Title: Network intrusion detection using fusion features and convolutional bidirectional recurrent neural network

Authors: H. Jagruthi; C. Kavitha; Manjunath Mulimani

Addresses: Department of Information Science and Engineering, BNM Institute of Technology, Bangalore, Karnataka, India ' Department of Computer Science and Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore, Karnataka, India ' Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India

Abstract: In this paper, novel fusion features to train Convolutional Bidirectional Recurrent Neural Network (CBRNN) are proposed for network intrusion detection. UNSW-NB15 data sets' attack behaviours (input features) are fused with their first and second-order derivatives at different stages to get fusion features. In this work, we have taken architectural advantage and combine both Convolutional Neural Network (CNN) and bidirectional Long Short-Term Memory (LSTM) as Recurrent Neural Network (RNN) to get CBRNN. The input features and their first and second-order derivatives are fused and considered as input to CNN and this fusion is known as early fusion. Outputs of the CNN layers are fused and used as input to the bidirectional LSTM, this fusion is known as late fusion. Results show that late fusion features are more suitable for intrusion detection and outperform the state-of-the-art approaches with average recognition accuracies of 98.00% and 91.50% for binary and multiclass classification configurations, respectively.

Keywords: intrusion detection; fusion features; convolutional neural network; bidirectional long short-term memory; convolutional bidirectional recurrent neural network; UNSW-NB15 data set.

DOI: 10.1504/IJCAT.2022.126095

International Journal of Computer Applications in Technology, 2022 Vol.69 No.1, pp.93 - 100

Received: 25 May 2021
Accepted: 02 Aug 2021
Published online: 11 Oct 2022 *

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