Advertisement

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

  • Javad Hosseinkhani
  • Hamed TaherdoostEmail author
  • Solmaz Keikhaee
Article
  • 5 Downloads

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.

Keywords

ANTON framework Semantic focused crawler Web crime mining Naïve/Bayes SVM 

Notes

References

  1. 1.
    Dorigo M, Birattari M (2011) Ant colony optimization. Encyclopedia of machine learning. Springer, Boston, pp 36–39Google Scholar
  2. 2.
    Hosseinkhani J, Ibrahim S, Chuprat S, Naniz JH (2014) Web crime mining by means of data mining techniques. Res J Appl Sci Eng Technol 7(10):2027–2032CrossRefGoogle Scholar
  3. 3.
    Hosseinkhani J, Chaprut S, Taherdoost H (2012) Criminal network mining by Web structure and content mining. In: 11th WSEAS international conference on information security and privacy, Prague, Czech Republic, pp 24–26Google Scholar
  4. 4.
    Karthika S, Bose S (2011) A comparative study of social networking approaches in identifying the covert nodes. Int J Web Serv Comput 2:65–78CrossRefGoogle Scholar
  5. 5.
    Hosseinkhani J, Chuprat S, Taherdoost H (2012) Discovering criminal networks by Web structure mining. In: 7th International conference on computing and convergence technology, pp 1074–1079Google Scholar
  6. 6.
    Iqbal F, Fung B, Debbabi M (2012) Mining criminal networks from chat log. In: International joint conferences on Web intelligence and intelligent agent technology, vol 01, pp 332–337Google Scholar
  7. 7.
    Matsokis A, Kiritsis D (2010) An ontology-based approach for product lifecycle management. Comput Ind 61(8):787–797CrossRefGoogle Scholar
  8. 8.
    Liu Y, Zhang D, Lu G, Ma WY (2007) A survey of content-based image retrieval with high-level semantics. Pattern Recognit 40(1):262–282CrossRefGoogle Scholar
  9. 9.
    Keyvanpour MR, Javideh M, Ebrahimi MR (2011) Detecting and investigating crime by means of data mining: a general crime matching framework. Procedia Comput Sci 3:872–880CrossRefGoogle Scholar
  10. 10.
    Phua C, Lee V, Smith K, Gayler R (2010) A comprehensive survey of data mining-based fraud detection research. arXiv 1–14Google Scholar
  11. 11.
    Al-Zaidy R, Benjamin CMF, Youssef AM, Fortin F (2012) Mining criminal networks from unstructured text documents. Digit Investig 8(3–4):147–160CrossRefGoogle Scholar
  12. 12.
    Tayal DK, Jain A, Arora S, Agarwal S, Gupta T, Tyagi N (2015) Crime detection and criminal identification in India using data mining techniques. AI Soc 30(1):117–127CrossRefGoogle Scholar
  13. 13.
    Hosseinkhani J, Chuprat S, Taherdoost H, Moghaddam AS (2012) Propose a framework for criminal mining by Web structure and content mining. Int J Adv Comput Sci Inf Technol 1(1):1–13Google Scholar
  14. 14.
    Sivagaminathan RK, Ramakrishnan S (2007) A hybrid approach for feature subset selection using neural networks and ant colony optimization. Expert Syst Appl 33(1):49–60CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer, Damavand BranchIslamic Azad UniversityDamavandIran
  2. 2.Research Club, Research and Development DepartmentHamta GroupKuala LumpurMalaysia
  3. 3.Hamta Academy, Advanced Academic and Industrial Training CentreKuala LumpurMalaysia
  4. 4.Tablokar Co, Switchgear ManufacturerTehranIran

Personalised recommendations