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A deep learning approach for effective intrusion detection in wireless networks using CNN

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Abstract

Security is playing a major role in this Internet world due to the rapid growth of Internet users. The various intrusion detection systems were developed by many researchers in the past to identify and detect the intruders using data mining techniques. However, the existing systems are not able to achieve sufficient detection accuracy when using the data mining. For this purpose, we propose a new intrusion detection system to provide security in data communication by identifying and detecting the intruders effectively in wireless networks. Here, we propose a new feature selection algorithm called conditional random field and linear correlation coefficient-based feature selection algorithm to select the most contributed features and classify them using the existing convolutional neural network. The experiments have been conducted for evaluating the proposed intrusion detection system that achieves 98.88% as overall detection accuracy. The tenfold cross-validation has been done for evaluating the performance of the proposed model.

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Authors and Affiliations

  1. School of Computing Science and Engineering, Vellore Institute of Technology, Chennai, Tamilnadu, India

    B. Riyaz & Sannasi Ganapathy

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  1. B. Riyaz
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  2. Sannasi Ganapathy
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Correspondence to B. Riyaz.

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Communicated by V. Loia.

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Riyaz, B., Ganapathy, S. A deep learning approach for effective intrusion detection in wireless networks using CNN. Soft Comput 24, 17265–17278 (2020). https://doi.org/10.1007/s00500-020-05017-0

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