Novel Intrusion Detection Techniques for IOT Enabled Smart Cities

Authors

  • Ms.V. Sobana  PG Student, Department of CSE, VV College of Engineering, Tirunelveli, Tamil Nadu, India
  • Dr. P. Krishna Kumar  Professor, Department of CSE, VV College of Engineering, Tirunelveli, Tamil Nadu, India

Keywords:

Internet of Things, Intrusion Detection System, Multi-Layer Perceptron, Suport Vector Machine, Navie Bayes, Machine Learning

Abstract

Internet of Things for sustainable resource management critical to safeguard the future network infrastructure from intruders. With the growth of connected things, the most-widely used centralized (cloud-based) IDS often suffers from high latency and network overhead, thereby resulting in unresponsiveness to attacks and slow detection of malicious users. In this paper, the ML models to detect the various attacks accurately. To develop parallel machine-learning models corresponding to a partitioned attack dataset. In the distributed case, the parallel models individually perform both the feature selection and multi-layer perceptron classification. The effectiveness of the proposed architecture by using machine learning algorithms SVM and NB to achieve the high accuracy and lowest building time performance.

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International Journal of Scientific Research in Science and Technology

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Published

2021-04-10

Issue

Volume 9, Issue 1, March-April-2021

Section

Research Articles

License

Copyright (c) IJSRST

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Ms.V. Sobana, Dr. P. Krishna Kumar, " Novel Intrusion Detection Techniques for IOT Enabled Smart Cities , International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 9, Issue 1, pp.45-49, March-April-2021.