Intelligent Intrusion Detection Categorization using Support Vector and Fuzzy Logic

Abstract

Intelligent intrusion detection is a crucial component in ensuring the security of computer networks and systems. Traditional intrusion detection systems (IDS) often struggle to handle the complexities and uncertainties associated with modern network environments. To address these challenges, a novel approach is proposed in this paper that combines fuzzy logic and support vector classification.

The main objective of this research is to enhance the accuracy and efficiency of intrusion detection by leveraging the complementary strengths of both fuzzy logic and support vector classification. Fuzzy logic allows for the representation of uncertainty and imprecision in data, making it well-suited for handling the vagueness often present in network traffic and system logs. On the other hand, support vector classification is a powerful machine learning technique known for its ability to handle high-dimensional data and effectively classify complex patterns.