ABSTRACT:

AI is everywhere. It is being applied to security as well. In our research on the security of medical and industrial IoT over the last 8 years, we have noticed several common mistakes, challenges, and issues in applying AI and securing IoT. This talk has three parts. In the first part of the talk, I discuss nine such common issues and mistakes. In the 2nd part, I discuss recent developments in AI that are already being adopted now. In the third part, I discuss technologies that are coming in recent future and ready for trial now.

This talk covers the following topics:

• Recent Advances, Issues, and Challenges in Cybersecurity
• Overview
• AI-Based Security of IoT: Our Research
• Part 1Common issues with AI Security studies
• Lessons Learnt: 9 Problems with AI Studies
• 1. No Domain Expertise
• 2. Random Datasets
• 2. Random Datasets
• Industrial Control Systems Security Using AI
• Internet of Medical Things Security Using AI
• 3. Imbalance of Security Data
• 4. Results Not Explainable
• 5. Wrong Metrics
• 6. Too Few or Too Many Features
• 7. No Sensitivity Analysis
• 8. No Real-World Validation
• 9. Omitting Assumptions and Limitations
• Jain's List of Issues Challenges with AI
• Part 2Recent Developments in AI and their Impact on Security
• Gartner's AI and Cybersecurity Hype Cycle 2025
• Seven Generations of AI
• Intent-Based Networking (IBN)
• GenAI-Driven Phishing and Attacks
• Generative AI for Phishing Defense
• Cisco AI Canvas
• Agentic AI
• Internet of AI Agents
• Part 3Security Technologies Coming Soon - Ready for Trials
• Gartner's Hype Cycle for Data Security Techs 2025
• Quantum Computing
• Post-Quantum Cryptography
• Quantum Key Distribution (QKD)
• Homomorphic Encryption and Secure Computation
• Edge AI: Hierarchical Deep Learning
• Zero-Trust Architecture
• Blockchain and Distributed Ledger Technology
• Zero-Knowledge Proof (ZKP)
• Summary
• Our Publications
• Acronyms

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