mlsanityData to 7 Oct 2026

CyberSentinel: An Emergent Threat Detection System for AI Security

arXiv 2502.14966 ↗First posted 20 Feb 2025cs.CR cs.AIcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 10 of its 24 references that are in the map. First posted on arXiv 20 Feb 2025.

Builds on

10 of 24 references.

References · 10

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  2. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,976
  5. 2019Theoretically Principled Trade-off between Robustness and Accuracycited by 1,510
  6. 2017Ensemble Adversarial Training: Attacks and Defensescited by 833
  7. 2015The Limitations of Deep Learning in Adversarial Settingscited by 784
  8. 2017Evasion Attacks against Machine Learning at Test Timecited by 686
  9. 2020Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claimscited by 163
  10. 2018Adversarial Attacks and Defences Competitioncited by 96

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Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025Engineering Risk-Aware, Security-by-Design Frameworks for Assurance of Large-Scale Autonomous AI Modelscited by 3
  2. 2025Operationalizing CaMeL: Strengthening LLM Defenses for Enterprise Deploymentcited by 0
  3. 2025Embedding Trust at Scale: Physics-Aware Neural Watermarking for Secure and Verifiable Data Pipelinescited by 0