AI and IoT: Accelerating the Power of DDoS Attacks

About This Course

“AI and IoT: Accelerating the Power of DDoS Attacks” is an advanced 3-week course that dives deep into the intersection of Artificial Intelligence (AI), the rapid growth of the Internet of Things (IoT), and modern cyber warfare tactics. While IoT enables real-time connectivity and data-driven automation, it also opens up new vulnerabilities that cyber attackers are exploiting, with AI amplifying their power.

In this course, participants will explore Distributed Denial-of-Service (DDoS) attack vectors, AI-based botnet orchestration, anomaly detection, network traffic modeling, and real-time mitigation frameworks. Using tools like Wireshark, Scapy, TensorFlow, Snort, and Keras, attendees will gain hands-on experience in both attack simulation and intelligent defense implementation.

Aim

The goal of this course is to explore how AI and IoT technologies are being used to scale and automate DDoS attacks. Participants will be trained in identifying, simulating, and mitigating these cyber threats using advanced defense strategies, preparing them to defend next-generation smart ecosystems from intelligent attacks.

Course Objectives

• Unveil the dual-use potential of AI in cybersecurity—both attacker and defender roles

• Train participants to implement robust, AI-based defense architectures

• Encourage ethical use of penetration testing tools for research and training

• Prepare professionals to defend next-gen smart ecosystems from intelligent cyber threats

Course Structure

📅 Module 1: The Evolving Cyber Threat Surface – DDoS in the Age of AI & IoT

Theme: Understanding the Modern Cyber Threat Landscape

• 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

  • Automating target identification, dynamic payload generation, and swarm intelligence

• Global Case Studies & Cyber Intelligence

  • Analyzing Mirai Botnet 2.0, Mozi, and Dark Nexus

Hands-On Lab:

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

• Real-time packet analysis with Zeek and Wireshark

📅 Module 2: Offensive Artificial Intelligence – Automating, Evolving, Attacking

Theme: Leveraging AI for Dynamic, Real-Time Attacks

• 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 attack impact

• Bypassing AI Defenses: Adversarial Evasion & Model Poisoning

  • Techniques for manipulating and defeating ML-based Intrusion Detection Systems (IDS)

• Emerging Threat Vectors

  • AI-generated network traffic mimicking normal behavior

Hands-On Lab:

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

• Generate adversarial inputs using FGSM or textGAN

📅 Module 3: Designing AI-Resilient IoT Architectures – From Detection to Defense

Theme: Building Secure IoT Architectures with AI

• 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)

  • Integrating AI into 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

Who Should Enrol?

• Cybersecurity professionals and network engineers

• AI/ML practitioners working on threat detection

• IoT product developers and embedded systems engineers

• Researchers in cyber warfare, data forensics, and smart systems

• Students with basic knowledge of networking and Python