A Distributed Denial-of-Service (DDoS) Attack Detection Approach in Fog Layer Based on Distributed Blockchain Database and Machine Learning
Subject Areas : Computer Engineering
Mohsen Eghbali
1
,
Mohammadreza Mollahoseini Ardakani
2
1 - Department of Computer Engineering, Maybod Branch, Islamic Azad University, Maybod, Iran
2 - Department of Computer Engineering, Maybod Branch, Islamic Azad University, Maybod, Iran
Keywords: The intrusion detection system, Fog layer, Machine learning, GAN neural network, Feature selection, Coati Optimization Algorithm (COA).,
Abstract :
DDoS attacks make network services unavailable to users by sending fake traffic by botnets. One of the methods to deal with DDoS attacks is to use machine learning, but these methods face challenges such as high volume of IoT traffic and data imbalance. This paper introduces a distributed intrusion detection system in the fog layer that detects network attack traffic in a decentralized manner. In this method, each fog node acts as an intrusion detection system, and by exchanging blacklists through the blockchain, they increase the secrecy of detecting attacks. Fog nodes identify the main features of network traffic using the Coati optimization algorithm and use these features to train a multilayer neural network in intrusion detection. The selection of features reduces traffic and increases the accuracy and speed of attack detection. Based on game theory, the GAN method is used to balance network traffic. Tests performed in the MATLAB and on the NSL-KDD show that the proposed system has accuracy, sensitivity, and precision of 98.67%, 98.52%, and 98.34%, respectively. This method is more accurate in identifying network attacks than feature selection methods such as WOA, GWO, and HHO and more accurate than LSTM and CNN.
Network traffic balancing in fog layer with game theory based on GAN network
Presenting a binary version of the Kuati optimization algorithm presented in 2023 for feature selection
Maintaining the confidentiality of the proposed intrusion detection system with blockchain and exchanging the blacklist with blockchain between fog nodes
Providing a distributed intrusion detection system in the fog layer to detect attacks on IoT
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