@Article{jcs.2022.038791,
AUTHOR = {Abdul Rauf, Muhammad Asif Khan, Hamid Hussain Awan, Waseem Shahzad, Najeeb Ul Husaan},
TITLE = {Discovering the Common Traits of Cybercrimes in Pakistan Using Associative Classification with Ant Colony Optimization},
JOURNAL = {Journal of Cyber Security},
VOLUME = {4},
YEAR = {2022},
NUMBER = {4},
PAGES = {201--222},
URL = {http://www.techscience.com/JCS/v4n4/53753},
ISSN = {2579-0064},
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.},
DOI = {10.32604/jcs.2022.038791}
}