A review on Machine Learning Approaches for Identifying and Preventing Cyber Attacks

Ujjawal Valiya; Deepak Kumar Gupta; Archana Jain

doi:10.69968/ijisem.2026v5i1160-168

Authors

Ujjawal Valiya M.Tech Student, School of Engineering & Technology, IIMT University, Meerut
Deepak Kumar Gupta Assistant Professor, Computer Science & Engineering Department, School of Engineering & Technology, IIMT University, Meerut
Archana Jain HOD, Computer Science & Engineering Department, School of Engineering & Technology, IIMT University, Meerut

DOI:

https://doi.org/10.69968/ijisem.2026v5i1160-168

Keywords:

Machine Learning, Cyber Attack Detection, Intrusion Detection Systems, Deep Learning, Cybersecurity

Abstract

The dynamic nature of cyber threats has brought in the need to come up with intelligent and adaptive security measures other than the conventional rule-based systems. Machine Learning (ML) has become a ground-breaking method to detect and prevent cyber attacks allowing recognition of patterns, detecting anomalies, and predictive threat analysis with the use of automated machines. The review paper presents an in-depth analysis of ML methods deployed to resolve cybersecurity issues, such as supervised, unsupervised, and deep-learning models, such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), and ensemble models. The paper examines how they have been used to detect various cyber threats, including DDoS, malware, phishing, ransomware, attacks on IoT, and attacks on the supply chain. In addition, the paper also surveys the latest literature, benchmark datasets, assessment metrics, and comparison of the performance of the current models. The major challenges, such as data imbalance, adversarial manipulation, model bias, privacy concerns, and scalability issues, are also presented. The review identifies the recent progress and specifies the research prospects to enhance more resilient and adaptive cybersecurity infrastructure based on ML.

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