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Malicious URL Detection Based on Associative Classification.

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Summary
This summary is machine-generated.

This study introduces a data mining method, classification based on association (CBA), to detect malicious URLs. The CBA algorithm achieved 95.8% accuracy, offering a robust defense against web-based malware distribution.

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Area of Science:

  • Cybersecurity
  • Data Mining
  • Machine Learning

Background:

  • Malicious URLs are primary channels for malware propagation.
  • Exploiting browser vulnerabilities allows remote access and control.
  • Malware aims to infiltrate networks, steal data, and conduct surveillance.

Purpose of the Study:

  • To present a data mining approach, classification based on association (CBA), for detecting malicious URLs.
  • To utilize both URL and webpage content features for enhanced detection accuracy.

Main Methods:

  • Employed the classification based on association (CBA) algorithm.
  • Utilized a training dataset of URLs for historical data analysis.
  • Discovered association rules to construct an accurate URL classification model.

Main Results:

  • Achieved a high accuracy rate of 95.8% in detecting malicious URLs.
  • Demonstrated comparable performance against established benchmark classification algorithms.
  • Maintained low false positive and false negative rates, indicating reliability.

Conclusions:

  • The CBA algorithm is effective for malicious URL detection.
  • This data mining approach offers a viable solution for combating web-based malware threats.
  • The method provides accurate and reliable detection with minimal errors.