA).Detailed insights into data modeling, security strategies, and AI integration will guide you in building scalable, secure solutions. Coverage of application life cycle management, which empowers solution architects to design, implement, and deploy Power Platform solutions effectively, is discussed next. You will then go through real-world scenarios, giving you a practical understanding of the challenges and considerations in managing Power Platform projects within a business context.The book concludes with strategies for continuous learning and resources for professional development, including practice questions to assess knowledge and readiness for the PL-600 exam. After reading the book, you will be ready to take the exam and become a successful Power Platform Solution Architect.WHAT YOU WILL LEARN* Understand the Solution Architect's role, responsibilities, and strategic approaches to successfully navigate projects* Master the basics of Power Platform Solution Architecture* Understand governance, security, and integration concepts in real-world scenarios* Design and deploy effective business solutions using Power Platform components* Gain the skills necessary to prep for the PL-600 certification examWHO THIS BOOK IS FORProfessionals pursuing Microsoft PL-600 Solution Architect certification and IT consultants and developers transitioning to solution architect rolesLOGANATHAN K is a seasoned Microsoft Certified Trainer (MCT) and Functional Consultant with extensive experience in Power Platform, Dynamics 365, and business process automation. Currently working as a Functional Consultant in a Microsoft Partner company, he is passionate about helping organizations drive digital transformation through the Power Platform and Microsoft Business Applications.As the author of the popular blog LK Techs (lktechs.com), he shares knowledge of Microsoft technologies, certification paths, and real-world use cases to help individuals build careers in IT. He holds several advanced certifications, including those in Microsoft Business Central and the Power Platform, and regularly conducts training sessions for students, professionals, and educators.Chapter 1: Getting Started with the PL-600 Exam: Overview and Essentials.- Chapter 2: Building a Successful Solution Architect Framework: Key Stages and Skills.- Chapter 3: Governance, Architecture, and Core Components in Power Platform and Dynamics 365.- Chapter 4: Leveraging Microsoft Copilot, RPA, and Securing Data Models in Power Platform Solutions.- Chapter 5: Implementing Analytics, AI, and ALM Strategies for Power Platform Success.- Chapter 6: Evaluating Your Expertise Through Real-World Scenarios.
Emerging Smart Agricultural Practices Using Artificial Intelligence
BRING THE LATEST TECHNOLOGY TO BEAR IN THE FIGHT FOR SUSTAINABLE AGRICULTURE WITH THIS TIMELY VOLUMEArtificial intelligence (AI) has the potential to revolutionize virtually every area of research and scientific practice, including agriculture. With AI solutions emerging to drive higher yields, produce increased resource efficiency, and foster sustainability, there is an urgent need for a volume outlining this progress and charting its future course. Emerging Smart Agricultural Practices Using Artificial Intelligence meets this need with a deep dive into the rapidly developing intersection of agriculture and artificial intelligence. Taking an interdisciplinary approach which applies data science, computer science, and engineering techniques, the book provides cutting-edge insights on the latest advancements in AI-driven agricultural practices. The result is an absolutely critical tool in the ongoing fight to develop sustainable world agriculture. In addition, this book provides:* Case studies and real-world applications of new techniques throughout* Detailed discussion of agricultural applications for AI-driven technologies such as machine learning, computer vision, and data analytics * A regional approach showcasing international best practices and addressing the varying needs of farmers worldwideEmerging Smart Agricultural Practices Using Artificial Intelligence is ideal for agricultural professionals and scientists, as well as data scientists, technologists, and agricultural policymakers. ASHISH KUMAR, PHD, is an Associate Professor with Bennett University, Greater Noida, U.P. India. He has published widely on subjects including object tracking, image processing, artificial intelligence, and medical imaging analysis, and is a member of the IEEE. JAI PRAKASH VERMA, PHD, is an Associate Professor in the Department of Computer Science and Engineering, Nirma University, Ahmedabad, India. He offers customized training on big data analytics to the Indian Navy, SAC-ISRO scientists in Ahmedabad, and other experts from industry and academia. RACHNA JAIN, PHD, is an Associate Professor in the Department of Information Technology Bhagwan Parshuram Institute of Technology. She has 18+ years of academic/research experience with more than 100+ publications in various international conferences and international journals (Scopus/ISI/SCI) of high repute.
