Скачать или смотреть Performance Analysis of Black Hole Attack Detection in MANETs

*Title: HTC MANET Framework Implementation for Black Hole Attack Detection

Implementation Plan

Scenario 1: Baseline (No Attack)

Step 1: Initially, we constructed a network consisting of 50 Mobile Devices, 2 Base Stations, and 1 Server.

Step 2: Then, we simulate the network and collect simulated data such as packets sent, packets received, delay, throughput, and energy usage.

Step 3: Next, we preprocess the collected data using normalization and filtering to remove noise and ensure uniform scaling of parameters.

Step 4: Then, we implement the AODV routing protocol for optimal route discovery and detect packet transmission in the baseline network based on collected data.

Step 5: Finally, we plot performance metrics for the following

5.1: Number of Mobile Nodes Vs. Packet delivery Ratio (%)
5.2: Number of Mobile Nodes Vs. Detection Accuracy (%)
5.3: Number of Mobile Nodes Vs. Throughput (Mbps)
5.4: Number of Mobile Nodes Vs. End to End Delay (ms)
5.5: Number of Mobile Nodes Vs. False Positive Rate

Scenario 2: Under Black Hole Attack


Step 1: Initially, we constructed a network consisting of 50 Mobile Devices, 2 Base Stations, and 1 Server .

Step 2: Then, we simulate the network under a Black-Hole Attack by introducing malicious nodes and collecting simulated data such as packets sent, packets received, delay, throughput, and energy usage.

Step 3: Next, we preprocess the collected data using normalization and filtering to remove noise and ensure uniform scaling of parameters.

Step 4: Then, we implement the AODV routing protocol under attack conditions to observe routing misbehavior and packet drops caused by malicious nodes based on collected data.

Step 5: Finally, we plot performance metrics for the following

5.1: Number of Mobile Nodes Vs. Packet delivery Ratio (%)
5.2: Number of Mobile Nodes Vs. Detection Accuracy (%)
5.3: Number of Mobile Nodes Vs. Throughput (Mbps)
5.4: Number of Mobile Nodes Vs. End to End Delay (ms)
5.5: Number of Mobile Nodes Vs. False Positive Rate

Scenario 3: Proposed HTC-MANET Mitigation


Step 1: Initially, we constructed a network consisting of 50 Mobile Devices, 2 Base Stations, and 1 Server .

Step 2: Then, we simulate the network under a Black-Hole Attack and collect simulated data packets sent, packets received, delay, throughput, and energy usage

Step 3: Next, we preprocess the collected data using normalization and filtering to remove noise and ensure uniform scaling of parameters.

Step 4: Then, we implement the AODV routing protocol under attack conditions to observe routing misbehavior and packet drops caused by malicious nodes based on collected data..

Step 5: Next, we perform the HTC-MANET mitigation process by applying trust evaluation, authentication verification, and decision-based isolation of malicious nodes to enhance network performance and security based on collected data.

Step 6: Finally, we plot performance metrics for the following

6.1: Number of Mobile Nodes Vs. Packet delivery Ratio (%)
6.2: Number of Mobile Nodes Vs. Detection Accuracy (%)
6.3: Number of Mobile Nodes Vs. Throughput (Mbps)
6.4: Number of Mobile Nodes Vs. End to End Delay (ms)
6.5: Number of Mobile Nodes Vs. False Positive Rate

Software Requirement:

1. Development Tool: Ns-3.35 or above with python
2. Operating System: Ubuntu- 18.04 LTS or above


Note:
*****

1) If the proposed plan does not fully align with your requirements, please provide all necessary details—including steps, parameters, models, and expected outcomes—in advance. Kindly ensure that any missing configurations or specifications are clearly outlined in the plan before confirming.

2) If there's no built-in solution for what the project needs, we can always turn to reference models, customize our own, different math models or write the code ourselves to fulfil the process.

3) If the plan satisfies your requirement, Please confirm with us.

4) Project based on Simulation only.


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