000			04341nam a22001697a 4500
003			NUST
082			_a005.8,ALT
100			_aAltaf, Ayesha _9124449
245			_aDesigning A Trust Management System for Malicious Node Detection and Prevention in Internet of Things / _cAyesha Altaf
260			_aRawalpindi, _bMCS (NUST), _cSeptember 2011
300			_axviii, 129 p
505			_aInternet of Things (IoT) is a rapidly growing field that provides seamless connectivity to physical objects to make them part of a smart environment. To fully utilize the potential power of these connected objects of IoT, trust existence among these objects is essential. Traditional security measures are not enough to provide comprehensive security to this smart world. Trust is used to mitigate the risk of uncertainty while connecting nodes to the Internet. This dissertation proposed the secure trust model for IoT-based Smart City for secure and trustworthy communication. We have proposed an adaptive Context-Based Trust Evaluation System (CTES) model, which focuses on calculating trust based on direct observations and indirect recommendations of communicating nodes. Each node takes recommendations from its identical contextsimilar nodes and filters out the malicious nodes. The weighing factor is dynamically assigned based on the previously calculated trust score experienced by the user. To enhance the security of smart buildings in IoT, this research also proposes an adaptive Context-Based Trust Evaluation System for Smart Building (CTES-SB) applications. The trust score for service is calculated based upon the client’s previous interaction and recommendation from context-similar clients. Using CTES-SB, the client selects the best service provider based on the previous and current trust scores for the next interaction. This research provides a classification of Trust Related Attacks (TRA) and a comparison of existing trust models with respect to TRA and Function Requirements (FR) of IoT. This comparison aim is to summarize the FR of IoT which must be considered while designing the Trust Management System (TMS). This research also focuses on the formal verification of the proposed CTES model. We analyze the effects of calculation of trust in terms of CTES accuracy, dynamic assignment for , and resiliency against Ballot Stuffing and Bad-Mouthing attacks to avoid bad nodes. The adaptive weights assigned to direct observations and indirect recommendations ensure the effectiveness of the Context-Based Trust Evaluation System Model (CTES) in detecting On-Off attacks. Moreover, context similarity measure calculations filter out those bad nodes which are posing a Sybil Attack. The similarity measure adapted in the proposed CTES is used to avoid the nodes which are changing their identity and making the environment vulnerable by posing a Sybil attack. Similarly, the dynamic assignment of makes the smart network safer by avoiding On–Off attacks of neighboring nodes. It is therefore verified and tested through simulation that by using these measures, the trust score of the suspected nodes becomes lower than the trust level and is hence discarded. The proposed CTES has been simulated on Contiki Cooja. The results ensure the significance of the proposed CTES model for dynamic assignment of and provide satisfactory results against EigenTrust, ServiceTrust, and ServiceTrust++ in terms of detecting malicious nodes and isolating them from providing recommendations. The comparison is in terms of filtering the malicious nodes from the network and the result shows that the trust converges quickly toward the ground-truth value. This is because EigenTrust establishes global trust thus failed in breaking chains of malicious nodes whereas ServiceTrust and ServiceTrust++ use uniform trust propagation with trust decay which helps them to identify some malicious chains and result in fewer failed services than EigenTrust. It has been observed that CTES is effective in a malicious environment for 22% of failed services in comparison of 45% to 60% of failed provided services in EigenTrust and ServiceTrust.
650			_aPhD Information Security Thesis _9132793
651			_aPhD IS Thesis _9132794
700			_aSupervised by Dr. Haier Abbas _9132792
942			_2ddc _cTHE
999			_c615832 _d615832