Comprehensive Impact Assessment of Intrusion Detection and Mitigation Strategies Using Support Vector Machine Classification

Abstract

Cybersecurity remains a paramount concern in today's interconnected digital space, necessitating continual evaluation and refinement of intrusion detection and mitigation strategies. This study presents a comprehensive impact assessment of various intrusion detection and mitigation methods, adopting Support Vector Machine (SVM) classification for analysis. Through critical examination of diverse techniques, including signature-based, behavior-based, and anomaly-based approaches, the research evaluates their effectiveness in combating cyber threats. Notably, SVM classification achieves an accuracy score of 87%, revealing its utility in discerning subtle patterns indicative of malicious activity. Furthermore, the study highlights the significance of proactive measures such as user training, network segmentation, and multi-factor authentication in fortifying defense mechanisms. By providing valuable insights into the strengths and limitations of different approaches, this work contributes to the ongoing efforts to safeguard digital ecosystems against evolving cyber threats.

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