Open Access

ARTICLE

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

Abdul Rauf¹, Muhammad Asif Khan^1,*, 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:

Journal of Cyber Security 2022, 4(4), 201-222. https://doi.org/10.32604/jcs.2022.038791

Received 29 December 2022; Accepted 10 April 2023; Issue published 10 August 2023

Abstract

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.

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Keywords

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