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Discovering the Common Traits of Cybercrimes in Pakistan Using Associative Classification with Ant Colony Optimization

Abdul Rauf1, Muhammad Asif Khan1,*, Hamid Hussain Awan2, Waseem Shahzad3, Najeeb Ul Husaan4

1 Department of Computer Science & IT, Sarhad University of Science & Information Technology, Peshawar, 25000, Pakistan
2 Faculty of Computer Science, IBADAT International University, Sihala, Islamabad, 45750, Pakistan
3 School of Computing, National University of Computer and Emerging Sciences, Islamabad, 44000, Pakistan
4 Department of Computer Science, Network Home Institute of Information Technology, Multan, 60700, Pakistan

* Corresponding Author: Muhammad Asif Khan. Email: email

Journal of Cyber Security 2022, 4(4), 201-222.


In the modern world, law enforcement authorities are facing challenges due to the advanced technology used by criminals to commit crimes. Criminals follow specific patterns to carry out their crimes, which can be identified using machine learning and swarm intelligence approaches. This article proposes the use of the Ant Colony Optimization algorithm to create an associative classification of crime data, which can reveal potential relationships between different features and crime types. The experiments conducted in this research show that this approach can discover various associations among the features of crime data and the specific patterns that major crime types depend on. This research can be beneficial in discovering the patterns leading to a specific class of crimes, allowing law enforcement agencies to take proactive measures to prevent them. Experimental results demonstrate that ACO-based associative classification model predicted 10 out of 16 crime types with 90% or more accuracy based on discovery of association among dataset features. Hence, the proposed approach is a viable tool for application in forensic and investigation of crimes.


Cite This Article

A. Rauf, M. A. Khan, H. H. Awan, W. Shahzad and N. U. Husaan, "Discovering the common traits of cybercrimes in pakistan using associative classification with ant colony optimization," Journal of Cyber Security, vol. 4, no.4, pp. 201–222, 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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