Computational System to Classify Cyber Crime Offenses using Machine Learning

Rupa Ch, Thippa Reddy Gadekallu, Mustufa Haider Abidi and Abdulrahman Al-Ahmari
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Rupa Ch: Department of Computer Science, VR Siddhartha Engineering College, Vijayawada 520007, India
Thippa Reddy Gadekallu: School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India
Mustufa Haider Abidi: Raytheon Chair for Systems Engineering, Advanced Manufacturing Institute, King Saud University, Riyadh 11421, Saudi Arabia
Abdulrahman Al-Ahmari: Raytheon Chair for Systems Engineering, Advanced Manufacturing Institute, King Saud University, Riyadh 11421, Saudi Arabia

Sustainability, 2020, vol. 12, issue 10, 1-16

Abstract: Particularly in the last decade, Internet usage has been growing rapidly. However, as the Internet becomes a part of the day to day activities, cybercrime is also on the rise. Cybercrime will cost nearly $6 trillion per annum by 2021 as per the cybersecurity ventures report in 2020. For illegal activities, cybercriminals utilize any network computing devices as a primary means of communication with a victims’ devices, so attackers get profit in terms of finance, publicity and others by exploiting the vulnerabilities over the system. Cybercrimes are steadily increasing daily. Evaluating cybercrime attacks and providing protective measures by manual methods using existing technical approaches and also investigations has often failed to control cybercrime attacks. Existing literature in the area of cybercrime offenses suffers from a lack of a computation methods to predict cybercrime, especially on unstructured data. Therefore, this study proposes a flexible computational tool using machine learning techniques to analyze cybercrimes rate at a state wise in a country that helps to classify cybercrimes. Security analytics with the association of data analytic approaches help us for analyzing and classifying offenses from India-based integrated data that may be either structured or unstructured. The main strength of this work is testing analysis reports, which classify the offenses accurately with 99 percent accuracy.

Keywords: integrated cybercrimes; security analytics; machine learning approaches; supervised learning; classification; clustering; India (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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