Machine Learning Based Technique for Detection of Rank Attack in RPL based Internet of Things Networks
Vikram Neerugatti1, A. Rama Mohan Reddy2

1Vikram Neerugatti, Research scholar, Department of CSE. Sri Venkateswara University Tirupati, Andhra Pradesh, India.

2Dr. A. Rama Mohan Reddy, Professor, Department of CSE. Sri Venkateswara University Tirupati, Andhra Pradesh, India.

Manuscript received on 02 July 2019 | Revised Manuscript received on 16 July 2019 | Manuscript Published on 23 August 2019 | PP: 244-248 | Volume-8 Issue-9S3 August 2019 | Retrieval Number: I30440789S319/2019©BEIESP | DOI: 10.35940/ijitee.I3044.0789S319

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Abstract: 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.

Keywords: Internet of Things, RPL, rank attack, KNN, security
Scope of the Article: Internet of Things