Next Article in Journal
Advancing Applied Research in High Volume Transport in Low-Income Countries in Africa and South Asia
Next Article in Special Issue
Prevention and Fighting against Web Attacks through Anomaly Detection Technology. A Systematic Review
Previous Article in Journal
Building Performance Evaluation Using Coupled Simulation of EnergyPlus™ and an Occupant Behavior Model
Article

Computational System to Classify Cyber Crime Offenses using Machine Learning

1
Department of Computer Science, VR Siddhartha Engineering College, Vijayawada 520007, India
2
School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India
3
Raytheon Chair for Systems Engineering, Advanced Manufacturing Institute, King Saud University, Riyadh 11421, Saudi Arabia
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(10), 4087; https://doi.org/10.3390/su12104087
Received: 31 March 2020 / Revised: 10 May 2020 / Accepted: 14 May 2020 / Published: 16 May 2020
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. View Full-Text
Keywords: integrated cybercrimes; security analytics; machine learning approaches; supervised learning; classification; clustering; India integrated cybercrimes; security analytics; machine learning approaches; supervised learning; classification; clustering; India
Show Figures

Figure 1

MDPI and ACS Style

Ch, R.; Gadekallu, T.R.; Abidi, M.H.; Al-Ahmari, A. Computational System to Classify Cyber Crime Offenses using Machine Learning. Sustainability 2020, 12, 4087. https://doi.org/10.3390/su12104087

AMA Style

Ch R, Gadekallu TR, Abidi MH, Al-Ahmari A. Computational System to Classify Cyber Crime Offenses using Machine Learning. Sustainability. 2020; 12(10):4087. https://doi.org/10.3390/su12104087

Chicago/Turabian Style

Ch, Rupa, Thippa R. Gadekallu, Mustufa H. Abidi, and Abdulrahman Al-Ahmari. 2020. "Computational System to Classify Cyber Crime Offenses using Machine Learning" Sustainability 12, no. 10: 4087. https://doi.org/10.3390/su12104087

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop