Feature Reduction in MANET using Machine Learning Language
T.J. Nagalakshmi1, V. Veeramanikandan2, S. Ravichandran3
1T.J. Nagalakshmi, Assistant Professor, Department of ECE, Saveetha School of Engineering, Chennai (Tamil Nadu), India.
2V. Veeramanikandan, Department of ECE, Saveetha School of Engineering, Chennai (Tamil Nadu), India.
3Dr. S. Ravichandran, HOD & Professor, Department of Computer Science, Annai Fathima College of Arts & Science, Madurai (Tamil Nadu), India.
Manuscript received on 14 November 2019 | Revised Manuscript received on 22 December 2019 | Manuscript Published on 31 December 2019 | PP: 639-643 | Volume-9 Issue-2S4 December 2019 | Retrieval Number: B12311292S419/2019©BEIESP | DOI: 10.35940/ijitee.B1231.1292S419
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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: Mobile ad-hoc network (MANET) is an infrastructure-less network. Therefore, MANET involves a selection of exact security schemes to notice the false entrance of the mischievous nodes. Along these lines, we require solid instrument to identify these pernicious nodes and to arrange ordinary and irregular nodes based on the conduct or performance of nodes. Machine learning system nowadays used to built a best IDS for recognizing exception or misbehaving nodes rapidly and precisely give grouping by watching conduct of those nodes in the system. In MANET system, numbers of parameters are taken for analysation. It makes the IDS system complex. To avoid this complexity many techniques are derived for feature reduction. In this proposed work, we are testing how feature reduction can be done using Python machine learning program.
Keywords: Portable Impromptu System, Machine Learning Procedures, Bundle Dropping Assaults.
Scope of the Article: Machine Learning