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Joined Bi-model RNN with spatial attention and GAN based IoT botnet attacks detection

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Abstract

Currently, for many purposes machine and deep learning methods are utilized to identify botnet threats in IoT networks. On the other hand, highly imbalanced network traffic information in the training set frequently humiliates the classification. A novel method is proposed called Joined Bi-Model RNN with Spatial Attention (JBiRSA). This work will be a pathway for many artificial intelligence researchers in cyber security. The introduced Joint Bi model of LSTM and GRU is combined with the local information gathering data as an added mode. The distinguished traffic flow features are fine-tuned with spatial attention. In this work, in order to solve the data class imbalance a GAN model is proposed with multi kernel De-Convolution with three level of skip connection. In this model GAN with Traffic Encoder loss is proposed along with an effective generator especially suited for network traffic. Two data sets are used, namely N-BaIoT and IoT-23. N-BaIoT data set is formed by adding botnet attacks such as Bashlite and Mirai. IoT-23 data set is formed with 20 malware confines from various IoT devices and 3 precincts for benign anomalies. JBiRSA with GAN has proven to be efficient and has the potential to differentiate between benign and malicious traffic data in IoT attacks. JBiRSA with GAN provides an overall accuracy of 98.75%.

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Data Availability

IoT-23 data set which was given by the Avast AIC laboratory [12] was used in this work.

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Authors and Affiliations

  1. AR College of Engineering and Technology, Tirunelveli, Tamil Nadu, India

    S Senthil

  2. FX Engineering College, Tirunelveli, Tamil Nadu, India

    N Muthukumaran

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  1. S Senthil
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Correspondence to S Senthil.

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Senthil, S., Muthukumaran, N. Joined Bi-model RNN with spatial attention and GAN based IoT botnet attacks detection. Sādhanā 48, 141 (2023). https://doi.org/10.1007/s12046-023-02188-y

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