# CyberSentinel: An Emergent Threat Detection System for AI Security

**Authors:** Krti Tallam  
**arXiv:** [2502.14966](https://arxiv.org/abs/2502.14966)  
**Date:** 2025-02-20  
**Summary page:** https://www.summarizepaper.com/en/paper/2502.14966v1/

## Key points

- CyberSentinel is a unified single-agent system for real-time threat detection in AI-driven cybersecurity.
- The system integrates various detection mechanisms, including brute-force attack detection, phishing threat assessment, and emergent threat detection through machine learning.
- Flagged URLs can be directed to an alerting mechanism that integrates with incident response tools or notifies administrators through channels like Slack, email, or SIEM dashboards.
- The phishing detection module operates in a stateless manner for easy deployment and scalability under high traffic conditions.
- Organizations can customize CyberSentinel by adjusting the heuristic engine or integrating third-party threat feeds to adapt to changing security needs.
- The Emergent Threat Detector (ETD) processes multiple data streams using feature engineering techniques and anomaly detection models to identify emerging threats.
- By moving away from signature-based approaches towards adaptive methodologies, CyberSentinel enhances real-time threat detection while reducing false positives.

## Summary

CyberSentinel is a unified single-agent system designed to detect emergent threats in real-time within the rapidly evolving landscape of AI-driven cybersecurity. The system integrates various detection mechanisms such as brute-force attack detection through SSH log analysis, phishing threat assessment using domain blacklists and heuristic URL scoring, and emergent threat detection via machine learning-based anomaly detection. Additionally, flagged URLs can be directed to an alerting mechanism that can integrate with incident response tools or notify administrators through various channels like Slack, email, or SIEM dashboards. The phishing detection module operates in a stateless manner to simplify deployment and scale efficiently under high traffic conditions. Organizations can easily adapt CyberSentinel to their changing security needs by adjusting the heuristic engine or integrating third-party threat feeds. The Emergent Threat Detector (ETD) within CyberSentinel processes multiple data streams including system logs, network telemetry, GitHub activity, and user behavior. It utilizes feature engineering techniques and anomaly detection models like Isolation Forest and Mahalanobis Distance to identify emerging threats. The model continuously updates using historical data for adaptive threat detection. By moving away from signature-based approaches towards more adaptive and flexible methodologies, CyberSentinel enhances real-time threat detection while minimizing false positives that could disrupt legitimate user activities. This proactive approach strengthens cybersecurity defenses against novel security risks posed by AI-driven cyber threats.