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Genetic Algorithm Assisted Feature Selection For Secure IOT-Based URL Classification

  • Unique Paper ID: 175606
  • Volume: 11
  • Issue: 11
  • PageNo: 3814-3822
  • Abstract:
  • With the increasing reliance on digital platforms, malicious and phishing URLs have become a significant cybersecurity threat. Cybercriminals use deceptive links to steal personal information, distribute malware, and carry out fraudulent activities. Traditional detection methods struggle to identify these threats effectively due to the large volume of online data and the evolving nature of phishing attacks. As a result, there is a need for a more efficient and intelligent approach to detect and prevent such harmful URLs. This study proposes an advanced hybrid feature selection technique that enhances the accuracy of classifying URLs as either safe or suspicious by filtering out irrelevant data and improving detection performance. The proposed system extracts key features from URLs, including lexical patterns, domain-based attributes, and webpage content-related characteristics. It then applies a hybrid feature selection method that combines multiple filtering and wrapper-based approaches to identify the most significant features for classification. By integrating machine learning algorithms such as Support Vector Machines (SVM), Decision Trees, and Neural Networks, the system efficiently classifies URLs while reducing false positives. This approach not only improves detection accuracy but also speeds up the processing of large-scale URL data, making it highly effective for real-time cybersecurity applications. Experimental results show that the hybrid feature selection technique significantly outperforms traditional methods, offering higher accuracy, reduced computational complexity, and better adaptability to new phishing tactics. By implementing this system, organizations and individuals can enhance their cybersecurity defenses and mitigate risks associated with malicious URLs. This research contributes to the development of a fast, scalable, and reliable detection mechanism, ensuring a safer browsing experience and protecting users from cyber threats.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 11
  • PageNo: 3814-3822

Genetic Algorithm Assisted Feature Selection For Secure IOT-Based URL Classification

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Impact Factor
8.01 (Year 2024)
UGC Approved
Journal no 47859

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