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Modified Deep Learning Methodology Based Malicious Intrusion Detection System in Software Defined Networking
Software Defined Networking (SDN) has increased a high-level attention in recent years, mainly because of its ability to address the cyber security challenges. Machine learning architectures were developed as the SDN system to detect the security threads; however, present techniques are limited with (i) higher computation time during malicious switch detection, (ii) reduced malicious switch detection rate (MSDR). This paper presents modified deep learning architecture based SDN system consist of two stages: (i) training stage, computes the external feature maps from both trusted and malicious network switches connected to the SDN controller, (ii) testing stage, classifying the trust and malicious switches connected with SDN controller. The feature maps are trained and classified with Modified LeNET Convolutional Neural Networks (CNN) architecture. The proposed methodology is simulated via network simulator under environmental constraint conditions. The results shows that the proposed methodology reduced the malicious switch detection computational time about a half as well as it increased the MSDR to about 6% compared to the conventional methodologies.
SDN, Switch, Malicious, CNN, Feature Maps.
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