Enhancing Cybersecurity Through Machine Learning-based Intrusion Detection Systems

Abstract

This research paper explores the application of machine learning techniques in improving the efficiency and accuracy of intrusion detection systems (IDS) for enhancing cybersecurity. Traditional IDS often struggle with the ever-evolving nature of cyber threats, leading to high false positive rates and delayed responses. The proposed approach leverages machine learning algorithms, including deep learning models, to analyses network traffic patterns and identify anomalous behavior indicative of potential cyber-attacks. The study evaluates the performance of various machine learning algorithms in real-world scenarios and compares them with traditional rule-based IDS. The goal is to develop a more adaptive and robust intrusion detection system capable of accurately detecting and mitigating both known and novel cyber threats.

This research contributes to the field of computer science by addressing the pressing need for advanced cybersecurity solutions and leveraging the capabilities of machine learning to enhance the effectiveness of intrusion detection systems. The findings aim to provide valuable insights for the development of next-generation cybersecurity frameworks that can better protect critical systems and networks in the face of evolving cyber threats.

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