Performance Analysis of Network Intrusion Detection System for Real Time Anomaly Detection
Implementation plan:
—————————
Step 1: Initially, We collect and load the data from ” IoTNet24 Dataset ”
Step 2: Then, we pre-process the data using K-Nearest Neighbors technique used to impute missing data and Z-Score Normalization for one-hot encoding.
Step 3: Next, we perform the Feature Extraction process using BI-LSTM networks and Autoencoders with Stochastic Gradient Descent with Momentum for capturing temporal patterns in the data and select the features using Recursive Feature Elimination method.
Step 4: Next, we train the data using Bi-LSTM along with CNN ,GRU, adasyn and XGBoost to identify abnormalities in the network based on Threshold based detection.
Step 5: Finally, we plot graph for the following metrics:
5.1: Number of Epochs vs. Accuracy (%)
5.2: Number of Epochs vs. Precision (%)
5.3: Number of Epochs vs. Recall (%)
5.4: Number of Epochs Vs. Detection rate (%)
5.5: Number of Epochs Vs. False positive rate (%)
Software Requirements:
—————————–
1. Development Tool: Python 3.11.4 or above
2. Operating System: Windows-10(64-bit) or above
Dataset:
———-
Dataset link: https://www.kaggle.com/datasets/wittigenz/hydras
Note :-
——–
1) If the above plan does not satisfy your requirement, please provide the processing details, like the above step-by-step.
2) Please note that this implementation plan does not include any further steps after it is put into implementation.
3) If the plan satisfies your requirement, Please confirm with us.
4) Project based on Simulation only, not a real time project.
5) Please understand that any modifications made to the confirmed implementation plan will not be made before or after the project development.
We perform with an Existing Reference 2: Title: “Real-time intrusion detection based on residual learning through ResNet algorithm”
