Abstract
The popularity of network technology and the threats involved make it imperative to develop various techniques to effectively detect fraudulent activities. The focus of this paper is to discover actionable irregularity with significant potential to perceive abnormal network behavior. Three machine learning-based methods have been proposed for the analysis of suspicious network behavior. These methods are extensions of the techniques discussed as part of the introduction below. The method developed in the current work can be evaluated by testing its efficiency against real-time network attacks using available open-source network tools. Experimental results show that irregularities have been successfully identified from the dataset with a low false positive rate. Furthermore, we believe our method can be directly deployed in real-time environments (either independently of edge devices or via the cloud) to strengthen network security.
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References
Chiba, D., Tobe, K., Mori, T., Goto, S.: Detecting malicious websites by learning IP address features. In: 2012 IEEE/IPSJ 12th international symposium on applications and the internet, pp. 29–39 (2012). https://doi.org/10.1109/SAINT.2012.14
Ke, Q.: Research on threat detection in cyber security based on machine ... itopscience. Retrieved December 20, 2022, from https://doi.org/10.1088/1742-6596/2113/1/012074
Minnesota, Y.J.U., et al.: Identifying and tracking suspicious activities through IP Gray Space Analysis. In: Proceedings of the 3rd annual ACM workshop on mining network data, ACM conferences. Available at https://dl.acm.org/doi/10.1145/1269880.1269883
Yan, G., Li, Q., Guo, D., Li, B.: AULD: Large scale suspicious dns activities detection via unsupervised learning in advanced persistent threats. Sensors (Basel). 19(14), 3180 (2019). https://doi.org/10.3390/s19143180.PMID:31330986;PMCID:PMC6679267
Manocha, S., Girolami, M.: An empirical analysis of the probabilistic K-nearest Neighbor Classifier. Pattern Recognit. Lett., 28: 1818–1824
Machine learning techniques for anomaly detection: An overview. Available at https://www.researchgate.net/publication/325049804_Machine_Learning_Techniques_for_Anomaly_Detection_An_Overview
Sharma, R.K., Kalita, H.K., Borah, P.: Analysis of machine learning techniques based intrusion detection systems. In: Nagar, A., Mohapatra, D., Chaki, N. (eds.) Proceedings of 3rd international conference on advanced computing, networking and informatics. smart innovation, systems and technologies, vol. 44. Springer, New Delhi (2016). https://doi.org/10.1007/978-81-322-2529-4_51
Mabu, S., Chen, C., Lu, N., Shimada, K., Hirasawa, K.: An intrusion-detection model based on fuzzy class-association-rule mining using genetic network programming. In: IEEE transactions on systems, man, and cybernetics, Part C (Applications and Reviews), vol. 41, no. 1. pp. 130-139 (2011). https://doi.org/10.1109/TSMCC.2010.2050685
Rouhi, R., Keynia, F., Amiri, M.: Improving the intrusion detection systems’ performance by correlation as a sample selection method. J Comput Sci Applications. 1(3), 33–38 (2013). https://doi.org/10.12691/jcsa-1-3-1
Hajji, H.: Statistical analysis of network traffic for adaptive faults detection. In: 2005 IEEE trans neural networks, vol 16, no 5. pp 1053–1063 (2005)
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Singh, P., Razak, A. (2024). Fraudulent IP Address Detection Using Machine Learning Techniques. In: So In, C., Londhe, N.D., Bhatt, N., Kitsing, M. (eds) Information Systems for Intelligent Systems. ISBM 2023. Smart Innovation, Systems and Technologies, vol 379. Springer, Singapore. https://doi.org/10.1007/978-981-99-8612-5_23
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DOI: https://doi.org/10.1007/978-981-99-8612-5_23
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