CyberSentinel: An Emergent Threat Detection System for AI Security
It addresses critical vulnerabilities in AI security for cybersecurity applications, but appears incremental as it combines existing methods like log analysis and anomaly detection.
The paper tackles the problem of AI-driven cybersecurity threats by introducing CyberSentinel, a unified system for emergent threat detection, which integrates brute-force attack detection, phishing assessment, and anomaly detection to identify and mitigate novel risks in real time.
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.