LLM-Enhanced Semantic Policy Interpretation and Enforcement for Secure and Compliant Communication in 6G-Enabled IoT

Introduction

In this research we try to find how Large Language Models (LLMs) perform semantic communication to improve the IoT security and access control. In particular, we investigate whether LLMs can generate semantically extracted information from intrusion detection models and distil that knowledge into smaller client models. This experiment integrates LLM agents to build a novel policy enforcement framework for IoT Intrusion Detection Systems.

Getting Started

1. Clone the repository.

2. Install required Python libraries using pip install -r requirements.txt.

3. Create .env file in the root directory and add API keys as follows.

   OPENAI_API_KEY=
   GOOGLE_API_KEY=
   ANTHROPIC_API_KEY=
   LANGCHAIN_API_KEY=
   LANGCHAIN_PROJECT=
   LANGCHAIN_TRACING_V2=

4. Create data directory in the root directory and subdirectories for datasets as follows.

   data
   |-cic-iot
   |-wustl-iiot
   |-ton-iot
   |-bot-iot
   └-unsw-nb15
   

5. Place downloaded datasets in the relevant subdirectories.

6. Run main.ipynb Python notebook for each dataset.

Datasets

Name	Paper(s)	Year
CICIoT2023	CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment	2023
WUSTL-IIoT	WUSTL-IIOT-2021 Dataset for IIoT Cybersecurity Research	2021
TON_IoT	TON_IoT telemetry dataset: a new generation dataset of IoT and IIoT for data-driven Intrusion Detection Systems	2020
Bot-IoT	Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset	2019