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Peer-to-peer (P2P) networking is becoming prevalent in Internet of Thing (IoT) platforms due to its low-cost low-latency advantages over cloud-based solutions. However, P2P networking suffers from several critical security flaws that expose devices to remote attacks, eavesdropping and credential theft due to malicious peers who actively work to compromise networks. Therefore, trust and reputation management systems are emerging to address this problem. However, most systems struggle to identify new smart models of malicious peers, especially those who cooperate together to harm other peers. This paper proposes an intelligent trust management system, namely, Trutect, to tackle this issue. Trutect exploits the power of neural networks to provide recommendations on the trustworthiness of each peer. The system identifies the specific model of an individual peer, whether good or malicious. The system also detects malicious collectives and their suspicious group members. The experimental results show that compared to rival trust management systems, Trutect raises the success rates of good peers at a significantly lower running time. It is also capable of accurately identifying the peer model.

Peer-to-peer (P2P) networking is becoming prevalent in Internet of Thing (IoT) platforms due to its low-cost low-latency advantages over cloud-based solutions. However, P2P networking suffers from several critical security flaws that expose devices to remote attacks, eavesdropping and credential theft due to malicious peers who actively work to compromise networks. Therefore, trust and reputation management systems are emerging to address this problem. However, most systems struggle to identify new smart models of malicious peers, especially those who cooperate together to harm other peers. This paper proposes an intelligent trust management system, namely, Trutect, to tackle this issue. Trutect exploits the power of neural networks to provide recommendations on the trustworthiness of each peer. The system identifies the specific model of an individual peer, whether good or malicious. The system also detects malicious collectives and their suspicious group members. The experimental results show that compared to rival trust management systems, Trutect raises the success rates of good peers at a significantly lower running time. It is also capable of accurately identifying the peer model.

The interconnected cloud computing paradigm is gaining considerable attention as a fundamental emerging model of cloud computing. It allows a wide range of interactions and collaborations across multiple service providers. Despite the potential advantages of interconnected clouds, establishing trust among participating parties is a challenging issue. In this paper, we introduce a lightweight trust management algorithm based on subjective logic (InterTrust) to promote trust in interconnected clouds. The experimental results demonstrate that InterTrust is capable of producing accurate trust information with significantly low execution time and high scalability compared to application of both the well-established trust management algorithm trust network analysis with subjective logic and no trust algorithm.

Delivering electronic health care (eHealth) services across multi-cloud providers to implement patient-centric care demands a trustworthy brokering architecture. Specifically, such an architecture should aggregate relevant medical information to allow informed decision-making. It should also ensure that this information is complete and authentic and that no one has tampered with it. Brokers deployed in eHealth services may fall short of meeting such criteria due to two key behaviors. The first involves violating international health-data protection laws by allowing user anonymity and limiting user access rights. Second, brokers claiming to provide trustworthy transactions between interested parties usually rely on user feedback, an approach vulnerable to manipulation by malicious users. This paper addresses these data security and trust challenges by proposing HealthyBroker, a novel, trust-building brokering architecture for multiple cloud environments. This architecture is designed specifically for patient-centric cloud eHealth services. It enables care-team members to complete eHealth transactions securely and access relevant patient data on a “need-to-know” basis in compliance with data-protection laws. HealthyBroker also protects against potential malicious behavior by assessing the trust relationship and tracking it using a neutral, tamper-proof, distributed blockchain ledger. Trust is assessed based on two strategies. First, all transactions and user feedback are tracked and audited in a distributed ledger for transparency. Second, only feedback coming from trustworthy parties is taken into consideration. HealthyBroker was tested in a simulated eHealth multi-cloud environment. The test produced better results than a benchmark algorithm in terms of data accuracy, service time, and the reliability of feedback received as measured by three malicious behavior models (naïve, feedback isolated, and feedback collective). These results demonstrate that HealthyBroker c

