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http://hdl.handle.net/10603/341470
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DC Field | Value | Language |
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dc.coverage.spatial | Soft computing based trust management system for intrusion detection in wireless sensor networks | |
dc.date.accessioned | 2021-09-21T11:15:55Z | - |
dc.date.available | 2021-09-21T11:15:55Z | - |
dc.identifier.uri | http://hdl.handle.net/10603/341470 | - |
dc.description.abstract | Wireless Sensor Networks (WSNs) are used in many applications nowadays, because of its extensive usage. Immense growth in the applications using wireless sensor networks, make research attention towards it during the recent years. Nowadays, number of WSN applications increases, because of its popularity and it is very important to secure the network. WSNs are vulnerable to many attacks. But, there is a great challenge in providing security to the network, because of its specific features in terms of resources and capabilities. Among the various application areas of WSNs, military applications are very sensitive and important as well as more prone to intrusions. In those applications, trust management can be applied to provide security. With the essential existence of trust management in cluster based networks, it plays an important role in WSN security. In WSNs, the network layer is more exposed to attacks, because of the data forwarding function of this layer. This research work, focuses on a trust management system based on multi attributes, which can be applied to intrusion detection, and to improve detection accuracy. It uses a distributive trust calculation algorithm, which involves monitoring the neighboring nodes and calculation of trust of nodes using the trust metrics, namely, Message Success Rate (MSR), Elapsed Time at Node (ETN), Correctness (CS) and Fairness (FS) which are identified to monitor the behaviour of node in terms of network layer attacks. Along with the normal communication-based trust property MSR, this research work uses effective parameter ETN, which focus mainly on data and address modification attacks in an effective manner, and two socialinteraction-based parameters CS and FS, to address trust-related attacks effectively. Trust based malicious node detection is a facile method in Wireless Sensor Networks (WSNs). In trust based security model, decision is taken based on some specific behavior, and so it has uncertainty. Fuzzy systems play a major role in this. Fuzzy logic deals with uncertainty and has tolerance of imprecise data with high power of precision. So, in this research work, fuzzy logic based multi-attribute trust model is suggested for improving the trust calculation. The proposed trust model uses the previous Message Success Rate (MSR), Elapsed Time at Node (ETN), Correctness (CS) and Fairness (FS) as input to the fuzzy system. Once the four trust values are calculated, fuzzy computational theory is applied to compute the final fuzzy trust value of every node and finally it is converted into a crisp trust value which improves the performance of the trust system. newline | |
dc.format.extent | xvi,130 p. | |
dc.language | English | |
dc.relation | p.119-129 | |
dc.rights | university | |
dc.title | Soft computing based trust management system for intrusion detection in wireless sensor networks | |
dc.title.alternative | ||
dc.creator.researcher | Ram Prabha, V | |
dc.subject.keyword | Engineering and Technology | |
dc.subject.keyword | Computer Science | |
dc.subject.keyword | Telecommunications | |
dc.subject.keyword | Soft computing | |
dc.subject.keyword | Wireless sensor networks | |
dc.description.note | ||
dc.contributor.guide | Latha, P | |
dc.publisher.place | Chennai | |
dc.publisher.university | Anna University | |
dc.publisher.institution | Faculty of Information and Communication Engineering | |
dc.date.registered | ||
dc.date.completed | 2020 | |
dc.date.awarded | 2020 | |
dc.format.dimensions | 21cm | |
dc.format.accompanyingmaterial | None | |
dc.source.university | University | |
dc.type.degree | Ph.D. | |
Appears in Departments: | Faculty of Information and Communication Engineering |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 25.08 kB | Adobe PDF | View/Open |
02_certificates.pdf | 501.5 kB | Adobe PDF | View/Open | |
03_vivaproceedings.pdf | 1.17 MB | Adobe PDF | View/Open | |
04_bonafidecertificate.pdf | 555.05 kB | Adobe PDF | View/Open | |
05_abstracts.pdf | 9.42 kB | Adobe PDF | View/Open | |
06_acknowledgements.pdf | 680.05 kB | Adobe PDF | View/Open | |
07_contents.pdf | 15.97 kB | Adobe PDF | View/Open | |
08_listoftables.pdf | 7.93 kB | Adobe PDF | View/Open | |
09_listoffigures.pdf | 8.43 kB | Adobe PDF | View/Open | |
10_listofabbreviations.pdf | 8.54 kB | Adobe PDF | View/Open | |
11_chapter1.pdf | 481.9 kB | Adobe PDF | View/Open | |
12_chapter2.pdf | 174.92 kB | Adobe PDF | View/Open | |
13_chapter3.pdf | 408.2 kB | Adobe PDF | View/Open | |
14_chapter4.pdf | 303.38 kB | Adobe PDF | View/Open | |
15_chapter5.pdf | 318.54 kB | Adobe PDF | View/Open | |
16_conclusion.pdf | 16.86 kB | Adobe PDF | View/Open | |
17_references.pdf | 136.33 kB | Adobe PDF | View/Open | |
18_listofpublications.pdf | 85.46 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 48.09 kB | Adobe PDF | View/Open |
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