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Malicious url detection using Machine Learning

  • Unique Paper ID: 167646
  • Volume: 11
  • Issue: 4
  • PageNo: 15-19
  • Abstract:
  • In today's interconnected digital landscape, the rapid expansion of the internet has brought unprecedented convenience and accessibility to users worldwide. However, alongside these benefits, there exists a pervasive and ever-evolving threat: malicious Uniform Resource Locators (URLs). Cybersecurity is presented with a major challenge by these crafted URLs that are used in perpetrating phishing attacks, distributing malware and other forms of fraudulent activities. Still, this proliferation of malicious URLs poses a huge threat to cybersecurity because they are utilized for various forms of online scams including carrying out phishing attacks or distributing malware among others. Traditional methods for detecting such threats are overwhelmed as blacklists and rule-based systems have difficulties keeping up with the fast-evolving nature of such risks. The detection of malicious URL can be improved using machine learning. The use of machine learning is an excellent route in this field given its ability to process extensive data volumes while identifying subtle signs pointing to ill will. Such malintent can be easily differentiated from normal by training ML models on different features extracted from urls. Machine Learning (ML) models learn numerous features from various URLs which enable them to categorize benign and malicious ones with high precision. This method improves upon traditional methodologies, with higher detection rates and less false positive results. Our implementation involves collection of labelled URLs as a dataset, feature extraction and training several ML algorithms. Evaluations on the models are based on performance metrics such as precision, recall and F1 score The findings show that machine learning-based detection systems can efficiently identify malicious URLs to scale up cybersecurity defenses against changing internet threats.
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  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 4
  • PageNo: 15-19

Malicious url detection using Machine Learning

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