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Abstract
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Recently, fog computing has been developed to complement cloud computing, which can provide cloud services
at the edge of the network with real-time processing. However, the computational power of fog nodes is limited
and this leads to security issues. On the other hand, cyber-attacks have become common with the exponential
growth of Internet of Things (IoT) connected devices. This fact necessitates the development of Intrusion
Detection Systems (IDSs) in fog environments with the aim of detecting attacks. In this paper, we develop an IDS
named GAN-LSTM for fog environments that uses Generative Adversarial Networks (GANs) and Long Short-Term
Memory Networks (LSTMs). GAN-LSTM is used to identify anomalies in network traffic to special types of attacks
or non-attacks. In general, GAN-LSTM consists of three components: data preprocessing, generation of real
traffic patterns, and sequence analysis of real traffic data. Data preprocessing ensures data quality by removing
noise and irrelevant features. The pre-processed data is fed to the GAN to generate real traffic as a baseline for
normal behavior. Finally, the LSTM component is applied to detect anomalous anomalies in fog computing. The
proposed algorithm was evaluated on public databases and experimental results showed that GAN-LSTM improves
the accuracy of attack detection compared to equivalent approaches.
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