International Journal of Computer Networks and Applications

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Improving Performance and Efficiency of Software Defined Networking by Identifying Malicious Switches through Deep Learning Model


Affiliations
1 Department of Computer Science and Engineering, Annamalai University, Chidambaram, Tamil Nadu, India
2 Department of Computer Science and Engineering, King Khalid University, Abha, Saudi Arabia
 

In recent times, Software Defined Networking (SDN) has developed widely to provide capable solutions for future internet services. As with the solutions, SDN brings us a hazardous rise in malicious threats. We investigated a sort of Distributed Denial of Services (DDoS) assault known as an internet services attack, which evaluates the influence of both traffic flow and throughput depletions in order to characterize the abnormalities. This sort of attack has a significant impact on the whole SDN. This paper introduces a deep learning method to improve the performance efficiency of the SDN by classifying the network switch into either a trusted switch or a malicious switch device. In this research, an attack detection methodology for Internet services utilizing Software Defined Networking (SDN) is proposed. The SDN controller may evaluate traffic flow, detect anomalies, and restrict both incoming and outgoing traffic as well as source nodes. The SDN considers a Convolutional Neural Network (CNN) based attack detection system that can identify malicious node. Kaggle datasets are used to test and train CNN and the features such as packet duration, packet count, byte count, accuracy for identifying the flow of trusted and malicious switches. According to the results, the CNN-based attack detection system can identify the attack with an accuracy of 89 percent. The comparison evaluation with the already proposed LeNet CNN of the feature classification proves that the flow is the trusted one and with the constant throughput with the help of the deep learning model.

Keywords

Software Defined Networking (SDN), Kaggle Dataset, Convolutional Neural Networks (CNN), Keras, Internet Service Attack, Malicious Switches, Malicious Node, Distributed Denial of Services.
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  • Improving Performance and Efficiency of Software Defined Networking by Identifying Malicious Switches through Deep Learning Model

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Authors

Thangaraj Ethilu
Department of Computer Science and Engineering, Annamalai University, Chidambaram, Tamil Nadu, India
Abirami Sathappan
Department of Computer Science and Engineering, Annamalai University, Chidambaram, Tamil Nadu, India
Paul Rodrigues
Department of Computer Science and Engineering, King Khalid University, Abha, Saudi Arabia

Abstract


In recent times, Software Defined Networking (SDN) has developed widely to provide capable solutions for future internet services. As with the solutions, SDN brings us a hazardous rise in malicious threats. We investigated a sort of Distributed Denial of Services (DDoS) assault known as an internet services attack, which evaluates the influence of both traffic flow and throughput depletions in order to characterize the abnormalities. This sort of attack has a significant impact on the whole SDN. This paper introduces a deep learning method to improve the performance efficiency of the SDN by classifying the network switch into either a trusted switch or a malicious switch device. In this research, an attack detection methodology for Internet services utilizing Software Defined Networking (SDN) is proposed. The SDN controller may evaluate traffic flow, detect anomalies, and restrict both incoming and outgoing traffic as well as source nodes. The SDN considers a Convolutional Neural Network (CNN) based attack detection system that can identify malicious node. Kaggle datasets are used to test and train CNN and the features such as packet duration, packet count, byte count, accuracy for identifying the flow of trusted and malicious switches. According to the results, the CNN-based attack detection system can identify the attack with an accuracy of 89 percent. The comparison evaluation with the already proposed LeNet CNN of the feature classification proves that the flow is the trusted one and with the constant throughput with the help of the deep learning model.

Keywords


Software Defined Networking (SDN), Kaggle Dataset, Convolutional Neural Networks (CNN), Keras, Internet Service Attack, Malicious Switches, Malicious Node, Distributed Denial of Services.

References





DOI: https://doi.org/10.22247/ijcna%2F2022%2F211627