An IoT intrusion detection framework based on feature selection and large language models fine-tuning.

The rapid proliferation of Internet of Things (IoT) devices has significantly expanded the network attack surface, necessitating the deployment of advanced AI (artificial intelligence)-based intrusion detection systems (IDS) to bolster IoT security. But existing methods face two significant challenges: (1) Feature redundancy: Current approaches extract numerous flow-level features to learn attack behavior, resulting in high computational complexity and substantial redundant information. (2) Class imbalance: Limited attack traffic samples hinder models from effectively learning attack patterns. However, existing algorithms typically address only one of these issues, overlooking their interconnection. Therefore, we propose a Feature Selection and Large Language Models (LLMs)-based IoT intrusion detection framework (FSLLM). At its core is a multi-stage feature selection algorithm combining Minimum Redundancy Maximum Relevance algorithm (mRMR) and a Pearson Correlation Coefficient (PCC)-improved Covariance Matrix Adaptation Evolution Strategy algorithm (CMA-ES). This algorithm utilizes the CMA-ES algorithm for feature search while also taking into account the mutual information and collinearity among features, thereby more effectively reducing redundancy features. Subsequently, we employ the selected representative features to fine-tune LLMs and generate additional attack samples. This approach effectively reduces the computational cost of fine-tuning while producing higher-quality samples. Furthermore, we employ Focal Loss (FL) function-improved LightGBM as the classifier to improve detection performance. We evaluate our framework on five IoT intrusion detection datasets: NF-ToN-IoT-v2, NF-UNSW-NB15-v2, NF-BoT-IoT-v2, NF-CSE-CIC-IDS2018-v2, and CIC-ToN-IoT. Experimental results demonstrate that FSLLM achieves comparable or superior accuracy to current state-of-the-art methods while reducing redundant features by over 80%.