4 citations · 4 across the 3 of their papers we have counts for
5 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…
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…
A Generative Caching System for Large Language Models
Arun Iyengar, Ashish Kundu, Ramana Kompella +1
Caching has the potential to be of significant benefit for accessing large language models (LLMs) due to their high latencies which typically range from a small number of seconds t…
Using Retriever Augmented Large Language Models for Attack Graph Generation
Renascence Tarafder Prapty, Ashish Kundu, Arun Iyengar
As the complexity of modern systems increases, so does the importance of assessing their security posture through effective vulnerability management and threat modeling techniques.…
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…