AWS Certified Machine Learning Engineer Study Guide
PREPARE FOR THE AWS MACHINE LEARNING ENGINEER EXAM SMARTER AND FASTER AND GET JOB-READY WITH THIS EFFICIENT AND AUTHORITATIVE RESOURCEIn AWS Certified Machine Learning Engineer Study Guide: Associate (MLA-C01) Exam, veteran AWS Practice Director at Trace3—a leading IT consultancy offering AI, data, cloud and cybersecurity solutions for clients across industries—Dario Cabianca delivers a practical and up-to-date roadmap to preparing for the MLA-C01 exam. You'll learn the skills you need to succeed on the exam as well as those you need to hit the ground running at your first AI-related tech job.You'll learn how to prepare data for machine learning models on Amazon Web Services, build, train, refine models, evaluate model performance, deploy and secure your machine learning applications against bad actors.INSIDE THE BOOK:* Complimentary access to the Sybex online test bank, which includes an assessment test, chapter review questions, practice exam, flashcards, and a searchable key term glossary* Strategies for selecting and justifying an appropriate machine learning approach for specific business problems and identifying the most efficient AWS solutions for those problems* Practical techniques you can implement immediately in an artificial intelligence and machine learning (AI/ML) development or data science rolePerfect for everyone preparing for the AWS Certified Machine Learning Engineer -- Associate exam, AWS Certified Machine Learning Engineer Study Guide is also an invaluable resource for those preparing for their first role in AI or data science, as well as junior-level practicing professionals seeking to review the fundamentals with a convenient desk reference.ABOUT THE AUTHORDARIO CABIANCA is the AWS Practice Director at Trace3—a leading IT consultancy and AWS Advanced Consulting Partner—offering AI, data, cloud and cybersecurity solutions. He is the author of Google Cloud Platform (GCP) Professional Cloud Security Engineer Certification Companion and Google Cloud Platform (GCP) Professional Cloud Network Engineer Certification Companion. Dario has collaborated with leading global consulting firms and enterprises for over 20 years, delivering impactful solutions in enterprise architecture, cloud computing, cybersecurity, and artificial intelligence. ContentsChapter 1Introduction to Machine Learning1Understanding Artificial Intelligence2Data, Information, Knowledge3Data3Information4Knowledge5Understanding Machine Learning6ML Lifecycle6Define ML Problem6Collect Data8Process Data8Choose Algorithm8Train Model9Evaluate Model9Deploy Model9Derive Inference11Monitor Model11ML Concepts11Features11Target Variable12Optimization Problem12Objective Function13ML Algorithms vs. ML Models13Differences Between ML and AI14Understanding Deep Learning16Introduction to Neural Networks16Structure of a Neural Network16Neuron16Input Layer18Hidden Layers18Output Layer18How Neural Networks Work18Neural Networks Types19Artificial Neural Networks20Deep Neural Networks20Convolutional Neural Networks20Recurrent Neural Networks20Differences Between DL and ML21Case Studies21Case Study 1: Mobileye’s Autonomous Driving Technology21Case Study 2: Leidos’ Healthcare ML Applications21Summary22Exam Essentials23Review Questions24Chapter 2Data Ingestion and Storage27Introducing Ingestion and Storage28Ingesting and Storing Data28Data Formats and Ingestion Techniques31Choosing AWS Ingestion Services34Amazon Data Firehose35Amazon Kinesis Data Streams35Amazon Managed Streaming for Apache Kafka (MSK)36Amazon Managed Service for Apache Flink38AWS DataSync39AWS Glue40Choosing AWS Storage Services41Amazon Simple Storage Service (S3)42Amazon Elastic File System (EFS)45Amazon FSx for Lustre47Amazon FSx for NetApp ONTAP49Amazon FSx for Windows File Server50Amazon FSx for OpenZFS51Amazon Elastic Block Storage (EBS)51Amazon Relational Database Service (RDS)52Amazon DynamoDB52Troubleshooting53Summary54Exam Essentials55Review Questions57Chapter 4Model Selection61Understanding AWS AI Services63Vision64Amazon Rekognition64Amazon Textract65Speech66Amazon Polly66Amazon Transcribe67Language67Amazon Translate67Amazon Comprehend68Chatbot69Amazon Lex69Recommendation70Amazon Personalize70Generative