Intrusion Detection using Deep Learning Simulation
In this project, we create a network, it consists of 100 users 8 switches 4 controllers and 1 blockchain node. Initially , all users are registered to the blockchain for authentication by using the parameters ID, password, PUF and MAC address. After completed registration, blockchain provides the digital signature to the users using hybrid digital signature (HDS) algorithm. Next verify the switch flows and classify the switches into three classes such as normal, malicious and unknown by using gated switches into normal or malicious using quantum generative adversarial network. next based on these processes we detect the following attacks. Finally we plot the results graph for packet delivery ratio vs no of nodes , congestion rate vs no of packets, attack detection accuracy vs no of attackers, precision vs no of flows, re3call vs no of flows and link failure rate vs no of nodes.
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