{"id":{"repo_id":"the-open-u","oai_identifier":"oai:oro.open.ac.uk:48643"},"canonical_url":"https://search.dev.ndltd.org/etd/the-open-u/oai:oro.open.ac.uk:48643","repository":{"repo_id":"the-open-u","name":"The Open University","base_url":"https://oro.open.ac.uk/cgi/oai2"},"display":{"title":"A Unified Wormhole Attack Detection Framework for Mobile Ad hoc Networks","abstract":"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.","abstract_html":"The Internet is experiencing an evolution towards a ubiquitous network paradigm, via the so-called &lt;i&gt;internet-of-things&lt;/i&gt; (IoT), where small wireless computing devices like sensors and actuators are integrated into daily activities. Simultaneously, infrastructure-less systems such as &lt;i&gt;mobile ad hoc networks&lt;/i&gt; (MANET) are gaining popularity since they provide the possibility for devices in &lt;i&gt;wireless sensor networks&lt;/i&gt; or &lt;i&gt;vehicular ad hoc networks&lt;/i&gt; 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. &lt;br&gt;&lt;/br&gt;&lt;br&gt;&lt;/br&gt; 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 &lt;i&gt;packet traversal time and hop count analysis&lt;/i&gt; (TTHCA) which identifies infected routes, ii) an enhanced, dynamic &lt;i&gt;traversal time per hop analysis&lt;/i&gt; (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. &lt;br&gt;&lt;/br&gt;&lt;br&gt;&lt;/br&gt; 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.","abstract_has_math":false,"creators":["Karlsson, Jonny"],"institution":"The Open University","degree_name":"phd","degree_level":"doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-01","date_published":"2017-01","updated_at":"2026-07-24T05:02:09Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Karlsson, Jonny"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2017-01-23"]},{"key":"dc:date.issued","label":"Date","values":["2017-01"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["ARRAY(0x7ffb7416cc48)"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["The Open University"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["https://oro.open.ac.uk/48643/"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["phd"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://oro.open.ac.uk/48643/1/PhD_Thesis_JonnyKarlsson_FINAL_Electronic.pdf"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["A Unified Wormhole Attack Detection Framework for Mobile Ad hoc Networks"]}]}],"canonical_facts":{"dc:creator":["Karlsson, Jonny"],"dc:date":["2017-01-23"],"dc:date.issued":["2017-01"],"dc:description.abstract":["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."],"dc:format":["application/pdf"],"dc:identifier.uri":["https://oro.open.ac.uk/48643/1/PhD_Thesis_JonnyKarlsson_FINAL_Electronic.pdf"],"dc:language":["en"],"dc:publisher.department":["ARRAY(0x7ffb7416cc48)"],"dc:publisher.institution":["The Open University"],"dc:relation.isreferencedby":["https://oro.open.ac.uk/48643/"],"dc:title":["A Unified Wormhole Attack Detection Framework for Mobile Ad hoc Networks"],"dc:type":["Thesis"],"dc:type.qualificationlevel":["doctoral"],"dc:type.qualificationname":["phd"]},"updated_at":"2026-07-24T05:02:09Z"}