AI71Amazon Bedrock71Developing Models with Amazon SageMaker Built-in Algorithms81Supervised ML Algorithms81General Regression and Classification Algorithms83Recommendation102Forecasting104Unsupervised ML Algorithms105Clustering105Dimensionality Reduction113Topic Modeling119Anomaly Detection121Textual Analysis123BlazingText124Sequence-to-Sequence126Image Processing127Image Classification127Object Detection128Semantic Segmentation130Criteria for Model Selection131Summary132Exam Essentials133Review Questions136Chapter 5Model Training and Evaluation141Training143Local Training144Remote Training145Distributed Training146Monitoring Training Jobs147Debugging Training Jobs148Hyperparameter Tuning149Model Parameter and Hyperparameter151Exploring the Hyperparameter Space with Amazon SageMaker AI Automatic Model Tuning152Evaluation Metrics154Classification Problem Metrics154Regression Problem Metrics160Hyperparameter Tuning Techniques164Manual Search164Grid Search165Random Search165Bayesian Search165Multi-algorithm Optimization166Managing Bias and Variance Trade-Off166Addressing Overfitting and Underfitting168Underfitting168Overfitting170Regularization170Advanced Techniques173Model Performance Evaluation173Performance Evaluation Methods173K-Fold Cross-Validation174Random Train-Test Split175Holdout Set176Bootstrap176Evaluating Foundation Models177Automatic Evaluations177Human Evaluations177LLM-as-a-Judge177Programmatic Evaluations177Knowledge Base Evaluations177Deep Dive Model Tuning Example177Summary185Exam Essentials187Review Questions190Chapter 6Model Deployment and Orchestration193AWS Model Deployment Services194Deploying AI Services195Amazon Rekognition196Amazon Textract197Amazon Polly197Amazon Transcribe198Amazon Comprehend198Amazon Lex199Amazon Personalize199Amazon Bedrock200Deploying Your Model201Infrastructure Selection Considerations202Managed Model Deployments203Unmanaged Model Deployments211Optimizing ML Models for Edge Devices216Advanced Model Deployment Techniques218Autoscaling Endpoints218Deployment and Testing Strategies221Blue/Green Deployment221Orchestrating ML Workflows227Introducing Amazon SageMaker Pipelines228Code Repository and Version Control228Introducing Amazon SageMaker Model Registry229CI/CD230MLOps Orchestration230AWS Step Functions231Amazon Managed Workflows for Apache Airflow232Choosing an Orchestration Tool232Automating Model Building and Deployment233Define the Workflow Steps234Create and Configure Pipeline Steps234Define the Pipeline237Set Up Triggers and Schedules237Execute the Pipeline238Key Considerations238Deep-Dive Model Deployment Example238Summary247Exam Essentials248Review Questions250Chapter 7Model Monitoring and Cost Optimization253Monitoring Model Inference255Drifts in Models256Techniques to Monitor Data Quality and Model Performance257Monitoring Workflow259Design Principles for Monitoring261Operational Excellence Pillar261Security Pillar262Reliability Pillar263Performance Efficiency Pillar264Cost Optimization Pillar266Sustainability Pillar269Monitoring Infrastructure and Cost270Monitoring and Observability Services271Amazon CloudWatch Logs Insights272Amazon EventBridge273AWS CloudTrail274AWS X-Ray274Amazon GuardDuty275Amazon Inspector276AWS Security Hub277Cost Tracking and Optimization Services278AWS Cost Explorer278AWS Cost and Usage Reports279AWS Trusted Advisor280AWS Budgets280Pricing Models281Summary283Exam Essentials284Review Questions286Chapter 8Model Security289Security Design Principles290Implement a Strong Identity Foundation290Apply Security at all Layers291Enable Traceability292Protect Your Data (At-Rest, In-Use, and In-Transit)293Automate Security Processes294Prepare for Security Events295Securing AWS Services295Securing Identities with IAM296Identities296Access Policies302Securing Infrastructure and Data305Network Isolation with VPC305Private Connectivity306Data Protection306Monitoring and Auditing307Ensuring Compliance307Summary308Exam Essentials309Review Questions311
Preventing Bluetooth and Wireless Attacks in IoMT Healthcare Systems
