Current Internet of Things (IoT) networks have high detection rate of known types of attacks but the network node energy consumption is high. Aiming at this fact, an intrusion detection approach based on Practical Byzantine Fault Tolerance (PBFT) algorithm was proposed. Firstly, Support Vector Machine (SVM) was used for pre-training to obtain the intrusion detection decision rule, and the trained rule was applied to each node in IoT. Then, some nodes were voted to perform the active intrusion detection on other nodes in the network, while announce their detection results to other nodes. Finally, each node judged the state of other nodes according to PBFT algorithm, making the detection results reach consistency in the system. The simulation results on NSL-KDD dataset by TinyOS show that the proposed approach reduces the energy consumption by 12.2% and 7.6% averagely and respectively compared with Integrated Intrusion Detection System (ⅡDS) and Two-layer Dimension reduction and Two-tier Classification (TDTC) approach, effectively reducing the energy consumption of IoT.