Internet of Things (IoT) is a new Paradiagram in the network technology. It has the vast application in almost every field like retail, industries, and healthcare etc. It has challenges like security and privacy, robustness, weak links, less power, etc. A major challenge among these is security. Due to the weak connectivity links, these Internet of Things network leads to many attacks in the network layer. RPL is a routing protocol which establishes a path particularly for the constrained nodes in Internet of Things based networks. These RPL based network is exposed to many attacks like black hole attack, wormhole attack, sinkhole attack, rank attack, etc. This paper proposed a detection technique for rank attack based on the machine learning approach called MLTKNN, based on K-nearest neighbor algorithm. The proposed technique was simulated in the Cooja simulation with 30 motes and calculated the true positive rate and false positive rate of the proposed detection mechanism. Finally proved that, the performance of the proposed technique was efficient in terms of the delay, packet delivery rate and in detection of the rank attack.
Internet of Things (IoT) is the advanced technology, were the constrained nodes/things (all the objects around us such as chair, home, car, keys, etc.) will be connected to the internet to form a network, for sharing and monitoring the data, remotely. RPL (IPv6 Routing Protocol for Low Power and Lossy networks) is a routing protocol particularly designed for the constrained (low powered, low computation, less size, etc.) networks with the protocol 6LoWPAN (IPv6 Low Powered wireless Personal Area Networks). Due to the constrained behaviour of the RPL protocol, it will leads to many RPL routing attacks such as Sinkhole, Black hole, Wormhole, Selective forwarding, rank attacks, etc. This paper was focused on the Wormhole attack. The Wormhole attack will select the packets from one location and drops those packets in some other location (malicious) by forming the Tunnelling. To detect this attack here proposed and implemented a novel approach called (ADWA). Acknowledgement based technique for detection of the wormhole attack in RPL based Internet of Things networks. This approach was shown efficient results with the Telosb sky emulator nodes in the Contiki Cooja simulator, in terms of the Packet delivery ratio, delay and detection of wormhole attack.
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