A TIMELY TECHNICAL GUIDE TO SECURING NETWORK-CONNECTED MEDICAL DEVICESIn Preventing Bluetooth and Wireless Attacks in IoMT Healthcare Systems, Principal Security Architect for Connection, John Chirillo, delivers a robust and up-to-date discussion of securing network-connected medical devices. The author walks you through available attack vectors, detection and prevention strategies, probable future trends, emerging threats, and legal, regulatory, and ethical considerations that will frequently arise for practitioners working in the area. Following an introduction to the field of Internet of Medical Things devices and their recent evolution, the book provides a detailed and technical series of discussions—including common real-world scenarios, examples, and case studies—on how to prevent both common and unusual attacks against these devices. INSIDE THE BOOK:* Techniques for using recently created tools, including new encryption methods and artificial intelligence, to safeguard healthcare technology* Explorations of how the rise of quantum computing, 5G, and other new or emerging technology might impact medical device security* Examinations of sophisticated techniques used by bad actors to exploit vulnerabilities on Bluetooth and other wireless networksPerfect for cybersecurity professionals, IT specialists in healthcare environments, and IT, cybersecurity, or medical researchers with an interest in protecting sensitive personal data and critical medical infrastructure, Preventing Bluetooth and Wireless Attacks in IoMT Healthcare Systems is a timely and comprehensive guide to securing medical devices. JOHN CHIRILLO is an accomplished, published programmer and author with decades of hands-on experience. He’s a leading expert on medical device security who speaks regularly on regulatory compliance, risk assessment and mitigation, and incident management. Preface xxviiForeword xxixPART I FOUNDATION 1CHAPTER 1 INTRODUCTION TO IOMT IN HEALTHCARE 3What Is IoMT in Healthcare? 4Impact of IoMT on Healthcare 5How IoMT Works in Healthcare and Its Applications 16Challenges and Considerations in IoMT Adoption 17Best Practices for IoMT Security 18Future Trends in IoMT 20Key Takeaways of IoMT in Healthcare 22CHAPTER 2 THE EVOLVING LANDSCAPE OF WIRELESS TECHNOLOGIES IN MEDICAL DEVICES 23Overview of Wireless Technologies in Medical Devices 24Benefits of Wireless Technologies in Medical Devices 29Introduction to Risks in the Applications ofWireless Integration Challenges and Considerations 38Emerging Wireless Trends and Future Directions 40Regulatory Landscape for Wireless Medical Devices 41Best Practices for Wireless Technology Implementation 43Key Takeaways of Wireless Technologies in Healthcare 44CHAPTER 3 INTRODUCTION TO BLUETOOTH AND WI-FI IN HEALTHCARE 46Bluetooth Communication in Healthcare 47Wi-Fi Communication in Healthcare 52Overview of Bluetooth and Wi-Fi Security Risks 58Key Takeaways of Bluetooth and Wi-Fi 64PART II ATTACK VECTORS 65CHAPTER 4 BLUETOOTH VULNERABILITIES, TOOLS, AND MITIGATION PLANNING 67Introduction to Bluetooth Security 68Common Bluetooth Vulnerabilities 71Bluetooth Hacking Tools 82Mitigating Bluetooth Vulnerabilities 101Key Takeaways of Bluetooth Vulnerabilities and Exploits 103CHAPTER 5 WI-FI AND OTHER WIRELESS PROTOCOL VULNERABILITIES 104INTRODUCTION TO WI-FI SECURITY 105Building a Resilient Network Architecture with Segmentation 107Strong Authentication and Access Control 108Wi-Fi 6/6E Security Solutions 110Common Wi-Fi Vulnerabilities with Examples and Case Studies 111Wi-Fi Hacking Tools 120Bettercap 122coWPAtty 125Fern Wi-Fi Cracker 128Hashcat 131Wifite 134Kismet 138Reaver 141Storm 145WiFi Pineapple 146WiFi-Pumpkin 149Wifiphisher 151Wireshark 153Modern Wireless Operational Guide for Healthcare Compliance 156Key Takeaways of Wi-Fi Vulnerabilities and Exploits 159CHAPTER 6 MAN-IN-THE-MIDDLE ATTACKS ON MEDICAL DEVICES 161Understanding Medical Device Man-in-the-Middle Attacks 162Exploits and Other Potential Impacts of MITM Attacks on Medical Devices 167Challenges in Securing Medical Devices 168Mitigation Strategies for Healthcare Organizations 169Implement Robust Device Authentication 171Deploy Network Segmentation and Isolation 174Ensure Regular Updates and Patching 176Deploy Advanced Monitoring and Intrusion Detection 179Conduct Training and Awareness Programs 182Collaborate with Vendors to Enhance Device Security 186Key Benefits of a Comprehensive Mitigation Strategy 190Key Takeaways of Man-in-the-Middle