SeizeMaliciousURL: A novel learning approach to detect malicious URLs

• Published in: Journal of information security and applications Vol. 62; p. 102967
• Main Authors: Mondal, Dipankar Kumar, Singh, Bikash Chandra, Hu, Haibo, Biswas, Shivazi, Alom, Zulfikar, Azim, Mohammad Abdul
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2021
• Subjects: Classification; Machine learning; Malicious URLs detection; Malicious URLs detection; Machine learning; Classification
• ISSN: 2214-2126
• DOI: 10.1016/j.jisa.2021.102967

Malicious websites are increasing in abundance, which brings serious web security threats to users. As a result, individuals lose their assets, values, information, etc. to unauthorized parties and become victims while visiting such websites. The research community put efforts into developing effective and efficient models for detecting malicious URLs to make available notifications about websites that users access. Perceiving this, numerous methods make use of various types of machine learning (ML) approaches. However, until now, no technique has perfectly detected malicious URLs, as they are susceptible to false positive and false negative decisions in classifying URLs in malicious and non-malicious groups. To improve this issue, this article proposes a new approach based on a machine learning technique. The proposed approach uses multiple classifiers (i.e., ensemble learning) to predict the class probabilities of URLs and then applies a threshold to filter the decisions of multiple classifiers. Next, the decisions are combined concerning its corresponding class probabilities and find the class label with the highest class probability as the final decision on unlabeled URLs. The results show that the proposed method offers better performance in terms of malicious URL detection than other methods.