166 citations · 291 across the 36 of their papers we have counts for
16 papers · 1 filter
LLM4CodeRE: Generative AI for Code Decompilation Analysis and Reverse Engineering
Hamed Jelodar, Samita Bai, Tochukwu Emmanuel Nwankwo +4
Code decompilation analysis is a fundamental yet challenging task in malware reverse engineering, particularly due to the pervasive use of sophisticated obfuscation techniques. Alt…
Automated Malware Family Classification using Weighted Hierarchical Ensembles of Large Language Models
Samita Bai, Hamed Jelodar, Tochukwu Emmanuel Nwankwo +4
Malware family classification remains a challenging task in automated malware analysis, particularly in real-world settings characterized by obfuscation, packing, and rapidly evolv…
Routing-Aware Explanations for Mixture of Experts Graph Models in Malware Detection
Hossein Shokouhinejad, Roozbeh Razavi-Far, Griffin Higgins +1
Mixture-of-Experts (MoE) offers flexible graph reasoning by combining multiple views of a graph through a learned router. We investigate routing-aware explanations for MoE graph mo…
Transparent Malware Detection With Granular Assembly Flow Explainability via Graph Neural Networks
Griffin Higgins, Roozbeh Razavi-Far, Hossein Shokouhinejad +1
As malware continues to become increasingly sophisticated, threatening, and evasive, malware detection systems must keep pace and become equally intelligent, powerful, and transpar…
LLM Security and Safety: Insights from Homotopy-Inspired Prompt Obfuscation
Luis Lazo, Hamed Jelodar, Roozbeh Razavi-Far
In this study, we propose a homotopy-inspired prompt obfuscation framework to enhance understanding of security and safety vulnerabilities in Large Language Models (LLMs). By syste…
A Research and Development Portfolio of GNN Centric Malware Detection, Explainability, and Dataset Curation
Hossein Shokouhinejad, Griffin Higgins, Roozbeh Razavi-Far +1
Graph Neural Networks (GNNs) have become an effective tool for malware detection by capturing program execution through graph-structured representations. However, important challen…
Explainable Attention-Guided Stacked Graph Neural Networks for Malware Detection
Hossein Shokouhinejad, Roozbeh Razavi-Far, Griffin Higgins +1
Malware detection in modern computing environments demands models that are not only accurate but also interpretable and robust to evasive techniques. Graph neural networks (GNNs) h…
A Study on Semi-Supervised Detection of DDoS Attacks under Class Imbalance
Ehsan Hallaji, Vaishnavi Shanmugam, Roozbeh Razavi-Far +1
One of the most difficult challenges in cybersecurity is eliminating Distributed Denial of Service (DDoS) attacks. Automating this task using artificial intelligence is a complex p…
Dual Explanations via Subgraph Matching for Malware Detection
Hossein Shokouhinejad, Roozbeh Razavi-Far, Griffin Higgins +1
Interpretable malware detection is crucial for understanding harmful behaviors and building trust in automated security systems. Traditional explainable methods for Graph Neural Ne…
Large Language Model (LLM) for Software Security: Code Analysis, Malware Analysis, Reverse Engineering
Hamed Jelodar, Samita Bai, Parisa Hamedi +3
Large Language Models (LLMs) have recently emerged as powerful tools in cybersecurity, offering advanced capabilities in malware detection, generation, and real-time monitoring. Nu…
On the Consistency of GNN Explanations for Malware Detection
Hossein Shokouhinejad, Griffin Higgins, Roozbeh Razavi-Far +2
Control Flow Graphs (CFGs) are critical for analyzing program execution and characterizing malware behavior. With the growing adoption of Graph Neural Networks (GNNs), CFG-based re…
Towards Privacy-Preserving Split Learning: Destabilizing Adversarial Inference and Reconstruction Attacks in the Cloud
Griffin Higgins, Roozbeh Razavi-Far, Xichen Zhang +3
This work aims to provide both privacy and utility within a split learning framework while considering both forward attribute inference and backward reconstruction attacks. To addr…
A Study on the Importance of Features in Detecting Advanced Persistent Threats Using Machine Learning
Ehsan Hallaji, Roozbeh Razavi-Far, Mehrdad Saif
Advanced Persistent Threats (APTs) pose a significant security risk to organizations and industries. These attacks often lead to severe data breaches and compromise the system for…
Recent Advances in Malware Detection: Graph Learning and Explainability
Hossein Shokouhinejad, Roozbeh Razavi-Far, Hesamodin Mohammadian +4
The rapid evolution of malware has necessitated the development of sophisticated detection methods that go beyond traditional signature-based approaches. Graph learning techniques…
Explainable Malware Detection through Integrated Graph Reduction and Learning Techniques
Hesamodin Mohammadian, Griffin Higgins, Samuel Ansong +2
Control Flow Graphs and Function Call Graphs have become pivotal in providing a detailed understanding of program execution and effectively characterizing the behavior of malware.…
Decentralized Federated Learning: A Survey on Security and Privacy
Ehsan Hallaji, Roozbeh Razavi-Far, Mehrdad Saif +2
Federated learning has been rapidly evolving and gaining popularity in recent years due to its privacy-preserving features, among other advantages. Nevertheless, the exchange of mo…