AI/ML for Security

Applying machine learning and AI frameworks to cybersecurity challenges, including threat detection, behavioral analysis, and network defense.

Focus Areas

Intelligent Threat Detection

Developing machine learning models for network intrusion detection, DDoS attack classification, and anomalous behavior identification.
Behavioral Biometric Analysis

Applying ML to keystroke dynamics, gait patterns, and multi-modal behavioral signals for continuous authentication and threat level assessment.
ML for Manufacturing Security

Using image classification and machine learning to detect malicious defects in manufacturing processes such as 3D printing.

IEEE WIFS 2026

IllumiCue: A Selective Physical Consistency Cue for Face-Swap Video Forensics

ACM Digital Threats 2026

An AI-Based Gait Authentication Framework Leveraging Swing Phase Dynamics

Springer 2026

Adapting Transformer Foundation Models for Multi-Class DDoS Attack Classification via Parameter-Efficient Fine-Tuning

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

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