Detection of Malicious URLs using Artificial Intelligence
Main Article Content
Abstract
Background/Objectives: The main objective of the project is to avoid various security threats and network attacks by detecting malicious Uniform Resource Locator(URL) based on the keyword text classification.
Methods/Statistical analysis: A semi-supervised technique, naive Bayes classification is proposed to locate malicious URL by text classification phenomena. The probabilities of the predicted and the exact values are calculated and it results with high probability. With more accuracy the malignant URL is predicted. A page rank algorithm is used to detect the blacklist which contains the URLs that are already noted as spam, malware or phishing URL.
Findings: With the persistent improvement of Web assaults, many web applications have been languishing from different types of security dangers and system assaults. The security identification of URLs has consistently been the focal point of Web security. One of the main sources of attacks is via malicious URLs; the attackers may send embedding executable codes or injects malicious codes through these URLs. Thus, it is important to improve the unwavering quality and security of web applications by precisely identifying malignant URLs. The utilization of profound figuring out how to group URLs to recognize Web guests' aims has significant hypothetical and scientific values for Web security investigate, giving new plans to canny security discovery.
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Training Datasets: https://www.kaggle.com/teseract/urldataset