Study on Detection of Cyber Attacks Using Machine Learning
International Research Journal of Modernization in Engineering Technology and Science · 2023
The escalating complexity and sophistication of cyber attacks have created a pressing need for innovative and effective defense mechanisms.Machine learning has emerged as a powerful tool in combating these threats by enabling the detection, classification, and mitigation of cyber attacks.This abstract provides an overview of the application of machine learning techniques in the field of cybersecurity.Machine learning algorithms can analyze large volumes of data, including network traffic, system logs, and user behavior, to identify patterns and anomalies indicative of cyber attacks.Supervised learning algorithms leverage labeled datasets to classify instances as either normal or malicious, while unsupervised learning methods identify deviations from expected behavior without predefined labels.Hybrid approaches combine the strengths of both methods to establish baselines and detect anomalies.Key challenges in employing machine learning for cyber attack detection include dealing with noisy and irrelevant data, selecting meaningful features, and addressing potential evasion techniques employed by adversaries.Adversarial machine learning techniques aim to enhance the robustness of defense systems by identifying vulnerabilities and mitigating evasion attempts.By harnessing the power of machine learning, organizations can proactively detect and respond to cyber threats, minimizing potential damage and safeguarding critical assets.Continuous adaptation and improvement of machine learning models are essential to keep pace with the evolving tactics of cybercriminals.The integration of machine learning with other cybersecurity practices holds immense potential for developing intelligent defense systems capable of effectively countering cyber attacks in an increasingly interconnected world.