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ANTON Framework Based on Semantic Focused Crawler to Support Web Crime Mining Using SVM

Author

Listed:
  • Javad Hosseinkhani

    (Islamic Azad University)

  • Hamed Taherdoost

    (Hamta Group
    Hamta Academy, Advanced Academic and Industrial Training Centre
    Tablokar Co, Switchgear Manufacturer)

  • Solmaz Keikhaee

    (Islamic Azad University)

Abstract

Crime analysis is one of the important activities in information security agencies. They collect the crimes data with appropriate procedures and tools from the Web. The main challenge which many of these agencies are facing is to have an efficient and accurate analysis of the increasing rate of crime information. The cybercrime information presented on Web pages are in the form of text and need to be analyzed and investigated. Although some approaches have been presented to support Web crime mining, the issues of efficiency and effectiveness still exist. Due to the fact that most of the crime information is based on Web ontology, semantic technology can be used to study the patterns and the process of Web crimes. Therefore, in order to extract and reveal the Internet crime, an improved Web ontology is useful to extract the characteristics and relationships among Web pages for the recreation and extraction of crime scenarios. The main purpose of this study is to develop an optimized ontology-based approach for Web crime mining. The proposed framework was designed based on enhanced crime ontology using ant-miner focused crawler, which drew inspiration from biological researches on the ant foraging behavior. Ant-colony optimization was used to optimize the proposed framework. The proposed work was evaluated based on accuracy criteria. The evaluation results show that this research provides an effective solution through crime ontologies and an enhanced ant-based crawler.

Suggested Citation

  • Javad Hosseinkhani & Hamed Taherdoost & Solmaz Keikhaee, 2021. "ANTON Framework Based on Semantic Focused Crawler to Support Web Crime Mining Using SVM," Annals of Data Science, Springer, vol. 8(2), pages 227-240, June.
    • Handle: RePEc:spr:aodasc:v:8:y:2021:i:2:d:10.1007_s40745-019-00208-5
    DOI: 10.1007/s40745-019-00208-5
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