Summary of An Adaptive End-to-end Iot Security Framework Using Explainable Ai and Llms, by Sudipto Baral et al.
An Adaptive End-to-End IoT Security Framework Using Explainable AI and LLMs
by Sudipto Baral, Sajal Saha, Anwar Haque
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel framework for real-time IoT attack detection and response that combines machine learning (ML), explainable AI (XAI), and large language models (LLM). The framework leverages XAI techniques like SHAP and LIME to ensure adaptability across various ML algorithms. LLMs enhance interpretability and accessibility of detection decisions, providing actionable explanations for system administrators. The end-to-end framework facilitates seamless transition from development to deployment and demonstrates real-world application capability. Experiments with the CIC-IOT-2023 dataset show Gemini and OPENAI LLMs excel in attack mitigation, offering precise strategies or extensive security measures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a comprehensive framework for detecting and responding to IoT attacks using machine learning (ML), explainable AI (XAI), and large language models (LLM). The goal is to make it easier to understand what’s happening when an attack happens. It uses special XAI techniques to help with this, and also lets experts check how well the model is doing and fix any mistakes. |
Keywords
* Artificial intelligence * Gemini * Machine learning
