A Unified Wormhole Attack Detection Framework for Mobile Ad hoc Networks

The Internet is experiencing an evolution towards a ubiquitous network paradigm, via the so-called <i>internet-of-things</i> (IoT), where small wireless computing devices like sensors and actuators are integrated into daily activities. Simultaneously, infrastructure-less systems such as <i>mobile ad hoc networks</i> (MANET) are gaining popularity since they provide the possibility for devices in <i>wireless sensor networks</i> or <i>vehicular ad hoc networks</i> to share measured and monitored information without having to be connected to a base station. While MANETs offer many advantages, including self-configurability and application in rural areas which lack network infrastructure, they also present major challenges especially in regard to routing security. In a highly dynamic MANET, where nodes arbitrarily join and leave the network, it is difficult to ensure that nodes are trustworthy for multi-hop routing. Wormhole attacks belong to most severe routing threats because they are able to disrupt a major part of the network traffic, while concomitantly being extremely difficult to detect. <br></br><br></br> This thesis presents a new unified wormhole attack detection framework which is effective for all known wormhole types, alongside incurring low false positive rates, network loads and computational time, for a variety of diverse MANET scenarios. The framework makes three original technical contributions: i) a new accurate wormhole detection algorithm based on <i>packet traversal time and hop count analysis</i> (TTHCA) which identifies infected routes, ii) an enhanced, dynamic <i>traversal time per hop analysis</i> (TTpHA) detection model which is adaptable to node radio range fluctuations, and iii) a method for automatically detecting time measurement tampering in both TTHCA and TTpHA. <br></br><br></br> The thesis findings indicate that this new wormhole detection framework provides significant performance improvements compared to other existing solutions by accurately, efficiently and robustly detecting all wormhole variants under a wide range of network conditions.

Name dc:type.qualificationname

phd

Level dc:type.qualificationlevel

doctoral

Grantor dc:publisher.institution

The Open University

Year dc:date.issued

2017

Author dc:creator

• Karlsson, Jonny

Language dc:language

en