BELL-IDS: Blockchain-Empowered Large Language Model-Aided Context-Aware IDS in IoFT
Paper Title: BELL-IDS: Blockchain-Empowered Large Language Model-Aided Context-Aware IDS in IoFT
Journal Name: IEEE Internet of Things
Abstract: The widespread implementation of the Internet of Flying Things (IoFT) in several domains, such as military, industrial, and civilian, has emphasized their effectiveness in delivering adaptable and dynamic services. However, IoFT networks' growing interconnectivity and transparency make them susceptible to various malicious threats. Existing intrusion detection systems (IDS), especially those grounded on antiquated machine learning (ML) methods, face difficulty identifying real-time and abrupt assaults since they depend on obsolete datasets. Recent progress in Large Language Models (LLMs) has shown remarkable competencies in understanding complex data and tackling complicated challenges. In IoFT networks, these advancements could offer a potential solution to conventional IDS constraints.
Furthermore, distinct IDS solutions lack the robustness to protect IoFT networks from sophisticated threats, particularly when a single hacked node can endanger the entire network's security. This paper proposed a blockchain-aided LLM-based context-aware IDS for IoFT networks. Using LLM with context-aware capabilities, the proposed framework systems can analyze real-time network traffic, adapt to evolving threat contexts, and efficiently identify zero-day attacks. The proposed model achieves (98.47% and 98.12%) accuracy on different datasets, demonstrating the advantages of using LLM models in detecting cyber-attacks. Furthermore, detecting threats is only one aspect of the proposed framework; effective mitigation is equally essential. A real-time incident response system is designed to address this, employing blockchain-aided smart contracts to mitigate detected attacks effectively. The proposed framework combines the dynamic learning of LLMs with the blockchain's decentralized and immutable nature to enhance the detection of unknown threats and ensure accurate identification and response.