AI-Assisted Cybersecurity Threat Detection Through Behavioral Analytics and Anomaly Mining
Keywords:
cybersecurity, behavioral analytics, anomaly mining, artificial intelligence, threat detection, machine learning, network security, socio-technical systems, governance, algorithmic fairnessAbstract
The accelerating complexity of cyber threats demands detection paradigms that transcend static signature-based defenses toward adaptive, context-aware systems. This paper presents a comprehensive examination of AI-assisted cybersecurity threat detection through the dual lenses of behavioral analytics and anomaly mining. We argue that the integration of machine learning techniques with behavioral profiling enables the identification of subtle, previously unknown attack patterns while simultaneously raising critical structural challenges related to scalability, interpretability, fairness, and operational governance. The paper first establishes a conceptual foundation by distinguishing between supervised and unsupervised approaches to anomaly mining, then develops a multi-layered architectural framework that spans data acquisition, feature engineering, model inference, and human-in-the-loop decision support. We analyze the trade-offs inherent in deploying such systems across heterogeneous network infrastructures, emphasizing the tension between detection accuracy and false positive rates, the computational cost of real-time processing, and the need for continuous model adaptation in adversarial environments. Governance and policy dimensions are explored through the lenses of algorithmic bias, data privacy regulations, and the accountability of automated threat response. Cross-domain comparisons with fraud detection and industrial control systems illustrate the transferability and domain-specific limitations of behavioral analytics. Finally, we discuss sustainability concerns related to resource consumption and model drift, and outline forward-looking research directions that address robustness, transparency, and the socio-technical embedding of AI-enhanced security operations. The study contributes a systems-oriented perspective that bridges technical design with organizational and ethical imperatives, providing a reference for researchers and practitioners seeking to build resilient, responsible cybersecurity infrastructures.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
