5 papers
Can Drift-Adaptive Malware Detectors Be Made Robust? Attacks and Defenses Under White-Box and Black-Box Threats
Adrian Shuai Li, Md Ajwad Akil, Elisa Bertino
Concept drift and adversarial evasion are two major challenges for deploying machine learning-based malware detectors. While both have been studied separately, their combination, t…
LFreeDA: Label-Free Drift Adaptation for Windows Malware Detection
Adrian Shuai Li, Elisa Bertino
Machine learning (ML)-based malware detectors degrade over time as concept drift introduces new and evolving families unseen during training. Retraining is limited by the cost and…
LLMalMorph: On The Feasibility of Generating Variant Malware using Large-Language-Models
Md Ajwad Akil, Adrian Shuai Li, Imtiaz Karim +4
Large Language Models (LLMs) have transformed software development and automated code generation. Motivated by these advancements, this paper explores the feasibility of LLMs in mo…
Revisiting Concept Drift in Windows Malware Detection: Adaptation to Real Drifted Malware with Minimal Samples
Adrian Shuai Li, Arun Iyengar, Ashish Kundu +1
In applying deep learning for malware classification, it is crucial to account for the prevalence of malware evolution, which can cause trained classifiers to fail on drifted malwa…
Adversarial Domain Adaptation for Metal Cutting Sound Detection: Leveraging Abundant Lab Data for Scarce Industry Data
Mir Imtiaz Mostafiz, Eunseob Kim, Adrian Shuai Li +3
Cutting state monitoring in the milling process is crucial for improving manufacturing efficiency and tool life. Cutting sound detection using machine learning (ML) models, inspire…