Malware Analysis & Detection
Finding malicious code that does not want to be found.
Static, dynamic, and hybrid analysis of malicious binaries, with a focus on Android and IoT malware. We build detectors that survive obfuscation and evasion, and study how detection degrades as malware families evolve.
Topics
Publications
2025
IoT - FedMalDetect: Federated Learning Based Malware Detection for IoT Edge Devices
2025 IEEE Conference on Communications and Network Security (CNS 2025)
2025
GLAMP: Generative Learning for Adversarially-Robust Malware Prediction
IEEE Transactions on Emerging Topics in Computing (TETC). [Q1]