Performance Evaluation of Intrusion Detection and Mitigation
Title: TrustCentric: Honeynet based Intrusion Detection and Mitigation through Qualitative Risk Assessment using AI Technologies in IoT Empowered Drone Environment
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s/w req:
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1)NS-3.26
2)ubuntu-14.04 LTS(32 bit os)
Execution Steps:
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Open the ternimal (ctrl + alt + t)
change the directory on terminal.
First execute the files
change the image locations in the main files.
command to execute :
./waf --run filename --vis
Note:
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1) use our module else you get error.
2)Refer howtoaddour model.txt to addmodule
Implementation Plan:
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Step 1: We create a Network, it consists of 25 - IoT users,25-shallow drones (SD),5- big drones (BD), 1-ground control system (GCS),4- edge server, 1- cloud server , 1- auditing agent (AA) , 1- Trust Supremacy (TC) and 1- Blockchain Node.
Step 2: Initially, Register and authenticate the GCS, edge server and AA based on ID, MAC address and IP address, and also the IoT Users based on name, ID, mail ID and biometrics and key by Trust Supremacy (TC) and also the drones based on model ID, model type, timestamp, history of path planning, password, acoustic fingerprint and sound features.
step 3: Next, Perform the key generation based on Quantum Cryptography Protocol. All the credentials provided by entities, drones and IoT users are encrypted using quantum cryptography technique and stored in blockchain.
Step 4: Next, clustering the shallow drones (SD) and also the big drones (BD) act as Cluster Head.
Step 5: Next , the ground control system provides the Access control for the drones based on the tri-contract.
Step 6: Next, perform the trajectory path selection by using Chaos Game Optimization Algorithm (COA).
Step 7: Next, the drones sense the data and encrypt the data by using Hash based on Block Cipher (HBC-256) hashing algorithm. and also remove the duplicate data based on Advanced Median Filter (AMF) process and attack detection by using Deep Skip Connection Gated Recurrent Unit
(DS-GRU) and als classify the attacks as high, medium and low using Non-Cooperative Fuzzy Game Theory (NCFGT).
Step 8: Next, the AA performs the data auditing by using the Multi-agent Deep Deterministic Policy Gradient (Multi-DDPG) algorithm.
[The process based on your proposal :- TrustCentric: Honeynet based Intrusion Detection and Mitigation through Qualitative Risk Assessment using AI Technologies in IoT Empowered Drone Environment ]
Step 9: Finally,we plot the results graph for Number of Attacks vs Attack detection rate, Number of UAV vs Power consumption, Number of UAV vs Rate of unauthorized access, Number of UAV vs Authentication time, Rate of Communication threat vs Rate of Privacy strengthen and Number of Attacks vs Packet Loss.
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S/W Req:
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1) ns-3.26
2) ubuntu 14.04 [32 bit]
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Note:-
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1) We perform the EXISTING process based on the REFERENCE Title: - Intelligent Cyber-Security System for IoT-Aided Drones Using Voting Classifier
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