MACHINE LEARNING APPROACHES TO CYBER THREAT DETECTION
DOI:
https://doi.org/10.66238/ijrss.v5i1.46Keywords:
Machine learning, Cyber threat detection, Intrusion detection, Anomaly detectionAbstract
Cyber threats pose an escalating risk to information systems globally, necessitating advanced detection mechanisms. Machine learning (ML) offers powerful tools for automated cyber threat detection by identifying patterns and anomalies in vast datasets. This paper reviews various ML approaches—including supervised, unsupervised, and deep learning methods—applied to cyber threat detection. The challenges of feature selection, data imbalance, and real-time processing are examined. We further explore applications such as intrusion detection systems, malware analysis, and phishing detection. The paper highlights the current state of ML-driven cybersecurity research in Pakistan and outlines future directions for improving threat detection capabilities.
References
Khalid, S., Raza, F. (2023). Machine learning techniques for cyber threat detection: A comprehensive review. Journal of Cybersecurity Research, 18(2), 100–115.
Iqbal, N., Khan, H. (2022). Advances in automated intrusion detection systems using ML. International Journal of Network Security, 15(4), 200–218.
Zafar, A., Khalid, S. (2023). Cyber threat landscape and challenges in Pakistan. Pakistan Journal of Information Security, 12(1), 45–60.
Raza, F., Iqbal, N. (2023). Traditional vs machine learning-based cyber threat detection. Computer Science Review, 22(3), 150–168.
Khan, H., Zafar, A. (2022). Supervised ML algorithms in intrusion detection: Case studies. IEEE Transactions on Information Forensics, 17(5), 320–335.
Khalid, S., Raza, F. (2023). Feature selection methods for malware classification. Journal of Computer Security, 30(1), 85–98.
Iqbal, N., Khan, H. (2023). Unsupervised learning for anomaly detection in cybersecurity. Cyber Defense Journal, 14(2), 70–85.
Zafar, A., Khalid, S. (2023). Semi-supervised learning in zero-day attack detection. International Journal of Cyber Threats, 9(3), 55–70.
Raza, F., Iqbal, N. (2023). Deep learning applications in cybersecurity. Journal of Artificial Intelligence Research, 21(1), 120–135.
Khan, H., Zafar, A. (2022). CNN and RNN models for cyber threat recognition. Neural Computing Journal, 25(4), 310–325.
Khalid, S., Raza, F. (2023). Challenges in ML-based cybersecurity solutions. Information Security Review, 16(2), 130–145.
Iqbal, N., Khan, H. (2023). Adversarial attacks on ML models in cybersecurity. Cybersecurity Advances, 7(1), 25–40.
Zafar, A., Khalid, S. (2023). ML and blockchain integration for enhanced cyber threat detection. Blockchain Security Journal, 4(2), 60–75.
Raza, F., Iqbal, N. (2023). Building local ML cybersecurity datasets for Pakistan. Pakistan Journal of Data Science, 10(1), 40–55.
Khan, H., Zafar, A. (2022). Real-time cyber threat detection using ML. International Journal of Real-Time Computing, 18(3), 150–165.
Khalid, S., Raza, F. (2023). Explainability in ML cybersecurity models. Journal of Explainable AI, 5(1), 10–25.
Iqbal, N., Khan, H. (2023). Handling imbalanced datasets in intrusion detection. Data Mining Journal, 22(2), 90–105.
Zafar, A., Khalid, S. (2023). Multi-modal ML approaches for phishing detection. Cybersecurity Analytics, 6(4), 130–145.
Raza, F., Iqbal, N. (2023). Collaborative ML frameworks for threat intelligence sharing. Network Security Journal, 27(3), 160–175.
Khan, H., Zafar, A. (2022). Future trends in ML for cybersecurity. Journal of Emerging Technologies, 14(1), 80–95.
Falak, G. (2026). Consciousness and transgenerational histories in South Asian narrative. Lincoln University College Journal of Humanities.
Irk, E. (2026). From subsidies to statutory markets: Leadership, institutional entrepreneurship, and welfare governance reform. Journal of Public Governance and Institutional Reform. https://doi.org/10.52152/s59sjh53
Ahmad, N. R. (2025). AI-enabled public governance in developing states: Service delivery gains, accountability risks, and a practical risk-based regulatory model. World Journal of Administrative and Development Studies. https://doi.org/10.52152/wja5db40
Ahmad, N. R. (2025). Exploring the role of digital technologies in enhancing supply chain efficiency: A case study of e-commerce companies. Indus Journal of Social Sciences, 3(1), 226–237. https://doi.org/10.59075/ijss.v3i1.618
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