This thesis is made up of four parts that are sub-divided into eight chapters. Part I includes the first two chapters, introduces and gives the background of this thesis. Chapter 1 provides the motivating context and the focus of this thesis. In Chapter 2, the concepts of reputation, trust and reputation management are introduced. The chapter discusses the usefulness, objectives and some problems of reputation and trust management systems.
Part II details an overview of reputation and trust-based models and a background of the DDDAS paradigm. Chapter 3 considers the existing problems of reputation and trust management and critically reviews literature focused on reputation and trust management models with proposed solutions to the trust related problems that have shown useful results. The chapter describes how the existing models attempt to solve the problems; each with its merits and faults. Comparative and gap analysis of the models are discussed extensively in
1.8 Roadmap of Thesis 15
this chapter. Chapter 4 introduces the dynamic data-driven application systems paradigm and motivates the usefulness of the paradigm for reputation management. The chapter discusses the DDDAS computational model in the context of examples of novel capabilities enabled through its implementation in different application areas. In addition, notable literature that adopts the paradigm is comprehensively described. The chapter elaborates on the issues of trust dynamics. We also describe how the DDDAS coupled with an agent-based simulation approach can be useful for the problem of trust dynamics.
In part III, we present the approach adopted in this thesis and an evaluation. Chapter 5 builds on the gaps in the literature that are identified in Chapters 3 and 4. The chapter introduces the novel framework (D3-FRT) for the predictive reputation management system that adopts a rich agent-based simulation approach. The chapter describes how D3-FRT is capable of providing dynamic trust ratings of domain members at runtime and making predictions. Chapter 6 applies the proposed framework in a network situation to examine its effectiveness in providing trusted communications among participants. D3-FRT’s performance in terms of its predictive capability, dynamism, and scalability and in varying scenarios is evaluated in this chapter.
Part IV summarises this thesis. Chapter 7 presents the overall conclusions based on the work done, reviews the contributions that this thesis has made and details some promising directions for future work.
Finally, Appendix A gives the attack modelling on the reputation system as they have been implemented in this research and in Appendix B, a sample of the data that is generated by the D3-FRT is presented.
Chapter 2
The Notion of Reputation and Trust
2.1
Background
The spread of Internet usage, proliferation of mobile devices, computers, and online market places, as well as the rapid growth of wireless networks and other related domains have changed the landscape of security. Users are exposed to greater risks as they collaborate anonymously with one another within diverse domains. The domains rely primarily on cooperative user behaviour for their effective operations because without this cooperation, they cannot fulfil their functions.
In order to reduce risks and improve performance, applications must manage trust relationships between users, by motivating cooperation and honest participation [SAB10]. P2P networks for example have undergone rapid progress and significant developments in recent years in this regard. However, due to their anonymous and open nature, malicious users can abuse the system by disseminating bogus files or acting together to commit as much damage as possible (collusion attack) [OBT12]. For such networks to be effective in
fulfilling their purpose of anonymous sharing, they should be relatively reliable, efficient and secure [CLB10]. Moreover, Internet applications have evolved from centralised and private computing platforms to distributed and collaborative computing systems. Collaboration is in fact today a fundamental Internet computing requirement.
Reputation and Trust Management is well suited for the requirements and the research is highly interdisciplinary [LS10], involving researchers from networking and communications: Mobile Adhoc Networks (MANETs)1, Wireless Sensor Network (WSNs)2 and P2P, data management and information systems, e-commerce and online communities: YouTube, Amazon and eBay, Artificial Intelligence, and also the Social Sciences and Evolution Biology. The concepts of trust and reputation have been developed into Reputation and Trust-based Models (RTMs). These models have gained popularity because they have been shown to be promising in the area of reputation and trust management as they aim to collect, aggregate, and disseminate feedback about a user’s behaviour, based on some predetermined premise.
Reputation and trust management is useful for establishing healthy and efficient collaborations among a network of participants and users that might not have sufficient prior knowledge about each other [LS10]. For example, eBay has several millions of auctions simultaneously open, serving as a listing service where buyers and sellers assume all associated risks with transactions [RZFK00]. There are occurrences of fraudulent transactions however there is a higher rate of successful transactions as well, which are primarily attributed to the reputation system on eBay called the Feedback Forum. This is 1Infrastructureless networks that have no fixed routers; all nodes are capable of movement and can be
connected dynamically in an arbitrary manner [RCK99]
2These are a large number of densely deployed nodes that mainly use broadcast communication