Attacks on Medical Devices 194CHAPTER 7 REPLAY AND SPOOFING ATTACKS IN IOMT 196Understanding Replay Attacks in IoMT 197How Replay Attacks Work in IoMT Systems 197Implications of Replay Attacks in Healthcare 198Use Case of a Replay Attack on an Infusion Pump 199Other Examples of Replay Attacks in IoMT 200Strategies for Mitigation of Replay Attacks 200What Is a Spoofing Attack in IoMT? 202Mitigation Strategies for Spoofing Attacks in IoMT 205Key Takeaways of Replay and Spoofing Attacks in IoMT 206CHAPTER 8 DENIAL OF SERVICE IN WIRELESS MEDICAL NETWORKS 208Understanding DoS Attacks 208Common Types of DoS Attacks, Targets, and Device Impact 209Impact of DoS Attacks on Healthcare Operations 213Common Vulnerabilities That Enable DoS Attacks in Wireless Medical Networks 214Mitigation Strategies for Denial of Service Attacks 217Key Takeaways from DoS in Wireless Medical Networks 224PART III CASE STUDIES AND REAL-WORLD SCENARIOS 227CHAPTER 9 PACEMAKER HACKING 229Understanding Pacemaker Technology and Its Risks and Limitations 230How Does the Heart Normally Function? 230What Is a Pacemaker? 230Understanding Vulnerabilities in Pacemakers in Today’s Connected World 233Real-World Case Studies and Impact 235Strategies and Technologies to Mitigate Pacemaker Cybersecurity Risks 242More on Consequences of Pacemaker Hacking 244Key Takeaways from Pacemaker Hacking 245CHAPTER 10 INSULIN PUMP VULNERABILITIES AND EXPLOITS 247Understanding Insulin Pumps and Their Vulnerabilities 249Implications and Real-World Scenarios of Insulin Pump Exploits 258Education and Training for Patients and Healthcare Providers 261Key Takeaways from Insulin Pump Vulnerabilities and Exploits 261CHAPTER 11 ATTACK VECTOR TRENDS AND HOSPITAL NETWORK BREACHES WITH IOMT DEVICES 263Understanding the IoMT Risk Landscape 264Attack Vector Trends and Landscape 268Malware Analysis for Digital Forensics Investigations 272Key Takeaways from Hospital Network Breaches with IoMT Devices 280CHAPTER 12 WEARABLE MEDICAL DEVICE SECURITY CHALLENGES 282The Rise of Wearable Medical Devices 282Security Challenges of Wearable Medical Devices 283New Trends and Threats in Wearable Device Security 289Proactive Measures for Mitigating Wearable Device Threats 290How AI Can Help 291Key Takeaways from Security Challenges of Wearable Medical Devices 294PART IV DETECTION AND PREVENTION 295CHAPTER 13 INTRUSION DETECTION AND PREVENTION FOR IOMT NETWORKS 297Introduction to Intrusion Detection and PreventionSystems for IoMT 297Understanding IoMT Ecosystems 299What Is Intrusion Detection and Prevention in IoMT Environments? 299Case Study: Implementing IDPS in a Healthcare Environment 302IDPS Solutions 304Best Practices for IoMT IDPS Deployment 331Modern Innovations in IoMT IDS 333Emerging Trends in IoMT IDS 336Key Takeaways from IDPS for IoMT Networks 336CHAPTER 14 MACHINE LEARNING APPROACHES TO WIRELESS ATTACK DETECTION 338Introduction to Machine Learning for WirelessMachine Learning Feature Engineering for Wireless Attack Detection 342Types of Machine Learning Techniques 344Machine Learning Applications in Healthcare and IoMT 350Challenges in Applying ML to Wireless Security in IoMT 352Future Directions of Machine Learning for Attack Detection in Healthcare 356Machine Learning Case Studies in Healthcare 362Key Takeaways from Machine Learning Approaches to Wireless Attack Detection 364CHAPTER 15 SECURE COMMUNICATION PROTOCOLS FOR MEDICAL DEVICES 366Importance of Secure Communication in Medical Devices 366Key Security Requirements for Medical Device Communication 368Secure Communication Protocols for Medical Devices 371Encryption Algorithms and Key Management 373Secure Device Pairing and Onboarding 377Out-of-Band Authentication Methods 377Regulatory Compliance and Standards 379Challenges in Implementing Secure Communication Protocols 381Best Practices for Secure Medical Device Communication 383Emerging Technologies and Future Trends 384Secure Communication Strategies 386Ethical Considerations 387Key Takeaways from Secure Communication Protocols for Medical Devices 389CHAPTER 16 BEST PRACTICES FOR IOMT DEVICE SECURITY 391Endpoint Security Best Practices 392Network Security Best Practices 393Perimeter Security Best Practices 394Cloud Security Best Practices 395Network Segmentation 