Conference paper Open Access

Misbehavior Detection in the Internet of Things: A Network-Coding-aware Statistical Approach

Antonopoulos, Angelos; Verikoukis, Christos


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    <subfield code="a">Grant numbers : This work has been supported by the research projects CellFive (TEC2014-60130-P) and AGAUR the Catalan Government (2014-SGR-1551).© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.</subfield>
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    <subfield code="a">&lt;p&gt;In the Internet of Things (IoT) context, the massive proliferation of wireless devices implies dense networks that require cooperation for the multihop transmission of the sensor data to central units. The altruistic user behavior and the isolation of malicious users are fundamental requirements for the proper operation of any cooperative network. However, the introduction of new communication techniques that improve the cooperative performance (e.g., network coding) hinders the application of traditional schemes on malicious users detection, which are mainly based on packet overhearing. In this paper, we introduce a non-parametric statistical approach, based on the Kruskal-Wallis method, for the detection of user misbehavior in network coding scenarios. The proposed method is shown to effectively handle attacks in the network, even when malicious users adopt a smart probabilistic misbehavior.&lt;/p&gt;</subfield>
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