5 papers
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…
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.…
Code Hallucination
Mirza Masfiqur Rahman, Ashish Kundu
Generative models such as large language models are extensively used as code copilots and for whole program generation. However, the programs they generate often have questionable…
Graphene: Infrastructure Security Posture Analysis with AI-generated Attack Graphs
Xin Jin, Charalampos Katsis, Fan Sang +4
The rampant occurrence of cybersecurity breaches imposes substantial limitations on the progress of network infrastructures, leading to compromised data, financial losses, potentia…