Full Description
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.
Contents
.- 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.
