Detection of Sybil Attack using Centrality and ACO in Opportunistic Networks

dc.contributor.authorKaur, Gurleen
dc.contributor.supervisorBhatia, Tarunpreet
dc.date.accessioned2016-08-08T10:17:37Z
dc.date.available2016-08-08T10:17:37Z
dc.date.issued2016-08-08
dc.descriptionMaster of Engineering-Information Securityen_US
dc.description.abstractThe evolution of opportunistic networks is on rise due to advancement in the technology of the handheld devices. In opportunistic networks, the routes are built dynamically, so there is no fixed network topology. Also these networks are delay tolerant and therefore tend to suffer from long delays as well as the network partitions; in the meantime they are very much at the risk of security threats. However, mitigating the effects of Sybil attack has been always a demanding and ongoing research topic in network security. Sybil attack is one of the most harmful attacks in networks. In this attack, the malicious node fabricates multiple identities to infect the network. Here, the combination of betweenness centrality and ACO has been used for optimal and secure routing against the Sybil attack. ONE simulator has been used for simulation of the entire scheme, where the simulation scenario consists of three different node groups’ viz. pedestrians, cars and trams in which the users are free to interact with one another. The effectiveness of the proposed technique has been determined by comparing parameters like packet drop, delivery probability, overhead ratio and throughput of ideal, vulnerable and detection modes. The integration of centrality and ACO has proved to be a very constructive scheme in mitigating the effects of Sybil attack in the opportunistic networks optimally without compromising the important parameters of a reliable and efficient network.en_US
dc.identifier.urihttp://hdl.handle.net/10266/4033
dc.language.isoenen_US
dc.subjectACOen_US
dc.subjectCentralityen_US
dc.subjectHiBOp Routingen_US
dc.subjectOpportunistic Networksen_US
dc.subjectOppNeten_US
dc.subjectSybil Attacken_US
dc.titleDetection of Sybil Attack using Centrality and ACO in Opportunistic Networksen_US
dc.typeThesisen_US

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