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Copyright © 2022 K. Veena et al. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

In the digital age, cybercrime is spreading its root widely. Internet evolution has turned out to a boon as well as curse for those confronting the issues of privacy, national security, social decency, IP rights, child protection, fighting, detecting, and prosecuting cybercrime. Hence, there arises a need to detect the cybercriminal. Cybercrime identification utilizes dataset that is taken from CBS open dataset. For identifying the cybercriminal, support vector machine (SVM) in the C SVM classification and K-nearest neighbor (KNN) models is utilized for determining the cybercrime information. The evaluation of the performance is done taking the following metrics into consideration: true positive, false positive, true negative and false negative, false alarm rate, detection rate, accuracy, recall, precision, specificity, sensitivity, classification rate, and Fowlkes-Mallows Scores. Expectation maximization (EM) calculation is utilized for evaluating the presentation of the Gaussian mixture model. The performance of classifier’s presentation is also done. Accuracy is accomplished in the event of grouping by means of SVM classifier as 89% in the supervised method.

Details

Title
C SVM Classification and KNN Techniques for Cyber Crime Detection
Author
Veena, K 1 ; Meena, K 2 ; Yuvaraja Teekaraman 3   VIAFID ORCID Logo  ; Kuppusamy, Ramya 4   VIAFID ORCID Logo  ; Radhakrishnan, Arun 5   VIAFID ORCID Logo 

 Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, 600119, Chennai, India 
 Department of Computer Science and Engineering, Institute of Aeronautical Engineering, Hyderabad, India 500043, 
 Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield S1 3JD, UK 
 Department of Electrical and Electronics Engineering, Sri Sairam College of Engineering, 562106, Bangalore City, India 
 Faculty of Electrical & Computer Engineering, Jimma Institute of Technology, Jimma University, Ethiopia 
Editor
Deepak Kumar Jain
Publication year
2022
Publication date
2022
Publisher
John Wiley & Sons, Inc.
e-ISSN
15308677
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2623773037
Copyright
Copyright © 2022 K. Veena et al. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.