RvbbitSafe

RvbbitSafe: हैकर का दुःस्वप्न - विंडोज के लिए छह-दुर्ग एंटी-रैनसमवेयर आर्किटेक्चर 🛡️ अवलोकन RvbbitSafe एक शोध प्रोटोटाइप है जो रैनसमवेयर सुरक्षा में एक प्रतिमान बदलाव प्रदर्शित करता है। यह एक सक्रिय, बहु-स्तरीय किला है जो हार्डवेयर वर्चुअलाइजेशन, AI-संचालित धोखा, निम्न-स्तरीय डेटा रिकवरी,...

aethel_core

Aethel-Core: Automated Cognitive Defense & Cyber Threat Simulation WAF A hybrid cybersecurity framework built in SWI-Prolog , designed for real-time threat mitigation and generating structured logic datasets for LLM Instruction Tuning and Neuro-Symbolic AI Training. Key Innovations & Architecture...

Automating Attack Graph Construction for Agentic Pentesting. Towards Neuro-Symbolic Vulnerability Hunting

Logic attack graphs grounded in scanner output provide explicit and auditable attack path reasoning LLM-based agents lack. Integrating symbolic frameworks such as MulVAL to contemporary security workflows or agentic pipelines, however, requires translating scanner evidence to initial facts, and...

NeuroGraph: An AI Graph-Driven Neuro-Symbolic Framework for Explainable Threat Reasoning in Advanced Manufacturing

The growing complexity of cyber-physical attack surfaces in advanced manufacturing has made cyber threat intelligence analysis increasingly difficult. Although large language models and retrieval-augmented generation have improved CTI workflows, text-based approaches remain vulnerable to...

Unsaid, Unsafe? Implicit Security Obligations in LLM-Based RTL Code Generation

Large Language Models LLMs generate register-transfer-level RTL code with rapidly improving functional correctness. Security of LLM-generated code, however, has been studied mainly for software, where flaws can still be patched after deployment. Insecure RTL offers no such remedy once taped out...

A Deployment-Oriented and Resource-Efficient Neuro-Symbolic Framework for Explainable DDoS Detection in Operational Technology Networks

Operational technology OT environments, including programmable logic controllers PLCs, industrial control systems ICS, and supervisory control and data acquisition SCADA systems, are increasingly targeted by distributed denial-of-service DDoS attacks. This paper presents a neuro-symbolic framewor...

Antiproof: Synthesizing Vulnerability Detectors and Proofs of Exploitability

Discovering vulnerabilities before attackers exploit them requires high recall and reliable automatic validation, but existing approaches struggle to achieve both without prohibitive cost. We present Antiproof, an end-to-end vulnerability discovery system that combines neuro-symbolic detector...

Agentic SABRE: An Uncertainty-Aware Neuro-Symbolic Multi-Agent Framework for Adaptive Ransomware Detection

Ransomware has evolved into a complex, adaptive, and fast-moving adversary category in which static signatures and monolithic classifiers fail to generalise under concept drift, evasion, and behavioural polymorphism. In this paper, we present Agentic SABRE Semantic-Behavioural Arbitration for...

HunterAgent: Neuro-Symbolic Attack Trace Reconstruction under Anti-Forensics

Modern alert-triage systems reduce SOC burden by filtering false positives, but flagging a high-risk alert is only the start of incident response. Threat hunting requires reconstructing causal attack chains across heterogeneous, partially corrupted logs. Against APTs using anti-forensics parent-P...

Securing the Dark Matter: A Semantic-Enhanced Neuro-Symbolic Framework for Supply Chain Analysis of Opaque Industrial Software

Automated vulnerability detection in critical-infrastructure software confronts a fundamental barrier: industrial software is routinely deployed as stripped, symbol-free binaries that deprive conventional Software Composition Analysis of the source-level transparency it requires. Existing binary...

Finding Memory Leaks in C/C++ Programs Via Neuro-Symbolic Augmented Static Analysis

Memory leaks remain prevalent in real-world C/C++ software. Static analyzers such as CodeQL provide scalable program analysis but frequently miss such bugs because they cannot recognize project-specific custom memory-management functions and lack path-sensitive control-flow modeling. We present...

From Threat Intelligence to Firewall Rules: Semantic Relations in Hybrid AI Agent and Expert System Architectures

Web security demands rapid response capabilities to evolving cyber threats. Agentic Artificial Intelligence AI promises automation, but the need for trustworthy security responses is of the utmost importance. This work investigates the role of semantic relations in extracting information for...

QRS: A Rule-Synthesizing Neuro-Symbolic Triad for Autonomous Vulnerability Discovery

Static Application Security Testing SAST tools are integral to modern DevSecOps pipelines, yet tools like CodeQL, Semgrep, and SonarQube remain fundamentally constrained: they require expert-crafted queries, generate excessive false positives, and detect only predefined vulnerability patterns...

A Neuro-Symbolic Multi-Agent Approach to Legal-Cybersecurity Knowledge Integration

The growing intersection of cybersecurity and law creates a complex information space where traditional legal research tools struggle to deal with nuanced connections between cases, statutes, and technical vulnerabilities. This knowledge divide hinders collaboration between legal experts and...

Neuro-Symbolic AI for Cybersecurity: State of the Art, Challenges, and Opportunities

Traditional Artificial Intelligence AI approaches in cybersecurity exhibit fundamental limitations: inadequate conceptual grounding leading to non-robustness against novel attacks; limited instructibility impeding analyst-guided adaptation; and misalignment with cybersecurity objectives...

Automated Static Vulnerability Detection Via a Holistic Neuro-Symbolic Approach

Static vulnerability detection is still a challenging problem and demands excessive human efforts, e.g., manual curation of good vulnerability patterns. None of prior works, including classic program analysis or Large Language Model LLM-based approaches, have fully automated such vulnerability...