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Modeling and Predicting Cyber Hacking Breaches

  • Unique Paper ID: 177074
  • Volume: 11
  • Issue: 12
  • PageNo: 3670-3674
  • Abstract:
  • The paper offers a practical method for detecting and classifying malicious URLs using a machine learning model that utilizes the Support Vector Machine (SVM) algorithm. The system determines whether URLs are malicious or safe by analyzing a dataset that contains known malicious indicators associated with different types of cyberattacks, including phishing, man-in-the-middle, and SQL injection. Real-world flag data is used to support the research and it was tested on a diverse set of URLs. By storing and analyzing the results, statistical insights are gained, which allows for visualization of attack distributions. By offering an automated, data-driven framework, the study helps cybersecurity research by identifying and mitigating web-based threats.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 12
  • PageNo: 3670-3674

Modeling and Predicting Cyber Hacking Breaches

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