Detect Potentially Risky Websites using Hidden Markov Model

Malicious URLs pose a significant cybersecurity threat, facilitating the spread of malicious software, unauthorized access to personal data, and phishing attempts. Conventional detection techniques relying on blacklists and heuristics are increasingly ineffective against evolving evasion strategies. This study introduces a novel approach titled "Malicious URL Detection based on Machine Learning," utilizing Hidden Markov Models (HMM) to quantify and predict the behavior of Malicious Web Services (MWS). Unlike traditional methods, this approach predicts response times, enabling quantitative ranking. Leveraging metrics like system predictability and response time variability, this methodology automatically identifies reliable MWS. Through experimentation with real-world data, our approach demonstrates superior performance, achieving an accuracy rate of 85% compared to the existing algorithm's 80%. This 5% improvement underscores its efficacy in recognizing and categorizing dangerous URLs. By considering dynamic parameters and employing advanced modeling techniques, this methodology offers an efficient means of cybersecurity threat detection, potentially advancing the field significantly.