Artificial Intelligence in Cybersecurity: A Review of Machine Learning-Based Threat Detection Techniques, Challenges, and Future Directions

Amrish Dhawan; Karmjit Kaur; Amninder Kaur

doi:10.62127/aijmr.2026.v04i05.1558

Artificial Intelligence in Cybersecurity: A Review of Machine Learning-Based Threat Detection Techniques, Challenges, and Future Directions

Author(s)	Dr. Amrish Dhawan, Ms. Karmjit Kaur, Ms. Amninder Kaur
Country	India
Abstract	Artificial intelligence (AI), particularly machine learning (ML), has gained increasing importance in cybersecurity threat detection by enabling the analysis of network traffic and the identification of potentially malicious activities. However, the effectiveness of these techniques depends on data quality, detection performance, generalisation to unfamiliar attacks, and resilience to adversarial manipulation. This paper presents a qualitative, literature-based review of machine learning techniques used in cybersecurity threat detection, with particular emphasis on intrusion detection systems. It examines supervised learning, unsupervised learning, deep learning, and hybrid or ensemble approaches, highlighting their principal capabilities, data requirements, and limitations. The review also considers key challenges, including false-positive alerts, dataset limitations, computational requirements, interpretability, and adversarial vulnerabilities. Furthermore, it discusses potential research directions involving representative evaluation datasets, improved detection of emerging threats, adversarial robustness, and computational efficiency. The review emphasises that the practical reliability of machine learning-based cybersecurity systems cannot be assessed through classification accuracy alone; generalisation, robustness, and operational suitability must also be considered. These insights provide a basis for understanding the capabilities and limitations of current approaches and for guiding further research into more dependable cybersecurity threat detection systems.
Keywords	Artificial intelligence, machine learning, cybersecurity, intrusion detection, threat detection, adversarial robustness, anomaly detection.
Discipline	Computer > AI / ML
Published In	Volume 4, Issue 5, September-October 2026
Published On	2026-10-12
DOI	https://doi.org/10.62127/aijmr.2026.v04i05.1558

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Artificial Intelligence in Cybersecurity: A Review of Machine Learning-Based Threat Detection Techniques, Challenges, and Future Directions

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