Analysis of Cyber Attack Detection Mitigation in IIoT WSN

Performance Analysis of Cyber Attack Detection and Mitigation in IIoT Wireless Sensor Networks

Implementation plan:

Scenario 1: IEEE 802.15.4 Static (Grid-Based) Topology

Step 1: Initially, we constructed a environment with 40 static IIOT grid-based nodes configured using IEEE 802.15.4 with RPL routing and CSMA/CA MAC protocol.

Step 2: Then, we simulate the network under the following conditions: Normal, Blackhole Attack, Wormhole Attack, Sybil Attack, Backoff Attack, and Flooding Attack.

Step 3: Next, we collect and store the Packet Delivery Ratio (%), End-to-End Delay (ms), Energy Consumption (J), and Throughput (bps) data of all conditions.

Step 4: Next, we plot the performance for the following metrics for all conditions:( Normal, Blackhole Attack, Wormhole Attack, Sybil Attack, Backoff Attack, and Flooding Attack)

4.1: Number of static nodes vs. Packet Delivery Ratio (%)

4.2: Number of static nodes vs. End-to-End Delay (ms)

4.3: Number of static nodes vs. Throughput (bps)

4.4: Number of static nodes vs. Energy Consumption (J)

Step 5: Finally, we apply a lightweight ML Decision Tree algorithm to detect and mitigate the attacks based on the collected data.

Scenario 2: IEEE 802.15.4 Mobile (MANET) Topology

Step 1: Initially, we constructed a WSN environment with 40 mobile nodes configured using IEEE 802.15.4 with AODV routing and CSMA/CA MAC protocol.

Step 2: Then, we simulate the network under the following conditions: Normal, Blackhole Attack, Wormhole Attack, Sybil Attack, Backoff Attack, and Flooding Attack.

Step 3: Next, we collect and store the Packet Delivery Ratio (%), End-to-End Delay (ms), Energy Consumption (J), and Throughput (bps) data of all conditions.

Step 4: Next, we plot the performance for the following metrics for all conditions:( Normal, Blackhole Attack, Wormhole Attack, Sybil Attack, Backoff Attack, and Flooding Attack)

4.1: Number of mobile nodes vs. Packet Delivery Ratio (%)

4.2: Number of mobile nodes vs. End-to-End Delay (ms)

4.3: Number of mobile nodes vs. Throughput (bps)

4.4: Number of mobile nodes vs. Energy Consumption (J)

Step 5: Finally, we apply a lightweight ML Decision Tree algorithm to detect and mitigate the attacks based on the collected data.

Scenario 3: LoRaWAN Static Topology

Step 1: Initially, we constructed a WSN environment with 40 static LoRaWAN nodes connected to 2 gateways and a central server

Step 2: Then, we simulate the network for 12000 ms under the following conditions: Normal, Sybil Attack, and Flooding Attack.

Step 3: Next, we collect and store the Packet Delivery Ratio (%), End-to-End Delay (ms), Energy Consumption (J), and Throughput (bps) data of all conditions.

Step 4: Next, we plot the performance for the following metrics for all conditions:( Normal, Blackhole Attack, Wormhole Attack, Sybil Attack, Backoff Attack, and Flooding Attack)

4.1: Number of static nodes vs. Packet Delivery Ratio (%)

4.2: Number of static nodes vs. End-to-End Delay (ms)

4.3: Number of static nodes vs. Throughput (bps)

4.4: Number of static nodes vs. Energy Consumption (J)

Step 5: Finally, we apply a lightweight ML Decision Tree algorithm to detect and mitigate the attacks based on the collected data.

Software Requirement:

1. Development Tool: OMNET++ 6.0 or above
2. Operating System: Windows- 10 (64-bit) or above

Note:

1. We make a simulation based process only, not a real time process.

2. If the above plan does not satisfy your requirement, please provide the processing details, like the above step-by-step.

3. Please note that this implementation plan does not include any further steps after it is put into implementation.

4. If the above plan satisfies your requirement, please confirm us soon.

Related Pages