Implementation Plan:
Step 1: Initially, we will construct a network with 50 IOT devices , 2 Gateway and 1 server.
Step 2: Then, we will simulate and collect data using AI-Enhanced Cybersecurity Events Dataset.
Step 3: Next, we will process the dataset using semantic chunking with spaCy and MiniLM embedding to enhance context understanding, entity identification, and improve cybersecurity data quality.
Step 4: Next, we will secure cybersecurity prompts using a lightweight context‑aware risk mechanism to detect adversarial inputs and prevent unsafe model interaction.
Step 5: Next, we will enhance privacy in cybersecurity using the PASCAI hybrid model, which combines PPAM for secure local‑remote communication and PACM for handling sensitive entities to ensure protected data analysis.
Step 6: Next, we will achieve adaptive cybersecurity decision‑making using the ARLSRF framework, which applies reinforcement learning and dynamic routing to intelligently select local or remote processing for secure threat response.
Step 7: Next, we will optimize routing stability using heuristic warm‑up routing and PPO‑based privacy reward optimization to balance privacy preservation and cybersecurity task utility.
Step 8: Next, we will minimize sensitive data exposure during distributed cybersecurity communication using a privacy‑preserving data sharing framework for secure processing and reliable response generation.
Step 9: Finally, we plot performance metrics for the following:
9.1: Privacy Leakage Rate
9.2: Routing Accuracy
9.3: Prompt Injection Detection Rate
9.4: Latency
9.5: Privacy–Utility Trade-off Score
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
