Insights from the Frontlines of AI & Cyber
Peer-reviewed research, strategic analysis, and thought leadership from Dr. Nachaat Mohamed published in IEEE, Springer, Scopus, and Taylor & Francis.
AI & Machine Learning in Cybersecurity: A Deep Dive into State-of-the-Art Techniques
Dr. Nachaat Mohamed
Knowledge and Information Systems (Springer) - Vol. 67, pp. 6969–7055
Traditional cyber defenses are failing against modern threats. This landmark 87-page open-access study maps exactly how AI and ML are reshaping intrusion detection, malware classification, behavioral analysis, and threat intelligence - and where the field must go next.
Key Takeaways
- ✦AI detects zero-day attacks traditional systems miss
- ✦Federated learning enables privacy-preserving defense
- ✦Adversarial ML is the next major frontier in cyber risk
- ✦Quantum computing will reshape cryptographic resilience
Current Trends in AI and ML for Cybersecurity: A State-of-the-Art Survey
Dr. Nachaat Mohamed
Cogent Engineering, Taylor & Francis - Published Oct 2023
A comprehensive survey mapping the most significant AI and ML developments in cybersecurity - covering current capabilities, critical gaps, and the strategic roadmap for next-generation cyber defense systems.
Key Takeaways
- ✦ML-driven anomaly detection outperforms rule-based systems
- ✦Deep learning is transforming malware analysis
- ✦Real-time threat intelligence requires AI automation
- ✦Human-AI collaboration is the future of SOC operations
SBI Model for APT Detection Using Credential Dumping Technique
Dr. Nachaat Mohamed, Bahari Belaton
IEEE Access - Vol. 9, pp. 42919–42932 (2021)
Advanced Persistent Threats silently devastate governments and enterprises for months before detection. This IEEE Access paper introduces the Strange Behavior Inspection (SBI) Model - a novel APT detection framework built on the MITRE ATT&CK matrix that identifies attackers at the first point of compromise, before credential dumping causes irreversible damage.
Key Takeaways
- ✦APTs identified at first victim machine - not after lateral movement begins
- ✦SBI monitors CPU, RAM, Windows Registry and file systems simultaneously
- ✦Built on MITRE ATT&CK - industry's most trusted threat intelligence framework
- ✦Detects credential dumping before privilege escalation occurs
AI & ML-Based Information Security in Electric Vehicles: A Review
Dr. Nachaat Mohamed, Mohit Bajaj, Saif Almazrouei, Francisco Jurado, Adel Oubelaid, Salah Kamel
IEEE GPECOM 2023 - pp. 108–113
As electric vehicles become critical national infrastructure, their cybersecurity has been dangerously overlooked. This IEEE conference paper reviews how AI and ML are being deployed to secure EV systems - covering authentication, intrusion detection, and attack prevention in connected vehicle environments.
Key Takeaways
- ✦AI now secures EV authentication against spoofing and relay attacks
- ✦ML-based intrusion detection protects CAN bus and OBD-II interfaces
- ✦Attack prevention models adapted from enterprise security to mobility sector
- ✦Critical gap identified: EV security research lags far behind deployment
Cutting-Edge Advances in AI & ML for Cybersecurity: Emerging Trends and Future Directions
Dr. Nachaat Mohamed
Cogent Business & Management (Taylor & Francis) - Vol. 12, No. 1 · Published 26 June 2025
The cyber threat landscape is evolving faster than most organizations can respond. This 2025 cutting-edge review maps the most significant emerging AI and ML developments reshaping cybersecurity defense - identifying where the field is today, where it must go next, and the critical gaps organizations cannot afford to ignore.
Key Takeaways
- ✦Traditional cyber defenses are becoming obsolete against AI-powered attacks
- ✦Next-generation threat detection demands adaptive, self-learning AI systems
- ✦Ethical and legal AI frameworks are now a board-level cybersecurity obligation
- ✦Future cyber resilience depends on human-AI collaborative defense models
- ✦Emerging attack vectors require proactive AI-driven threat anticipation - not reaction