166 citations · 291 across the 36 of their papers we have counts for
21 papers · 1 filter
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…
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…
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…
Proxy-Anchor and EVT-Driven Continual Learning Method for Generalized Category Discovery
Alireza Fathalizadeh, Roozbeh Razavi-Far
Continual generalized category discovery has been introduced and studied in the literature as a method that aims to continuously discover and learn novel categories in incoming dat…
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…
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…
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…
FedNIA: Noise-Induced Activation Analysis for Mitigating Data Poisoning in Federated Learning
Ehsan Hallaji, Roozbeh Razavi-Far, Mehrdad Saif
Federated learning systems are increasingly threatened by data poisoning attacks, where malicious clients compromise global models by contributing tampered updates. Existing defens…
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…
TrustChain: A Blockchain Framework for Auditing and Verifying Aggregators in Decentralized Federated Learning
Ehsan Hallaji, Roozbeh Razavi-Far, Mehrdad Saif
The server-less nature of Decentralized Federated Learning (DFL) requires allocating the aggregation role to specific participants in each federated round. Current DFL architecture…
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…
Federated Continual Learning: Concepts, Challenges, and Solutions
Parisa Hamedi, Roozbeh Razavi-Far, Ehsan Hallaji
Federated Continual Learning (FCL) has emerged as a robust solution for collaborative model training in dynamic environments, where data samples are continuously generated and dist…