Federated Learning for IoT Security Enhancement

Title: - Federated learning-based security enhancement in IoT environment
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Updated Implementation plan:
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Step 1: Initially, we construct a network consisting of 50- IoT Devices, 50- Users, 1-Blockchain, 1-Edge cloud Server.

Step 2: Next, we implement Two-layer authentication. This include the following steps:

2.1: Device level authentication:

2.1.1: Registration of IoT devices with Elliptic Curve Cryptography (ECC) keys.

2.1.2: Then, Authentication of the devices will be performed.

2.2: User level authentication:

2.2.1: Registration of Users will be implemented.

2.2.2: Authentication of Users using Multi-factor Authentication (User id, password, fingerprint ID).

2.2.3: Access control is done using Behavioral Context-Adaptive Role-Based Access Control (BCARBAC) algorithms.

Step 3: Then, we perform the federated learning, which involves the following steps:

3.1: Here, we implement the Differentially Averaged Federated Learning approach (DAFL) and then fine tune the model to the new data.

3.2: Then, we Update the model by distributing it to all devices.

3.2.1. Then, we use Transport Layer Security (TLS) to encrypt communications channels between the central server and devices

3.2.2. We implement the SHA-256 to ensure the integrity of the model updates during transmission.

3.3: Next, we secure the model updates by using the Secure Multi-Party Computation (MPC).

Step 4: Next, Blockchain integration is performed using the following steps:

4.1: We implement CryptoHash Seal to secure the blockchain communications.

4.2: We include smart contracts to automate and enforce the rules of data sharing and model updates.

4.3: Set up mining nodes to perform PoW computations and secure the blockchain network.

Step 5: Then, we store the data into the Edge-cloud Server by encrypting it for secure monitoring.

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

6.1: Threshold vs False positive rate (%)

6.2: Threshold vs False negative rate (%)

6.3: Number of communication rounds Vs. Loss (%)

6.4: Time (s) vs. Energy Consumption (J)

6.5: Time (s) vs. Latency (ms)

6.6: Time (s) vs. Encryption effectiveness (%)

6.7: Time (s) vs. Success rate (%)



Software Requirement:
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1. Development Tool: Ns3.35 with Python

2. Development OS: Ubuntu 22.04 LTS



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) This project is only based on simulations. Not a real time project.

4) If the plan satisfies your requirement, please inform us.

We develop an existing project:

Title: "An improved anomaly detection model for IoT security using decision tree and gradient boosting"
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1. #FederatedLearning
2. #IoTSecurity
3. #PerformanceAnalysis
4. #MachineLearning
5. #DataPrivacy
6. #SmartDevices
7. #CyberSecurity
8. #AIResearch
9. #DistributedLearning
10. #ThesisGuidance
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