396Strong Authentication and Access Controls 397Regular Updates and Patching 401AI-Powered Monitoring and Analytics 403Zero Trust Security Model 405Encryption and Data Protection 407Asset Inventory and Management 409Vendor Management and Third-Party Risk Assessment 411Compliance with Regulatory Standards 414Continuous Monitoring and Incident Response 417Employee Training and Awareness 420Secure Device Onboarding and Decommissioning 422Physical Security Measures 425Backup and Recovery 428Secure Communication Protocols 430Data Minimization and Retention Policies 433Cybersecurity Insurance 435Regular Security Audits 436Key Takeaways of Best Practices for IoMT Device Security 438PART V FUTURE TRENDS AND EMERGING THREATS 441CHAPTER 17 5G AND BEYOND AND IMPLICATIONS FOR IOMT SECURITY 443Introduction to 5G and Beyond Technologies 443Impact of 5G on IoMT 445Security Implications for IoMT 447Regulatory Considerations 450Future Research Directions 455Industry Collaboration and Knowledge Sharing 456Key Takeaways of 5G and Beyond and Implications for IoMT Security 458CHAPTER 18 QUANTUM COMPUTING IN MEDICAL DEVICE SECURITY 459Fundamentals of Quantum Computing 459Potential Applications in Medical Device Security 461Challenges Posed by Quantum Computing 462Quantum Attack on IoMT Firmware 463Quantum-Resistant Cryptography for Medical Devices 466Quantum Sensing and Metrology in Medical Devices 467Quantum-Safe Network Protocols for Medical Devices 468Regulatory and Standardization Efforts 469Ethical and Privacy Considerations 470Future Research Directions 472Preparing the Healthcare Industry for the Quantum Era 473Key Takeaways from Quantum Computing in Medical Device Security 475CHAPTER 19 AI-DRIVEN ATTACKS AND DEFENSES IN HEALTHCARE 476Types of AI-Driven Attacks in Healthcare 476Impact of AI-Driven Attacks on Healthcare 478AI-Driven Defenses in Healthcare 480Challenges in Implementing AI-Driven Defenses 484Future Trends in AI-Driven Healthcare Cybersecurity 486Best Practices for Healthcare Organizations 488Key Takeaways from AI-Driven Attacks and Defenses in Healthcare 489PART VI LEGAL AND ETHICAL CONSIDERATIONS 491CHAPTER 20 REGULATORY FRAMEWORKS FOR IOMT SECURITY 493Key Regulatory Bodies and Frameworks 493Legal Considerations 495Ethical Considerations 498Challenges in Regulatory Framework Development 500Best Practices for Regulatory Compliance 502Future Trends in IoMT Security Regulation 504Examples of Benefits from Regulation Implementation 505Recommendations for Stakeholders 507Key Takeaways from Regulatory Frameworks for IoMT Security 509CHAPTER 21 GUIDELINES FOR ETHICAL HACKING IN HEALTHCARE 510Importance of Ethical Hacking in Healthcare 510Scope of Ethical Hacking in Healthcare 512Legal and Regulatory Considerations 513Ethical Boundaries and Guidelines 515Best Practices for Ethical Hacking in Healthcare 516Challenges in Healthcare Ethical Hacking 519Emerging Trends and Future Considerations 520Training and Certification for Healthcare Ethical Hackers 521Case Studies 523Key Takeaways from Ethical Hacking in Healthcare 524Conclusion 525Index 527
Navigating Misinformation
Informed navigation of misinformation on social media constitutes a major challenge. The field of Human-Computer Interaction (HCI) suggests digital misinformation interventions as user-centered countermeasures. This book clusters (1) existing misinformation interventions within a taxonomy encompassing designs, interaction types, and timings. The book demonstrates that current research mostly addresses higher-educated participants, and targets Twitter/X and Facebook. It highlights trends toward comprehensible interventions in contrast to top-down approaches. The findings informed (2) the design, implementation, and evaluation of simulated apps for TikTok, voice messages, and Twitter/X as indicator-based interventions. Therefore, (3) the book identified misinformation indicators for various modalities that were perceived as comprehensible.The book empirically demonstrates that (4) indicator-based interventions are positively received due to their transparency. However, they also come with challenges, such as users' blind trust and lack of realistic assessments of biases. This research outlines chances and implications for future research.
