Machine Learning System for Malicious Website Detection: A Literature Review

Chaitanya R. Vyawhare, Reshma Y. Totare, Prashant S. Sonawane, Purva B. Deshmukh
2022 International Journal for Research in Applied Science and Engineering Technology  
Abstract: Today the most important concern in the field of cyber security is finding the serious problems that make loss in secure information. It is mainly due to malicious URLs. Malicious URLs are generated daily. This URLs are having a short life span. Various techniques are used by researchers for detecting such threats in a timely manner. Blacklist method is famous among them. Researchers uses this blacklist method for easily identifying the harmful URLs. They are very simple and easy
more » ... d. Due to their simplicity they are used as a traditional method for detecting such URLs. But this method suffers from many problems. The lack of ability in detecting newly generated malicious URLs is one of the main drawbacks of Blacklist method. Heuristic approach is also used for identifying some common attacks. It is an advanced technique of Blacklist method. But this method cannot be used for all type of attacks. So this method is used very shortly. For a good experience, the researchers introduce machine learning techniques. Machine Learning techniques go through several phases and detect the malicious URLs in an accurate manner. This method also gives the details about the false positive rate. This review paper studies the different phases such as feature extraction phase and feature representation phase of machine learning techniques for detecting malicious URLs. Different machine learning algorithms used for such detection is also discuss in this paper. And also gives a better understanding about the advantage of using machine learning over other techniques for detecting malicious URLs and problems it suffers. Keywords: Blacklist, Cyber Security, Malicious URL
doi:10.22214/ijraset.2022.42050 fatcat:fpzg3ymysvfehnzttmstg5snem