Published Paper Details:

Identifying security dangers and confirming the legitimacy of URLs are the main topics of this study paper, which focuses on phishing and malware. By utilizing the potent SVM algorithm, we put forth a sturdy approach for identifying and categorizing harmful URLs linked to malware infections and phishing scams. Our methodology consists of analysing general features included in malware and phishing URLs that are taken from large datasets that are made available by reliable sources. Designed for end-host deployment, the SVM algorithm shows its effectiveness in identifying malware URLs and phishing URLs, both known and unknown. Our solution provides precise and timely detection of URLs by examining their subtle properties, which enables proactive mitigation of cyber threats. Our methodology's efficacy is demonstrated through experimental validation, exhibiting a low rate of false positives and a high detection accuracy. By offering a sophisticated and effective method for recognizing and differentiating between security risks and authentic URLs, this research advances cybersecurity. In this case, the use of SVM is a major step in strengthening the resistance of digital ecosystems against malware and phishing attempts.