Python für Kinder
Python für Kinder – Spielend leicht programmieren lernen Dein Kind liebt Computer und hat Spaß daran, Dinge auszuprobieren? Es fragt sich, wie Spiele oder Apps eigentlich funktionieren, und möchte selbst kreativ werden? Dann ist das Buch "Python für Kinder" genau das Richtige! Florian Dalwigk, ein erfolgreicher YouTuber mit über 100.000 Abonnenten, bringt mit diesem Buch die Welt der Programmierung auf spielerische Weise direkt zu euch nach Hause. Dieses Buch bietet einen leichten Einstieg in die Welt der Python-Programmierung, selbst für absolute Anfänger. Florian erklärt Schritt für Schritt, wie dein Kind spannende Projekte umsetzen kann – von Spielen bis hin zu kreativen Übungen, die nicht nur Spaß machen, sondern auch die Grundlagen der Informatik vermitteln. Das erwartet dein Kind in diesem Buch: - Einfacher Einstieg: Dank klarer Erklärungen und leicht verständlicher Beispiele kann dein Kind ohne Vorkenntnisse loslegen. - Python kinderleicht installieren: Ob auf Mac, Windows oder online – dein Kind lernt, wie Python eingerichtet wird, um sofort mit den Projekten zu starten. - Bewegte Schildkröten: Mit Python können Schildkröten auf dem Bildschirm laufen und zeichnen – ein kreativer Weg, die Grundlagen der Programmierung zu verstehen. - Spiele programmieren: Mit spaßigen Projekten wie eigenen Spielen lernt dein Kind spielerisch wichtige Konzepte wie Schleifen und Bedingungen. - Grundlagen der Informatik: Was sind Algorithmen, wie funktioniert ein Computer, und warum sind Programme so wichtig? Dein Kind wird es bald wissen! Perfekt für Eltern und Kinder: - Gemeinsames Lernen: Auch Eltern ohne Vorkenntnisse können zusammen mit ihren Kindern die Projekte ausprobieren und so wertvolle Zeit gemeinsam verbringen. - Einfach loslegen: Mit leicht verständlichen Anleitungen und spannenden Aufgaben können Kinder sofort starten – ohne lange Vorbereitung. Florian Dalwigk – Der perfekte Begleiter ins Programmieren Mit seinem Bestseller "Python für Einsteiger" hat Florian Dalwigk bereits bewiesen, dass er komplexe Themen anschaulich und für alle verständlich machen kann. Jetzt bringt er dieses Wissen in "Python für Kinder" kindgerecht auf den Punkt. Werde Teil der digitalen Zukunft! Warum warten? Mit "Python für Kinder" kann dein Kind schon heute anfangen, die Welt der Technik spielerisch zu entdecken. Bestell jetzt das Buch und erlebe, wie viel Spaß Programmieren machen kann!
Konstruierte Wahrheiten
In einer Welt, in der immer mehr Fake News verbreitet werden, wird es zunehmend schwieriger, Wahrheit und Lüge, Wissen und Meinung auseinanderzuhalten. Desinformationskampagnen werden nicht nur als ein politisches Problem wahrgenommen, vielmehr geht es in der Fake-News-Debatte auch um fundamentale philosophische Fragen: Was ist Wahrheit? Wie können wir sie erkennen? Gibt es so etwas wie objektive Fakten oder ist alles sozial konstruiert? Dieses Buch erklärt, wie Echokammern und alternative Weltbilder entstehen, es macht das postfaktische Denken für die gegenwärtige Wahrheitskrise verantwortlich und zeigt, wie wir einem drohenden Wahrheitsrelativismus entgehen können.THOMAS ZOGLAUER (Dr. phil. habil.) lehrt Philosophie an der Brandenburgischen Technischen Universität Cottbus-Senftenberg und an der Graduierten-Akademie der Universität Stuttgart und ist Autor zahlreicher Bücher zur Technikphilosophie und angewandten Ethik.Filterblasen und Echokammern.- Verschwörungstheorien.- Fake News.- Epistemologie des Postfaktischen.- Wahrheitstheorien.- Information und Wissen.
