Intrusion Detection System using SMIFS and Multi class Multi layer Perceptron
V Maheshwar Reddy1, I Ravi Prakash Reddy2, K Adi Narayana Reddy3

1V Maheshwar Reddy, Assistant Professor ACE Engineering College, Telangana, India.
2I Ravi Prakash Reddy, Professor, G. Narayanamma Institute of Technology and Science, Telangana, India.
3K Adi Narayana Reddy, Professor, ACE Engineering College, Telangana, India.

Manuscript received on 30 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 2622-2628 | Volume-8 Issue-9, July 2019 | Retrieval Number: I8982078919/19©BEIESP | DOI: 10.35940/ijitee.I8982.078919
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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 (

Abstract: As the new technologies are emerging, data is getting generated in larger volumes high dimensions. The high dimensionality of data may rise to great challenge while classification. The presence of redundant features and noisy data degrades the performance of the model. So, it is necessary to extract the relevant features from given data set. Feature extraction is an important step in many machine learning algorithms. Many researchers have been attempted to extract the features. Among these different feature extraction methods, mutual information is widely used feature selection method because of its good quality of quantifying dependency among the features in classification problems. To cope with this issue, in this paper we proposed simplified mutual information based feature selection with less computational overhead. The selected feature subset is experimented with multilayered perceptron on KDD CUP 99 data set with 2- class classification, 5-class classification and 4-class classification. The accuracy is of these models almost similar with less number of features.
Keywords: IDS, Perceptron, Mutual Information, Entropy, Conditional Entropy, Feature Selection.

Scope of the Article: Assemblage and System