The Internet of Things has broad application in military field, commerce, environmental monitoring, and many other fields. However, the open nature of the information media and the poor deployment environment have brought great risks to the security of wireless sensor networks, seriously restricting the application of wireless sensor network. Internet of Things composed of wireless sensor network faces security threats mainly from Dos attack, replay attack, integrity attack, false routing information attack, and flooding attack. In this paper, we proposed a new intrusion detection system based onK-nearest neighbor (K-nearest neighbor, referred to as KNN below) classification algorithm in wireless sensor network. This system can separate abnormal nodes from normal nodes by observing their abnormal behaviors, and we analyse parameter selection and error rate of the intrusion detection system. The paper elaborates on the design and implementation of the detection system. This system has achieved efficient, rapid intrusion detection by improving the wireless ad hoc on-demand distance vector routing protocol (Ad hoc On-Demand Distance the Vector Routing, AODV). Finally, the test results show that: the system has high detection accuracy and speed, in accordance with the requirement of wireless sensor network intrusion detection.

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a b s t r a c tAdvanced Metering Infrastructure (AMI) is the core component in a smart grid that exhibits a highly complex network configuration. AMI shares information about consumption, outages, and electricity rates reliably and efficiently by bidirectional communication between smart meters and utilities. However, the numerous smart meters being connected through mesh networks open new opportunities for attackers to interfere with communications and compromise utilities assets or steal customers private information.In this paper, we present a new DoS attack, called puppet attack, which can result in denial of service in AMI network. The intruder can select any normal node as a puppet node and send attack packets to this puppet node. When the puppet node receives these attack packets, this node will be controlled by the attacker and flood more packets so as to exhaust the network communication bandwidth and node energy. Simulation results show that puppet attack is a serious and packet deliver rate goes down to 20-10%. After analyzing the puppet attack, we propose the detection and prevention mechanism. Simulations show that puppet attack causes the same damage as a flooding attack and the proposed method can prevent the puppet attack efficiently.

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