AI and IoT: Accelerating the Power of DDoS Attacks

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• Understanding the Modern IoT Threat Landscape

  • Device sprawl, vulnerabilities, and real-world incidents

• Anatomy of DDoS Attacks in IoT Ecosystems

  • Volumetric, protocol exploitation, and application layer attacks

• AI as a Force Multiplier for DDoS Campaigns

  • Target automation, dynamic payload generation, and swarm intelligence

• Global Case Studies & Cyber Intelligence

  • Mirai Botnet 2.0, Mozi, Dark Nexus

Interactive Demonstration

• Simulated DDoS traffic using open-source toolkits (e.g., LOIC, Hping3)

• Real-time packet analysis with Zeek and Wireshark

• Adversarial Machine Learning in DDoS Attacks

  • ML-based target profiling and attack path optimization

• Deep Reinforcement Learning for Dynamic Attack Strategies

  • AI bots adapting in real-time to evade detection and maximize impact

• Bypassing AI Defenses: Adversarial Evasion & Model Poisoning

  • Techniques for manipulating and defeating ML-based IDS systems

• Emerging Threat Vectors

  • AI-generated network traffic mimicking normal behavior

Practical Exercise

• Build and test a simple anomaly detection model using Python (e.g., Random Forest or SVM)

• Generate adversarial inputs using FGSM or textGAN

• AI-Augmented Intrusion Detection & Prevention Systems (IDPS)

  • Supervised and unsupervised models for real-time traffic scoring

• Federated Learning for Edge-Based Defense

  • Privacy-aware distributed models for smart devices

• Security Orchestration, Automation, and Response (SOAR)

  • AI integration in automated mitigation pipelines

• Governance, Risk, and Compliance (GRC)

  • Alignment with ISO/IEC 27001, NIST RMF, OWASP IoT Top 10, and GDPR

Capstone Design Project

• Group design of a secure IoT network architecture

• Threat modeling using STRIDE and MITRE ATT&CK for IoT