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Multimodal Fusion for Vulnerability Detection: Integrating Sequence and Graph-Based Analysis with LLM Augmentation

RAG-SmartVuln: Enhancing Smart Contract Vulnerability Detection via Retrieval-Augmented LLMs

ProDef-MDS: A Proactive Defense Mechanism Protecting Malware Detection Systems from Adversarial Attacks

Malware threatens cybersecurity by enabling data theft, unauthorized access, and extortion. Traditional malware detection systems (MDS) struggle with the increasing volume and complexity of…

XGV-BERT: Leveraging Contextualized Language Model and Graph Neural Network for Efficient Software Vulnerability Detection

PoFQ: A Blockchain Consensus Protocol for Decentralized Federated Learning-Based Threat Hunting Approach in a Trustless Computing Landscape

An empirical review of the effectiveness of different language processing approaches in Software Code Vulnerability Detection

The advancement of software vulnerability detection tools has accelerated in recent years, yet the prevalence and severity of vulnerabilities continue to escalate, posing significant…

RAX-ClaMal: Dynamic Android malware classification based on RAX register values

Detecting malware on Android remains a major challenge because malicious apps use sophisticated evasion techniques. This study presents RAX-ClaMal, a novel approach leveraging dynamic…

On the Effectiveness of Adversarial Samples against Ensemble Learning-based Windows PE Malware Detectors

The cybersecurity landscape is witnessing an increasing prevalence of threats and malicious programs, posing formidable challenges to conventional detection techniques. Although machine learning (ML)…

VulnSense: Efficient Vulnerability Detection in Ethereum Smart Contracts by Multimodal Learning with Graph Neural Network and Language Model