Performance Analysis of DDoS Attack Detection and Mitigation in a Smart City Environment
Implementation Plan:
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Step 1: Initially, we collect and load data from DDoS attack detection dataset
Step 2: Then, we preprocess the collected data using Local Outlier Factor (LOF) and Equal Time Step Sliding Window method.
Step 3: Next, we perform a feature extraction process using Deep Autoencoders with Gradient Boosting Machines (GBM) technique.
Step 4: Then, we train the data using Cyclical Learning Rates (CLR) with Adam Optimization Algorithm to improve speed and accuracy.
Step 5: Next, we filter the unwanted harmful updates using SecureMeta-RFA Aggregation and Backdoor detection with Feedback-based FL (BAFFLE)
Step 6: Next, we detect DDoS attacks using the Federated LSTM networks with Random Forest classifiers (FedLSTM-RF) OptimizerNet algorithm.
Step 7: Then, we mitigate the DDOS attack using the Sparse DRL-Enhanced Extreme Learning Machine (SD-ELM) algorithm.
Step 8: Finally, we plot performance metrics for the following:
8.1: Number of Epochs Vs. Accuracy (%)
8.2:Number of Epochs Vs. Precision (%)
8.3: Number of Epochs Vs. Recall (%)
8.4:Number of Epochs Vs. F1-Score (%)
8.5: Number of Epochs Vs. Response time
8.6:Number of Epochs Vs. Learning rate
Software Requirements:
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1. Development Tool: Python 3.11.9
2. Operating System: Windows-10 (64-bit) or above
Dataset:
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Link : https://www.kaggle.com/datasets/himadri07/ciciot2023
Note:
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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.
We perform with an Existing Approach Ref 1- Title: Federated Learning for Decentralized DDoS Attack Detection in IoT Networks
Existing approach CICIDS2017 dataset link: https://www.kaggle.com/datasets/chethuhn/network-intrusion-dataset
