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Verifiable AI Reviewers: Decentralized Skill Matching and Soulbound Reputation for Multi-Agent Peer Review

PRECISE: Precision-Driven Discovery of Token-centric MEV

Explainable Malware Detection from Noisy API Sequences with RAG-Based MITRE ATT&CK Mapping

An Empirical Study on the Transferability of Transformer-Based Models for Software Vulnerability Detection

Despite the dominance of Transformer-based models in software vulnerability detection, the extent to which their learned security logic generalizes across different programming languages remains…

Graph-Driven LLM-Augmented Stateful API Fuzzing for OWASP API Top 10

Security testing of REST APIs remains difficult because real-world OpenAPI specifications are often incomplete, many security flaws are inherently stateful, and prevailing stateful fuzzers…

A Class-incremental and Few-shot learning model for Intrusion detection under Concept drift

Network Intrusion Detection Systems (NIDS) based on machine learning must evolve after deployment to detect emerging threats very soon with a few labeled samples,…

Assessing Evasion and Behavioral Trade-offs of Obfuscation-Conditioned LLMs in PowerShell Attack Synthesis

S-AECR: Word Intelligence and Explanation Strategy for Cyber Threat Intelligence Extraction and Mapping

MORPH-IDS: A Context-Driven Multi-Agent Reinforcement Learning Framework for Drift-Aware Moving Target Defense in Adversarial-Robust Intrusion Detection

In the rapidly evolving cybersecurity landscape, Machine Learning (ML)-based Network Intrusion Detection Systems (NIDS) have become essential for detecting sophisticated threats, yet they are…