Hybrid Machine Learning Approach For Real-Time Malicious Url Detection Using Som-Rmo And Rbfn With Tabu Search Optimization

TL;DR

This paper presents a hybrid machine learning model combining SOM-RMO for feature extraction and RBFN with Tabu Search for classification, achieving high accuracy in real-time malicious URL detection.

Contribution

It introduces a novel hybrid approach that enhances feature selection and classification accuracy for malicious URL detection in real-time environments.

Findings

Achieved 96.5% accuracy on benchmark dataset

Outperformed traditional detection methods

Demonstrated high precision and recall in identifying malicious URLs

Abstract

The proliferation of malicious URLs has become a significant threat to internet security, encompassing SPAM, phishing, malware, and defacement attacks. Traditional detection methods struggle to keep pace with the evolving nature of these threats. Detecting malicious URLs in real-time requires advanced techniques capable of handling large datasets and identifying novel attack patterns. The challenge lies in developing a robust model that combines efficient feature extraction with accurate classification. We propose a hybrid machine learning approach combining Self-Organizing Map based Radial Movement Optimization (SOM-RMO) for feature extraction and Radial Basis Function Network (RBFN) based Tabu Search for classification. SOM-RMO effectively reduces dimensionality and highlights significant features, while RBFN, optimized with Tabu Search, classifies URLs with high precision. The proposed model demonstrates superior performance in detecting various malicious URL attacks. On a benchmark dataset, our approach achieved an accuracy of 96.5%, precision of 95.2%, recall of 94.8%, and an F1-score of 95.0%, outperforming traditional methods significantly.