DEVELOPMENT OF AN EFFECTIVE SYSTEM FOR DETECTING CYBERCRIMES USING MODIFIED RIPPLE DOWN RULE SYSTEM AND NEURAL NETWORK.
Cybercrime is an unlawful act in which computer is the tools to commit an offense; cyber criminals perform operation in cyber space with the help of the internet. Most existing techniques used in detecting cybercrimes could detect individual attacks but failed in terms of coordinated and distributed attacks. Also, most of the detection system used to curb cybercrimes on web application generates a large number of false alarms. Hence, this research developed an enhanced system which could not only detect individual, coordinated and distributed attacks but also reduce the number of false alarms. The research data for this work which consists of six cards (labeled A, B, C, D, E and F) were sourced from an online shopping store. The six cards contain four attributes with associated two thousand seven hundred (2700) transactions. The number of transactions carried out through each card were 200, 300, 400, 500, 600 and 700 respectively. Sixty percent of transactions carried out on each card were used to train the system while the remaining forty percent were used to test the system. The acquired attributes through each card were used as inputs in developing the system. Radial basis function was used for features extraction and the extracted features were moved to the Modified Ripple Down Rule engine that compared the profiling of the cardholder transaction information. The developed system was implemented on Matrix laboratory environment. The performance of the developed system was evaluated at 0.80 threshold using Sensitivity, Specificity, False Alarm Rate, Accuracy and Computational Time.
. J. O. Odumesi, “A socio-technological analysis of cybercrime and cyber security in Nigeria”., International Journal of Sociology and Anthropology, Vol.6, Issue 3, pp.116-125. 2014
. D. Sumanjit, and N. Tapaswini, “Impact of Cyber Crime: Issues and Challenges”. International Journal of Engineering Sciences & Emerging Technologies, Vol. 6, Issue 2, pp. 142-153, 2013
. P. Adeyemi, and A. Nkechi, “Research on Intrusion Detection and Response: A Survey”. International Journal of Network Security, Vol.2, pp. 84-102, 2016
. U. B. Steve, O. Diepreye and D. A. Uduak, “Information Communication Technologies in the Management of Education for Sustainable Development in Africa.” An International Multi-Disciplinary Journal, Ethiopia, Vol. 3, Issue 3, pp. 414-428, 2009
. O. Maitanmi, S. Ogunlere, S. Ayinde and Y. Adekunle. “Impact of Cyber Crimes on Nigerian Economy”. The International Journal Of Engineering And Science (IJES) Vol.2, Issue. 4, pp. 45-51, 2013
. A. Patcha and J .M. Park . “An overview of anomaly detection techniques: Existing solutions and latest technological trends”. Computer Networks,Vol. 51, pp. 3448-3470, 2007
. G. Gianini, M. Anisetti, V. Azzini, V. Bellandi, E. Damiani and S. Marrara. “ An Artificial Immune System approach to Anomaly Detection in Multimedia Ambient Intelligence“. 3rd IEEE International Conference on Digital Ecosystems and Technologies. Pp. 502 – 506, 2009
. O. O. Ogundile, “Fraud Analysis in Nigeria’s Mobile Telecommunication Industry”, International Journal of Scientific and Research Publications, Vol. 3, Issue 2, ISSN 2250- 3153, 2013
. M. A. Lebbe, J. I. Agbinya, Z. Chaczko and F. Chiang, “Self-Organized Classification of Dangers for Secure Wireless Mesh Networks”, Australasian Telecommunication Networks and Applications Conference. pp. 322 – 327, 2007
. A. Srivastava, Kundu.A, Sural.S and Maju-mdar. A.K, “Credit Card Fraud Detection Using Hidden Markov Model”, IEEE Transactions on Dependable & Secure Computing Vol. 5, 2008.
. S. Panigrahi, A. Kundun, S. Sural and A.K. Majum-dar, “Credit card fraud detection: A fusion approach using Dempster–Shafer theory and Bayesian learning” Science Direct, pp. 354-363, 2009.
. E. Duman and H.M. Ozcelik., “Detecting credit card fraud by genetic algorithm and scatter search”, Science Direct, Expert System with Applications. Vol.38 , pp 13057- 13063, 2011
. H. Farvaresh and M.M. Sepehri, “A data mining framework for detecting subscription fraud in telecommunication”, Science Direct, Engineering Applications of Artificial Intelligence. Vol.24, pp. 182-194, 2010.
. K. Kim, Y. Choi and J. Park. “Pricing fraud detection in online shopping malls using a finite mixture model”. Science Direct Electronic Commerce Research and Applications, pp. 195-207, 2013
. M. A. Lebbe, J. I. Agbinya, Z. Chaczko, and F. Chiang . “Self-Organized Classification of Dangers for Secure Wireless Mesh Networks”, Australasian Telecommunication Networks and Applications Conference. Ppp. 322 – 327, 2007
. S. Wu and S. Wang, “Information-Theoretic Outlier Detection for Large-Scale Categorical Data”, IEEE Vol. 25 Issue..3, 2013
. Y. Sahin, S. Bulkan.and E. Duman., “A cost-sensitive decision tree approach for fraud detection”, Science Direct, Expert System with Applications. Vol. 40, pp-5916- 5923, 2013.
. A. Zhang, C. Chen and H. Karimi. “A new adaptive LSSVR with on-line multikernel RBF tuning to evaluate analog circuit performance”. Abstract and Applied Analysis.Vol. 20, pp. 1-7. Article ID 231735, 2013
. V.Mareeswari, Dr G. Gunasekaran, “Prevention of Credit Card Fraud Detection based on HSVM”, International Conference on Information Communication and Embedded System 2016.
. S. M. Jaba, P. Soumyashree, and K. M. Ashis, “A Novel Approach for Credit Card Fraud Detection Targeting the Indian Market”, IJCSI International Journal of Computer Science Issues, Vol. 10, Issue 3, No 2, 2013.
. IC. Yeh, and C. Lien, “The comparisons of data mining techniques for the predictive accuracy of probability of defualt of credit card client,” Expert Syetem with Applications Vol. 36, pp.2473-2480, 2008
Copyright (c) 2023 IJRDO -Journal of Computer Science Engineering
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Author(s) and co-author(s) jointly and severally represent and warrant that the Article is original with the author(s) and does not infringe any copyright or violate any other right of any third parties, and that the Article has not been published elsewhere. Author(s) agree to the terms that the IJRDO Journal will have the full right to remove the published article on any misconduct found in the published article.