7 citations · 13 across the 16 of their papers we have counts for
12 papers · 1 filter
Asm2SrcEval: Evaluating Large Language Models for Assembly-to-Source Code Translation
Parisa Hamedi, Hamed Jelodar, Samita Bai +3
Assembly-to-source code translation is a critical task in reverse engineering, cybersecurity, and software maintenance, yet systematic benchmarks for evaluating large language mode…
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…
Can Small GenAI Language Models Rival Large Language Models in Understanding Application Behavior?
Mohammad Meymani, Hamed Jelodar, Parisa Hamedi +2
Generative AI (GenAI) models, particularly large language models (LLMs), have transformed multiple domains, including natural language processing, software analysis, and code under…
SBAN: A Framework & Multi-Dimensional Dataset for Large Language Model Pre-Training and Software Code Mining
Hamed Jelodar, Mohammad Meymani, Samita Bai +2
This paper introduces SBAN (Source code, Binary, Assembly, and Natural Language Description), a large-scale, multi-dimensional dataset designed to advance the pre-training and eval…
FlexiDataGen: An Adaptive LLM Framework for Dynamic Semantic Dataset Generation in Sensitive Domains
Hamed Jelodar, Samita Bai, Roozbeh Razavi-Far +1
Dataset availability and quality remain critical challenges in machine learning, especially in domains where data are scarce, expensive to acquire, or constrained by privacy regula…
XGen-Q: An Explainable Domain-Adaptive LLM Framework with Retrieval-Augmented Generation for Software Security
Hamed Jelodar, Mohammad Meymani, Roozbeh Razavi-Far +1
Generative AI and large language models (LLMs) have shown strong capabilities in code understanding, but their use in cybersecurity, particularly for malware detection and analysis…
NLD-LLM: A systematic framework for evaluating small language transformer models on natural language description
Hamed Jelodar, Mohammad Meymani, Parisa Hamedi +4
Natural Language Description (NLD) is a Natural Language Processing (NLP) task that requires models to generate structured and meaningful outputs from natural language inputs. In t…
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…
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…
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…