2 citations · 3 across the 11 of their papers we have counts for
12 papers
KAN-Robust-Bench: A Benchmark for Evaluating the Robustness of Kolmogorov-Arnold Networks
Mohammad Meymani, Roozbeh Razavi-Far
While machine learning models have demonstrated strong performance in many domains, these models have shown profound vulnerabilities when they are exposed to adversarial threats. W…
Quantum Adversarial Machine Learning: From Classical Adaptations to Quantum-Native Methods
Roozbeh Razavi-Far, Mohammad Meymani, Erfan Mahmoudinia +6
Machine learning has revolutionized numerous industrial domains. Despite recent advances, machine learning models remain vulnerable to adversarial threats. Adversarial machine lear…
Integrating Graphs, Large Language Models, and Agents: Reasoning and Retrieval
Hamed Jelodar, Samita Bai, Mohammad Meymani +3
Generative AI, particularly Large Language Models, increasingly integrates graph-based representations to enhance reasoning, retrieval, and structured decision-making. Despite rapi…
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…
Divided We Fall: Defending Against Adversarial Attacks via Soft-Gated Fractional Mixture-of-Experts with Randomized Adversarial Training
Mohammad Meymani, Roozbeh Razavi-Far
Machine learning is a powerful tool enabling full automation of a huge number of tasks without explicit programming. Despite recent progress of machine learning in different domain…
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…
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…
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…
Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets
Hamed Jelodar, Mohammad Meymani, Roozbeh Razavi-Far
Large language models (LLMs) and transformer-based architectures are increasingly utilized for source code analysis. As software systems grow in complexity, integrating LLMs into c…