Edge Computing
UNDERSTAND THE COMPUTING TECHNOLOGY THAT WILL POWER A CONNECTED FUTUREThe explosive growth of the Internet of Things (IoT) in recent years has revolutionized virtually every area of technology. It has also driven a drastically increased demand for computing power, as traditional cloud computing proved insufficient in terms of bandwidth, latency, and privacy. Edge computing, in which data is processed at the edge of the network, closer to where it’s generated, has emerged as an alternative which meets the new data needs of an increasingly connected world. Edge Computing offers a thorough but accessible overview of this cutting-edge technology. Beginning with the fundamentals of edge computing, including its history, key characteristics, and use cases, it describes the architecture and infrastructure of edge computing and the hardware that enables it. The book also explores edge intelligence, where artificial intelligence is integrated into edge computing to enable smaller, faster, and more autonomous decision-making. The result is an essential tool for any researcher looking to understand this increasingly ubiquitous method for processing data. Edge Computing readers will also find:* Real-world applications and case studies drawn from industries including healthcare and urban development* Detailed discussion of topics including latency, security, privacy, and scalability* A concluding summary of key findings and a look forward at an evolving computing landscapeEdge Computing is ideal for students, professionals, and enthusiasts looking to understand one of technology’s most exciting new paradigms. LANYU XU, PHD, is Assistant Professor in the Department of Computer Science and Engineering, Oakland University, Michigan, where she leads the Edge Intelligence System Laboratory. Her research intersects edge computing and deep learning, emphasizing the development of efficient edge intelligence systems. Her work explores optimization frameworks, intelligent systems, and AI applications to address challenges in efficiency and real-world applicability of edge systems across various domains. WEISONG SHI, PHD, is an Alumni Distinguished Professor and Chair of the Department of Computer and Information Sciences at the University of Delaware, where he leads the Connected and Autonomous Research Laboratory. He is an internationally renowned expert in edge computing, autonomous driving, and connected health. His pioneer paper, “Edge Computing: Vision and Challenges,” has been cited more than 8000 times in eight years. He is an IEEE Fellow.
Protecting and Mitigating Against Cyber Threats
THE BOOK PROVIDES INVALUABLE INSIGHTS INTO THE TRANSFORMATIVE ROLE OF AI AND ML IN SECURITY, OFFERING ESSENTIAL STRATEGIES AND REAL-WORLD APPLICATIONS TO EFFECTIVELY NAVIGATE THE COMPLEX LANDSCAPE OF TODAY’S CYBER THREATS.Protecting and Mitigating Against Cyber Threats delves into the dynamic junction of artificial intelligence (AI) and machine learning (ML) within the domain of security solicitations. Through an exploration of the revolutionary possibilities of AI and ML technologies, this book seeks to disentangle the intricacies of today’s security concerns. There is a fundamental shift in the security soliciting landscape, driven by the extraordinary expansion of data and the constant evolution of cyber threat complexity. This shift calls for a novel strategy, and AI and ML show great promise for strengthening digital defenses. This volume offers a thorough examination, breaking down the concepts and real-world uses of this cutting-edge technology by integrating knowledge from cybersecurity, computer science, and related topics. It bridges the gap between theory and application by looking at real-world case studies and providing useful examples. Protecting and Mitigating Against Cyber Threats provides a roadmap for navigating the changing threat landscape by explaining the current state of AI and ML in security solicitations and projecting forthcoming developments, bringing readers through the unexplored realms of AI and ML applications in protecting digital ecosystems, as the need for efficient security solutions grows. It is a pertinent addition to the multi-disciplinary discussion influencing cybersecurity and digital resilience in the future. Readers will find in this book:* Provides comprehensive coverage on various aspects of security solicitations, ranging from theoretical foundations to practical applications;* Includes real-world case studies and examples to illustrate how AI and machine learning technologies are currently utilized in security solicitations;* Explores and discusses emerging trends at the intersection of AI, machine learning, and security solicitations, including topics like threat detection, fraud prevention, risk analysis, and more;* Highlights the growing importance of AI and machine learning in security contexts and discusses the demand for knowledge in this area.AUDIENCECybersecurity professionals, researchers, academics, industry professionals, technology enthusiasts, policymakers, and strategists interested in the dynamic intersection of artificial intelligence (AI), machine learning (ML), and cybersecurity. SACHI NANDAN MOHANTY, PHD is an associate professor at the School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India, He has published 60 articles in journals of international repute, edited 24 books, and serves as an editor for several international journals. His research interests include data mining, big data analysis, cognitive science, fuzzy decision making, brain-computer interface, cognition, and computational intelligence. SUNEETA SATPATHY, PHD is an associate professor in the Center for Artificial Intelligence and Machine Learning at Siksha O. Anusandhan University, India. She has published several papers in international journals and conferences of repute and edited numerous books. Her research interests include computer forensics, cyber security, data fusion, data mining, big data analysis, and decision mining. MING YANG, PHD is a professor in the College of Computing and Software Engineering at Kennesaw State University, Georgia, USA and serves as a consultant for many companies. He has published over 70 peer-reviewed conference and journal papers and book chapters in addition to serving as an editor for several journals. His research interests include image processing, multimedia communication, computer vision, and machine learning. D. KHASIM VALI, PHD is an assistant professor in the School of Computer Science and Engineering, the Vellore Institute of Technology, Andhra Pradesh University, India, with over 18 years of teaching experience. He has 21 international publications to his credit and is a life member of ISTE and IETE. His research interests include artificial intelligence, machine learning, and deep learning.