7 citations · 8 across the 10 of their papers we have counts for
10 papers
PhantomCall: Evading ML Malware Detectors via Function Call Graph Perturbation
Md Ajwad Akil, Adrian Shuai Li, Imtiaz Karim +3
Prior adversarial attacks on Windows PE malware detectors target raw bytes, PE headers, or intra-function control-flow graphs, leaving the function call graph (FCG) unexplored as a…
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…
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…
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…
Transfer Learning for Security: Challenges and Future Directions
Adrian Shuai Li, Arun Iyengar, Ashish Kundu +1
Many machine learning and data mining algorithms rely on the assumption that the training and testing data share the same feature space and distribution. However, this assumption m…
Maximizing Information in Domain-Invariant Representation Improves Transfer Learning
Adrian Shuai Li, Elisa Bertino, Xuan-Hong Dang +3
We propose MaxDIRep, a domain adaptation method that improves the decomposition of data representations into domain-independent and domain-dependent components. Existing methods, s…
Building Manufacturing Deep Learning Models with Minimal and Imbalanced Training Data Using Domain Adaptation and Data Augmentation
Adrian Shuai Li, Elisa Bertino, Rih-Teng Wu +1
Deep learning (DL) techniques are highly effective for defect detection from images. Training DL classification models, however, requires vast amounts of labeled data which is ofte…
A Capability-based Distributed Authorization System to Enforce Context-aware Permission Sequences
Adrian Shuai Li, Reihaneh Safavi-Naini, Philip W. L. Fong
Controlled sharing is fundamental to distributed systems. We consider a capability-based distributed authorization system where a client receives capabilities (access tokens) from…