While hand in hand with the Internet of Things (IoT) to provide people with daily convenience, technologies such as big data are also assisting illegal information to invade and interfere with IoT. In this paper, we take the construction of multi-polar intrusion detection system as the ultimate research goal, design the global search strategy of intrusion, the difference degree detection model and the association rule analysis model to analyze the intrusion information in an all-round way. With this data preparation, support vector machine (SVM) classifier is established and fused with similar cluster-based sample size approximation algorithm to build FSVM model as the detection method of abnormal data. The Gray Wolf Optimization (GWO) algorithm is used for the optimization of support vector machine algorithm parameter selection to form the GWO-SVM algorithm, which in turn proposes an IoT intrusion detection system based on the GWO-SVM algorithm. The designed intrusion detection system shows excellent suitability for IoT security multi-pole intrusion detection with accuracy up to 100.00% and F1 value up to 99.01% in the performance evaluation.
