AI/ML for Security
Applying machine learning and AI frameworks to cybersecurity challenges, including threat detection, behavioral analysis, and network defense.
Focus Areas
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Intelligent Threat Detection
Developing machine learning models for network intrusion detection, DDoS attack classification, and anomalous behavior identification.
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Behavioral Biometric Analysis
Applying ML to keystroke dynamics, gait patterns, and multi-modal behavioral signals for continuous authentication and threat level assessment.
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ML for Manufacturing Security
Using image classification and machine learning to detect malicious defects in manufacturing processes such as 3D printing.
Related Publications
IEEE AIIoT 2026
Parallel Stream Transformer Based Architecture for Multimodal User Verification
IEEE AIIoT 2025
Multi-Modal Adversarial Activity Detection Using Keyboard and Mouse Dynamics
IEEE ICCCNT 2025
Enhanced Multi-Class DDoS Attack Identification Using a Meta-Learning Ensemble
ACM Digital Threats 2021
Game Theory based Cyber-Insurance to Cover Potential Loss from Mobile Malware
IEEE AISP 2020
Classification of Threat Level in Typing Activity Through Keystroke Dynamics
IEEE AISP 2020
Authentication by Mapping Keystrokes to Music: The Melody of Typing
IEEE AISP 2020
Formalizing PQRST Complex in Accelerometer-based Gait Cycle for Authentication
Springer 2020
Insights from BB-MAS: A Large Dataset for Typing, Gait and Swipes
ASME IMECE 2016
Detecting Malicious Defects in 3D Printing Process Using ML and Image Classification