Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.706426
Title: A unified wormhole attack detection framework for mobile ad hoc networks
Author: Karlsson, Jonny
ISNI:       0000 0004 6057 3222
Awarding Body: Open University
Current Institution: Open University
Date of Award: 2017
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Abstract:
The Internet is experiencing an evolution towards a ubiquitous network paradigm, via the so-called internet-of-things (IoT), where small wireless computing devices like sensors and actuators are integrated into daily activities. Simultaneously, infrastructure-less systems such as mobile ad hoc networks (MANET) are gaining popularity since they provide the possibility for devices in wireless sensor networks or vehicular ad hoc networks 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.



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 packet traversal time and hop count analysis (TTHCA) which identifies infected routes, ii) an enhanced, dynamic traversal time per hop analysis (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.



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.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.706426  DOI: Not available
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