CyberSentinel: An Emergent Threat Detection System for AI Security

TL;DR

CyberSentinel is a unified AI-based system that detects and mitigates emerging cybersecurity threats in real time by integrating multiple detection methods and adapting to evolving attack tactics.

Contribution

The paper presents CyberSentinel, a novel unified system combining multiple threat detection techniques for real-time AI security threat mitigation.

Findings

Effective detection of brute-force SSH attacks.

Successful identification of phishing threats using heuristic scoring.

Adaptive anomaly detection improves threat response to evolving tactics.

Abstract

The rapid advancement of artificial intelligence (AI) has significantly expanded the attack surface for AI-driven cybersecurity threats, necessitating adaptive defense strategies. This paper introduces CyberSentinel, a unified, single-agent system for emergent threat detection, designed to identify and mitigate novel security risks in real time. CyberSentinel integrates: (1) Brute-force attack detection through SSH log analysis, (2) Phishing threat assessment using domain blacklists and heuristic URL scoring, and (3) Emergent threat detection via machine learning-based anomaly detection. By continuously adapting to evolving adversarial tactics, CyberSentinel strengthens proactive cybersecurity defense, addressing critical vulnerabilities in AI security.