Simulation and Analysis of Intrusion Detection & Prevention
Title:- vBlock: Securing SDN-IoT Environment by Novel Picture Placement based Authentication and Two Stage Intrusion Detection & Prevention Using AI Techniques
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Software Requirements:
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1) ns-3.26 ----------- 32-bit version
2) ubuntu 14.04----------- single primary os not vmware with 32 bit system
3) Tool: JDK-1.8
4) IDE: Netbeans-12.3
Execution Steps:-------------------
Open the ternimal (ctrl + alt + t)
change the directory on terminal.
First execute the files
command to execute :
./waf --run filename --vis
Note:-----
1) Use our module else you get error.
2) Refer howtoaddour model.txt to addmodule
For NetAnim:
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Copy the img1 folder under the Code folder and paste into these path -- /home/[your device name]/ns-allinone-3.26/netanim-3.107
Implementation Plan:
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Step 1: We create a Network, it consists of 100- IoT users, 4-Edge assisted switches, 2-controllers and 1- virtual blockchain node.
Step 2: Initially, register and authenticate the users by considered the credentials like user name, and bio metrics , also grid based image location and also the datas in the virtual blockchain.
Step 3: Next, In Edge assisted switch validate the authenticated user flows and also classify into three classes as normal, malicious, and
suspicious by using Trading based Evolutionary Game Theory (TEGT).
Step 4: Next, Based on the classification constructing the attack graph using Isomorphism based Graph Neural Network (IGNN).
Step 5: Next, Perform the multi controller placement based on link failure history and also distance between the controller and switches by using FOX optimization algorithm.
Step 6: Next, Incoming suspicious flow packets into two classes such as normal and malicious packets in the multi controllers by using Dueling Deep Q Network (DDQN) and also the generated global paths are securely stored in the virtual blockchain.
[The process based your proposal : vBlock: Securing SDN-IoT Environment by Novel Picture Placement based Authentication and Two Stage Intrusion Detection & Prevention Using AI Techniques ]
Step 7: Finally,we plot the results graph for No of IoT users vs Malicious Traffic,No. of Malicious Flows Vs Attack Detection Rate,User Flow Vs Link Failure Rate,No. of Packets Vs Anomaly Detection Rate and No. of Transactions Vs Scalability.
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S/W Req:
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1) ns-3.26
2) jdk-1.8
3) ubuntu 14.04 [32 bit]
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Note:-
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We perform the EXISTING process based on the REFERENCE Title: - Deep learning feature fusion approach for intrusion detection system in SDN based IoT networks
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