This paper proposes a gossip-based protocol that utilises a multi-factor weighting function (MFWF) that takes several parameters into account: residual energy, Chebyshev distances to neighbouring nodes and the sink node, node density, and message priority. The effects of these parameters were examined to guide the customization of the weight function to effectively disseminate data to three types of IoT applications: critical, bandwidth-intensive, and energy-efficient applications. The performances of the three resulting MFWFs were assessed in comparison with the performances of the traditional gossiping protocol and the Fair Efficient Location-based Gossiping (FELGossiping) protocol in terms of end-to-end delay, network lifetime, rebroadcast nodes, and saved rebroadcasts. The experimental results demonstrated the proposed protocol’s ability to achieve a much shorter delay for critical IoT applications. For bandwidth-intensive IoT application, the proposed protocol was able to achieve a smaller percentage of rebroadcast nodes and an increased percentage of saved rebroadcasts, i.e., better bandwidth utilisation. The adapted MFWF for energy-efficient IoT application was able to improve the network lifetime compared to that of gossiping and FELGossiping. These results demonstrate the high level of flexibility of the proposed protocol with respect to network context and message priority. Keywords: Internet of Things (IoT); wireless sensor network (WSN); gossiping protocol; context-aware; content-aware; routing protocol, King Saud University (RG-1438-002)

Cloud computing plays a major role in smart cities development by facilitating the delivery of various services in an efficient and effective manner. In a Peer-to-Peer (P2P) federated clouds ecosystem, multiple Cloud Service Providers (CSPs) collaborate and share services among them when experiencing a shortage in certain resources. Hence, incoming service requests to this specific resource can be delegated to other members. Nevertheless, the lack of preexisting trust relationship among CSPs in this distributed environment can affect the quality of service (QoS). Therefore, a trust management system is required to assist trustworthy peers in seeking reliable communication partners. We address this challenge by proposing TrustyFeer, a trust management system that allows peers to evaluate the trustworthiness of other peers based on subjective logic opinions, formulated using peers’ reputations and Service Level Agreements (SLAs). To demonstrate the utility of TrustyFeer, we evaluate the performance of our method against two long-standing trust management systems. The simulation results show that TrustyFeer is more robust in decreasing the percentage of services that do not conform to SLAs and increasing the success rate of exchanged services by good CSPs conforming to SLAs. This should provide a trustworthy federated clouds ecosystem for a better, more sustainable future.

Summary: Vehicular Ad Hoc Network (VANET) supports wireless communication among vehicles using vehicle-to-vehicle (V2V) communication and between vehicles and infrastructure using vehicle-to-infrastructure (V2I) communication. This communication can be utilized to allow the distribution of safety and non-safety messages in the network. VANET supports a wide range of applications which rely on the messages exchanged within the network. Such applications will enhance the drivers' consciousness and improve their driving experience. However, the efficiency of these applications depends on the availability of vehicles real-time location information. A number of methods have been proposed to fulfill this requirement. However, designing a V2V-based localization method is challenged by the high mobility and dynamic topology of VANET and the interference noise due to objects and buildings. Currently, vehicle localization is based on GPS technology, which is not always reliable. Therefore, utilizing V2V communication in VANET can enhance the GPS positioning. With V2V-based localization, vehicles can determine their locations by exchanging mobility data among neighboring vehicles. In this research work, we address the above challenges and design a realistic V2V-based localization method that extends the centroid localization (CL) by assigning a weight value to each neighboring vehicle. This weight value is obtained using a weighting function that utilizes the following factors: 1) link quality distance between the neighboring vehicles 2) heading information and 3) map information. We also use fuzzy logic to model neighboring vehicles' weight values. Due to the sensitivity and importance of the exchanged information, it is very critical to ensure its integrity and reliability. Therefore, in this work, we present the design and the integration of a mobility data verification component into the proposed localization method, so that only verified data from trusted neighboring vehi, 2016, Includes bibliography., Degree granted: Dissertation (Ph.D.)--Florida Atlantic University, 2016., Collection: FAU Electronic Theses and Dissertations Collection