To build efficient Internet of Things (IoT) communications between various applications, the Wireless Sensor Network (WSN) is incorporated with smart technologies. The IoT-enabled WSN is susceptible to a range of attacks because of its environment and intrinsic untrustworthy transmission. Intrusion detection systems (IDSs) are commonly used in WSN to protect against attacks to safeguard their protection. Outliers impair sensor information excellence and consistency due to the impact of environments and the restriction of sensors computing and transmission capacities. An efficient outlier and intrusion detection method are therefore necessary to provide efficient communication. Previous approaches are not practical in terms of the undue burden of computing and energy consumption on sensors. This paper proposes an efficient trust knowledge-based outlier and intrusion detection (TK-OID) for IoT enabled WSN. In this work, two trust knowledge base is used to detect outliers and intrusion. The first trust knowledge base is located at the small gateway node, collecting the sensed information from the sensor and detecting the local outliers. The next is situated at the base station, used to collect and analyze the small gateway's data and detect the intruder sensors. The experimental results indicate that this work performance improved detection accuracy compared to other existing methods.

Trust, Outlier Detection, Intrusion Detection, Trust, Knowledge Base.