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Inhaltsverzeichnis
.- Introduction to AI in Cybersecurity.
.- Overview of cybersecurity fundamentals.
.- Introduction to Artificial Intelligence and Machine Learning.
.- Intersection of AI and cybersecurity.
.- Importance and challenges in enterprise networks.
.- Foundations of Cybersecurity.
.- Threat models and attack vectors.
.- Traditional cybersecurity methodologies.
.- Encryption, authentication, and authorization basics.
.- Limitations of traditional approaches in modern enterprise networks.
.- Machine Learning Essentials for Cybersecurity.
.- Supervised, unsupervised, and reinforcement learning basics.
.- Popular ML algorithms: Decision Trees, Neural Networks, and Deep Learning.
.- Feature engineering and model evaluation methods.
.- AI-Enhanced Intrusion Detection Systems (IDS).
.- Traditional intrusion detection systems (signature-based, anomaly-based).
.- AI-driven anomaly detection methods.
.- Building and training effective AI-driven IDS models.
.- Case study: Implementing AI-enhanced IDS in a commercial network.
.- AI in Malware and Advanced Persistent Threat (APT) Detection.
.- Overview of malware and APT threats.
.- AI-driven malware classification and detection methods.
.- Behavioral analytics and pattern recognition.
.- Real-world application: AI-based malware defense systems.
.- Automated Incident Response and Security Automation.
.- Fundamentals of automated incident response.
.- AI-driven security orchestration, automation, and response (SOAR) systems.
.- Frameworks and best practices for automation.
.- Case study: Enterprise incident response optimization using AI.
.- AI for Predictive Threat Intelligence and Risk Analysis.
.- Threat intelligence lifecycle and frameworks.
.- Predictive analytics and threat forecasting with AI.
.- Risk modeling and management using AI.
.- Practical case studies of predictive risk analytics.
.- Securing Cloud and Hybrid Networks with AI.
.- Cloud security challenges and approaches.
.- AI-driven cloud security mechanisms (e.g., anomaly detection, cloud workload protection).
.- Hybrid and multi-cloud network security practices.
.- Industry case studies demonstrating AI-driven cloud security.
.- Protecting IoT and Edge Devices Using AI.
.- Cybersecurity concerns in IoT ecosystems.
.- AI-driven security for IoT and edge computing networks.
.- Implementing lightweight AI models at the edge.
.- Real-world deployment scenarios and best practices.
.- Ethical and Regulatory Aspects of AI in Cybersecurity.
.- Ethical considerations of AI-based cybersecurity systems.
.- Bias, fairness, and transparency in AI models.
.- Privacy and data protection regulations (GDPR, CCPA).
.- Compliance frameworks and auditing AI systems.
.- Evaluating and Validating AI-Driven Cybersecurity Solutions.
.- Metrics for evaluating cybersecurity effectiveness.
.- Robustness and resilience of AI-driven security systems.
.- Penetration testing and red teaming AI defenses.
.- Case study: Enterprise validation protocols.
.- Future Directions and Emerging Trends.
.- Quantum computing and implications for AI cybersecurity.
.- AI-driven cybersecurity innovations (Generative AI, adversarial learning).
.- Future trends in enterprise cybersecurity.
.- Preparing enterprise networks for future threats.
.- Practical Projects and Case Studies.
.- Comprehensive hands-on projects integrating AI in cybersecurity.
.- Detailed real-world case studies from diverse industries.
.- Step-by-step guides and references for practical implementation.
Über den Autor / die Autorin
Anurag Reddy Ekkati Anurag Ekkati is a Senior Principal Software Engineer at Palo Alto Networks, focusing on cloud infrastructure security, digital certificate management, observability, and AI-driven cybersecurity automation. Anurag leads the design and development of secure, scalable platforms that enhance system reliability and operational visibility. One of Anurag's key contributions has been the end-to-end development of a highly reliable Digital Certificate Management Platform. This platform replaced multiple fragmented commercial solutions, resulting in zero certificate-related outages and millions in annual cost savings. Anurag also led the design and development of an internal observability solution that significantly improved metrics, tracing, and logging coverage while reducing reliance on costly external tools.
Dr.Bishwajeet Pandey
is a Professor at GL Bajaj Institute of Technology and Management, Greater Noida, India. He has been a Senior Member of IEEE since 2019. He holds an MTech in Computer Science from the IIIT, Gwalior, India, and a PhD in Computer Science from the Gran Sasso Science Institute, Italy. He has taught at esteemed institutions such as Chitkara University, Chandigarh; Jain University, Bangalore; Astana IT University, Kazakhstan; Eurasian National University, Kazakhstan (QS World Rank 321); Walsh College, USA; and UCSI University, Malaysia (QS World Rank 265). Dr. Pandey is a prolific researcher with over 30 published books and more than 220 research papers indexed in Scopus. He has garnered over 4500+ citations. His leadership roles include serving as the Research Head of the School of CSE at Jain University, Bangalore, from 2021 to 2023 and as the head of the International Global Academic Partnership Committee at Birla Institute of Applied Science, Bhimtal, from 2020 to 2021. In 2023, Dr. Pandey was honoured with the prestigious Professor of the Year Award at Lord's Cricket Ground by the London Organisation of Skills Development. Beyond his outstanding research output, his greatest strength lies in his global academic network. He has visited 49 countries, participated in 105 international conferences, and co-authored papers with 218 professors from 93 universities across 42 nations.
Zusammenfassung
AI-Powered Cybersecurity Essentials: Protecting Enterprise Networks explores how modern AI and machine learning safeguard complex, hybrid enterprise environments. The book progresses from cybersecurity and ML foundations to applied defenses: AI-enhanced intrusion detection, malware and APT discovery, automated incident response, and predictive threat intelligence with risk analysis. It then covers securing cloud and hybrid networks, protecting IoT and edge devices, rigorous evaluation and validation of AI-driven solutions, and the ethical and regulatory guardrails that govern responsible deployment, closing with actionable future trends.
Written for security architects, SOC analysts, network engineers, and researchers, the book blends principles with practical patterns, reference workflows, and implementation checklists. Readers will learn to design and tune AI-assisted controls, integrate them with existing stacks, operationalize detection and response, measure effectiveness, and navigate governance and compliance—so they can confidently deploy resilient, human-centered, AI-enabled defenses across the enterprise.
