Least Square Privacy Preserving Technique for Intrusion Detection System
Krishna Mandala1, Sobhan Babu Kappala2, Uma Shankar Rao Erothi3
1Krishna Mandala*, Dept of Mathematics, Raghu Institute of Technology, Visakhapatnam, India.
2Sobhan babu K, Dept of Mathematics, Jntuk, Ucen, Narsaraopet, India.
3Uma Shankar Rao Erothi, Dept of CSE, Raghu Institute of Technology, Visakhapatnam, India.
Manuscript received on November 14, 2019. | Revised Manuscript received on 24 November, 2019. | Manuscript published on December 10, 2019. | PP: 2312-2319 | Volume-9 Issue-2, December 2019. | Retrieval Number: B7447129219/2019©BEIESP | DOI: 10.35940/ijitee.B7447.129219
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Abstract: Network intrusion is a foremost growing concern threat in the cyberspace, which can be damage the network architecture in a multiple ways by modifying the system configuration/parameters. Hackers/Intruders are familiar with signature based intrusion detection models and they are making successful attempts to crash the networks. Hence, it is necessary to preserve user privacy on intrusion data. PPDM techniques forms a necessary but existing techniques such as Encryption, Perturbation, Data Transformation, Normalization, L-Diversity, K-Anonymity methods forms excessive generalization and suppression problems. In this paper, LSPPM distortion technique using Least Square Method with ensemble classification model have been proposed for providing efficient privacy preservation on intrusion data. The proposed methodology is validated on benchmark NSL_KDD intrusion dataset. A comparative analysis of NSL_KDD class attributes is performed for better classification in terms of accuracy, FAR, F-Score and time taken to build LSPPM-NIDS. The experimental results of state-of-art PPDM methods are also analyzed before and after distortion, and privacy measures to ascertain the degree of privacy offered.
Keywords: Network Intrusion Detection System, PPDM Techniques, Least Square Method, NSL_KDD, Ensemble classifier, Machine Learning
Scope of the Article: Machine Learning