Banking Intrusion Detection Systems based on customers behavior using Machine Learning algorithms: Comprehensive study
DOI:
https://doi.org/10.29304/jqcm.2020.12.4.711Keywords:
Network Intrusion Detection, Machine Learning algorithms, Anomaly DetectionAbstract
In recent years, The computer networks has tremendous growth and the Internet usage became essential in many fields in real life, combined with the huge amount of data transmitted over networks, generated an exponential increase in the amount of malicious and ambiguous threats to computer networks. By implementation of Machine Learning (ML) algorithms to protect computer networks and to overcome network security breaches. Many approaches appeared to the surface to achieve that purpose, one of them is the Network Intrusion Detection System (NIDS). This research aims to present a comprehensive study about employing machine learning algorithms because of what they have of effective and productive characteristics and capabilities when used in the area of tracking user behavior embedded to construct a framework simulates a banking system and examines the deviation of customer's normal utilization of the system, that is called intrusion. The experimental results obtained by this research that involves combining four algorithms in one framework showed high accuracy and low false alarm detection rate.
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References
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[2] Chih-Fong Tsai, Yu-Feng Hsu , Chia-Ying Lin , Wei-Yang Lin, "Intrusion detection by machine learning: A review," Expert Systems with Applications 36 ,2009.
[3] 2013Zakiyabanu S. Malek, and Bhushan Trivedi "User Behaviour based Intrusion Detection System Overview", International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2018.
[4] Nutan F. H. , Musharrat R. , Abdur R. O. , Faisal M. S. , Md. Avishek K. H. , and Dewan Md. F. "Application of Machine Learning Approaches in Intrusion Detection System: A Survey", International Journal of Advanced Research in Artificial Intelligence, Vol. 4, No.3, 2015.
[5] Guojun Z., Liping C. and Weitao H., “The Design of Cooperative Intrusion Detection System”, IEEE Computer Society, Seventh International Conference on Computational Intelligence and Security, 2011.
[6] K.Ho Law, and L.For Kwok, "IDS False Alarm Filtering Using KNN Classifier", Springer-Verlag Berlin Heidelberg, 2004.
[7] A. K. Ghosh, A. Schwartzbard and A. M. Shatz, “Learning Program Behavior Profiles for Intrusion Detection”, Proceedings of 1st USENIX Workshop on Intrusion Detection and Network Monitoring, Santa Clara, 1999.
[8] S. Andropov, A. Guirik, M. Budko, and M. Budko, “Machine learning: a review of classification and combining techniques” Springer Science+Business Media B.V., 2007.
[9] Zakiyabanu S. Malek, and Bhushan Trivedi "User Behaviour based Intrusion Detection System Overview", International Journal for Research in Applied Science & Engineering Technology, 2018.
[10] S. B. Kotsiantis , I. D. Zaharakis , P. E. Pintelas “Machine learning: a review of classification and combining techniques” Springer Science+Business Media B.V., 2007.
[11] Dewan M., Farid N., H. Emna B., Mohammad Z. R., Chowdhury M. R., "Attacks Classification in Adaptive Intrusion Detection using Decision Tree", World Academy of Science, Engineering and Technology , 2010 .
[12] Dewan M., Farid N., H. Emna B., Mohammad Z. R., Chowdhury M. R., "Attacks Classification in Adaptive Intrusion Detection using Decision Tree", World Academy of Science, Engineering and Technology , 2010 .
[13] Hafsa A. M., " Modified Hidden Naïve Bayes Classifier to Enhance Intrusion Detection system," thesis submitted to Department of Computer Sciences of University of Technology in partial fulfilment of requirements for the degree of Master of Science in Computer Science, 2016.
[14] S.V. Lakshmi, and T.E Prabakaran, "Application of k-Nearest Neighbour Classification Method for Intrusion Detection in Network Data", International Journal of Computer Applications (0975 – 8887), 2014.
[15] B.Basaveswara , and K.Swathi, "Fast kNN Classifiers for Network Intrusion Detection System", Indian Journal of Science and Technology, Vol 10(14), 2017.
[16] Wenchao Li, Ping Yi, Yue Wu, Li Pan, and Jianhua Li, "A New Intrusion Detection System Based on KNN Classification Algorithm in Wireless Sensor Network", Journal of Electrical and Computer Engineering , 2014.
[17] I.N. da Silva et al., "Artificial Neural Networks", Springer International Publishing, 2017.
[18] Sufyan T. Faraj Al-Janabi and Hadeel Amjed Saeed," A Neural Network Based Anomaly Intrusion Detection System", IEEE, 2011.
[19] S. B. Kotsiantis , I. D. Zaharakis , and P. E. Pintelas," Machine learning: a review of classification and combining techniques", Springer, 2007.
[20] J Zou , Y Han , and Sung-Sau So," Artificial Neural Networks Methods and Applications", Springer, 2008.
[21] Samuel J. and Karol W., "Machine learning algorithms in a distributed context", Bachelor thesis submitted to the Department of Computer and Information Science of Linköping University, 2018.
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Published
2020-11-19
How to Cite
Mahdi, W. S., & Maolood, A. T. (2020). Banking Intrusion Detection Systems based on customers behavior using Machine Learning algorithms: Comprehensive study. Journal of Al-Qadisiyah for Computer Science and Mathematics, 12(4), Comp Page 1 – 11. https://doi.org/10.29304/jqcm.2020.12.4.711
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Section
Computer Articles
