Performance Analysis of Intelligent Cyber Defense Security Using reinforcement learning
Implementation Plan:
Step 1: Initially, we will construct a network with 50 IOT devices , 2 Gateway and 1 server.
Step 2: Then, we simulate and collect data using AI-Enhanced Cybersecurity Events Dataset.
Step 3: Next, we pre-process the data using the VAST-KD-SMOTE algorithm to balance the class distribution and improve data quality.
Step 4: Next, we analyze public-private interaction layers to detect prompt injection attacks using XML-RoBERTa model based on collected data.
Step 5: Next, we handle the data securely using Federated Learning (FL) Differential Privacy with Zero-Knowledge Proofs (ZKP) hybrid privacy-preserving mechanism.
Step 6: Next, we perform multi-vector attack detection and enhance the adaptive Defense using DQL-GP-HMS method.
Step 7: Next, we control data transmission to prevent the data exfiltration of sensitive information using MQTT protocol.
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. False Positive Rate (FPR)
8.6: Number of IOT Devices vs. Attack Detection Rate (%)
8.7: Number of IOT Devices vs. Data leakage Rate (%)
Software Requirements:
1. Development Tool: NS – 3.30 or above with Python
2. Operating System: Ubuntu 20.04 LTS (64-bit) or above
Dataset Link:
Link: https://www.kaggle.com/datasets/hassaneskikri/ai-enhanced-cybersecurity-events-dataset
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.
5) If you have any changes in the dataset ,kindly provide us before we implement it.
We perform with an Existing Approach Ref 5: Title:- Federated Learning for Cybersecurity: A Privacy-Preserving Approach
