Detecting Intrusion with High Accuracy: Using Hybrid K-Multi Layer Perceptron
Amit Dogra1, Taqdir2
1Amit Dogra, Assistant Professor, Department of Computer Science and Engineering at SoET, BGSB University Rajouri (J&K).
2Dr. Taqdir, Assistant Professor, Department of Computer Science and Engineering, Guru Nanak Dev University, R/C Gurdaspur.
Manuscript received on 11 August 2019. | Revised Manuscript received on 16 August 2019. | Manuscript published on 30 September 2019. | PP: 4994-4999 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5645098319/2019©BEIESP | DOI: 10.35940/ijrte.C5645.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: TThe intrusion detection is the mechanism by which abnormality from the state driven dataset is discovered. The intrusion causes the problem of false discovery that mislead overall result. The resources from server may not be accessed by the use of intrusion be malicious users. The propose mechanism of Self organizing KMLP technique to discover abnormal patterns from the dataset. The dataset is synthetically derived to demonstrate the experimental work. The operation is demonstrated against K-Map clustering. The result is presented in terms of classification accuracy, number of attacks and execution time and result shows significant improvement by the margin of 10%.
Keywords: Intrusion Detection, Machine Learning , KMLP
Scope of the Article: Artificial Intelligence and Machine Learning