Ajit Kumar,
Assistant Professor, Department of Computer Applications, Noida Institute of Engineering & Technology, Greater Noida – 201306 ajitjnvassam@gmail.com https://orcid.org/0009-0001-8551-3539 |
Kumari Puja,
Assistant Professor Department of Computer Applications Noida Institute of Engineering & Technology Greater Noida – 201306 https://orcid.org/0009-0006-7487-4861 |
Anupama,
Assistant Professor, Department of Computer Applications, Noida Institute of Engineering & Technology, Greater Noida – 201306 https://orcid.org/0009-0009-1674-9087 |
Lakshmi Kumari,
Assistant Professor, Department of Computer Applications, Noida Institute of Engineering & Technology, Greater Noida – 201306 https://orcid.org/0009-0004-8161-1547 |
Sachin Kashyap,
Assistant Professor, Department of Computer Applications, Noida Institute of Engineering & Technology, Greater Noida – 201306 https://orcid.org/0009-0002-2641-1214 |
Jitendra Kumar Sonkar,
Assistant Professor, Department of Computer Applications, Noida Institute of Engineering & Technology, Greater Noida – 201306 https://orcid.org/0009-0002-9674-3679 |
Abstract:
As the digital landscape continues to expand, cyber-attacks have become a significant threat to organizations, industries, and governments worldwide. Traditional methods of cybersecurity are often inadequate in addressing the rapidly evolving nature of these threats. This has led to the integration of machine learning (ML) approaches as a powerful solution for detecting and preventing cyber-attacks. ML techniques, including supervised, unsupervised, and reinforcement learning, are now being applied to analyse vast amounts of data, identify patterns, and detect anomalies that could indicate malicious activity.
This paper explores the various machine learning algorithms used in cybersecurity, such as decision trees, support vector machines, neural networks, and clustering techniques, and how they contribute to threat detection and prevention. We analyse real-time applications of these methods in intrusion detection systems (IDS), malware detection, and network traffic analysis. Additionally, the paper discusses the challenges of implementing ML-based cybersecurity solutions, such as data scarcity, model interpretability, and adversarial attacks. Through these discussions, this research aims to highlight the role of machine learning in creating more robust, adaptive, and proactive cybersecurity systems. The future of cyber defence lies in the continual advancement of machine learning models that can evolve alongside the increasingly sophisticated threats they are designed to combat.
Keywords:
Cybersecurity, Machine Learning, Cyber Attacks, Intrusion Detection Systems, Malware Detection, Network Traffic Analysis, Supervised Learning, Unsupervised Learning, Anomaly Detection, Adversarial Attacks, Threat Prevention.
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