Friday 28 March 2025
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 designed to identify and mitigate novel security risks in real-time.
CyberSentinel is an emergent threat detection system that integrates three primary threat detection layers: 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. The system continuously adapts to evolving adversarial tactics by incorporating automated model retraining and adaptive thresholds.
One of the key features of CyberSentinel is its ability to detect anomalies in real-time. This is achieved through a combination of isolation forest and Mahalanobis distance models, which are trained on large datasets of normal behavior. When an anomaly is detected, the system can trigger automatic responses, such as blocking IP addresses or banning users.
CyberSentinel also includes a phishing detection module that uses natural language processing (NLP) to identify suspicious emails and URLs. This module is capable of detecting even the most sophisticated phishing attacks, which often use AI-generated content designed to evade traditional security measures.
The system’s scalability is another significant advantage. CyberSentinel can handle high-traffic security workloads with ease, making it an ideal solution for large-scale enterprise environments. The system’s modular design also allows it to be easily integrated with existing security tools and workflows.
In addition to its technical capabilities, CyberSentinel is designed with human factors in mind. The system includes features such as explainable AI (XAI) and transparency dashboards, which provide security analysts with a clear understanding of the system’s decision-making process.
CyberSentinel has been tested extensively in a variety of scenarios, including high-traffic security environments and real-world attack simulations. The results show that CyberSentinel is capable of detecting threats with high accuracy and precision, while also reducing false positives to near zero.
Overall, CyberSentinel represents a significant advancement in the field of AI-driven cybersecurity. Its ability to detect anomalies in real-time, identify sophisticated phishing attacks, and adapt to evolving adversarial tactics makes it an essential tool for organizations seeking to protect themselves against emerging threats.
Cite this article: “CyberSentinel: A Unified AI-Powered Cybersecurity System for Real-Time Threat Detection and Mitigation”, The Science Archive, 2025.
Artificial Intelligence, Cybersecurity, Threat Detection, Machine Learning, Anomaly Detection, Phishing Attacks, Real-Time Monitoring, Explainable Ai, Security Workflows, Advanced Persistent Threats
Reference: Krti Tallam, “CyberSentinel: An Emergent Threat Detection System for AI Security” (2025).
