IGO_CM: An Improved Grey-Wolf Optimization Based Classification Model for Cyber Crime Data Analysis Using Machine Learning

The internet utilization has been developing quickly, mostly in previous eras. Though, by way of the internet develops an important section of the daily life routine, cybercrime is similarly on the grow. The prices of cybercrime will approximately five lakh crores dollars per year through 2022 according to cyber security endeavours details in 2021. The cyber attackers exploit some internet resources as a principal way of transformation through a victim system; therefore intruders generate benefit based on economic, promotional and many more through developing the susceptibilities over devices. The computing cybercrime threats and providing security procedures through physical schemes utilizing previous methodological techniques and moreover examinations have unsuccessful many times to govern cybercrime threats. The previous literature in field of cybercrime threats agonizes from absence of evaluation schemes to guess the cybercrimes, mainly on unstructured information. Hence, an Improved Grey-wolf Optimization based Classification Model (IGO-CM) is developed with the help of chaos system and information entropy utilizing machine learning schemes for cybercrime data analysis to compute the rate of cybercrime by classifying the cybercrime data. The protection examinations by means of the relationship of data analysis methodologies provide services to examine and classify crime data in unstructured form taking from India. The implementation of IGO-CM is performed on MATLAB 2021a tool for unstructured cybercrime data and the outcomes describe the superior performance of IGO-CM depending on accuracy, F-Measure, standard deviation, purity index, intra-cluster distance, root mean square error, and time complexity against a popular classification scheme K-Means, and some optimization schemes like ACO, ALO, PSO and GO.