Semester of Graduation
Electrical and Computer Engineering
First Major Professor
Master of Science (MS)
Malicious web site is a foundation of criminal activities on Internet. This links enables partial or full machine control to the attackers. This results in victim systems, which get easily infected allowing attackers to utilize systems for quite a number of cyber-crimes such as stealing credentials, spamming, phishing, denial-of-service and many extra such attacks. Therefore, the methodology and technique to detect such crimes should be fast and precise with the additional capability to detect new malicious websites or content. This paper introduces an automatic tool to extract 110 significant features for a URL. Additionally, this paper also propose various aspects associated with the URL (Uniform Resource Locator) classification process which recognizes whether the target website is a malicious or benign. Standard datasets are utilized for training purpose from diverse sources. The rising issue related to spamming, phishing and malware, has created a requirement for solid framework solution which can analyze the extracted features, classify and further recognize the malicious URL.
Garg, Shivika, "Feature-Rich Models and Feature Reduction for Malicious URLs Classification and Prediction" (2019). Creative Components. 179.