5. What agent behaviours are of interest? What decisions do the agents make? What behaviours are being acted upon? What actions are being taken by the agents?
6. How do the agents interact with each other and the environment? How expansive or focused are agent interactions?
7. Where might the data come from, especially on agent behaviours, for our model?
Answers to these questions are discussed in the remainder of this chapter.
5.3
Postulations of Trust Framework
D3-FRT is potentially more proactive compared to other RTMs (described in detail in Chapter 2) because the framework aims to anticipate misbehaviour before an attack occurs [OBT11]. This is done with the use of past interactions among domain members, their recent and anticipated future RVs for trust management. The advantage of this proactive approach is that informed decisions can be made before misbehaviour occurs as illustrated in Figures 5.3 and 5.4. We refer to proactivity in terms of providing control such as downgrading of RV of suspect members that are predicted to be malicious before they can carry out an attack. The premise is that a member who has been compromised by an adversary exhibits a sequence of steps in order to misbehave. Proactivity in this research is the provision of useful information to stakeholders for countermeasures that can effectively reduce the effect of misbehaviour.
The hypothetical example in Figures 5.3 and 5.4 shows the difference in response times between the proposed approach and others. Figure 5.3 shows that the RV is only downgraded at time t5 after misbehaviour. D3-FRT can potentially predict the
Figure 5.3: Reactive approaches that compute ratings after each member interaction
misbehaviour between time interval t1 and t2 and the RV is downgraded at time t3 in
Figure 5.4. This is different to how other approaches work; that only downgrade RVs as a reaction to misbehaviour. Therefore, misbehaving members continue to attack until the reputation system identifies the misbehaviour.
Figure 5.4: Proactive approach that predicts misbehaviour
This capability fits within the DDDAS paradigm and it enables D3-FRT to perform better by making predictions about the possible future RVs of members. The prediction gives the system adequate time for preventive measures.
Furthermore, D3-FRT aims to rate agents and predict agents’ RVs in a manner that represents exhibited behaviours. The graph in Figure 5.5 depicts the expectations of the
5.4 An Agent-Based Model Approach 87
output from the framework which are changes to the RVs of agents depending on the behaviours that are exhibited.
Figure 5.5: Expected changes to RVs as agents exhibit different behaviours as time progresses
In the figure, MAX and MIN ratings are the maximum and minimum allowable RVs in the system respectively while Neural rating is the default RV for all domain members. The threshold is the minimum rating an agent can have to remain reputable; any value below the threshold is punishable depending on the nature of the application.
5.4
An Agent-Based Model Approach
ABMS is an approach to modelling systems composed of autonomous, interacting agents. In addition, agent-based modelling is a way to model the dynamics of complex systems and complex adaptive systems. Such systems often self-organise themselves and create an emergent order. Agent-based models also include models of behaviour (human or otherwise) which are used to observe the collective effects of agent behaviours and interactions. The development of agent modelling tools, the availability of micro-data, and advances in
computation have made possible a growing number of agent-based applications across a variety of domains and disciplines [MN10].
ABMS is widely used to understand systems composed of interacting individuals [NM07] (agent). These agents are the peers, nodes, participants, users and members that collaborate for a purpose in a domain. The term collaborate in this context is the interaction between agents in a specific domain. For example, consider mobile sensor nodes in a network monitoring vehicular movement as described in Chapter 1. The act of exchanging captured evidence between these nodes is referred to as collaboration. In this research, we use an ABMS approach that takes the agents and their interactions and embed them into our computational framework.
Due to the complexity (interactions and interdependencies) of the systems in use these days, a dynamic approach to prediction is required to capture all requirements, to have a close representation of reality. ABMS is suitable as evolving and dynamic behavioural changes in the domain are a major consideration for this research.
Provision of useful information to control the systems is another justification for the use of ABMS. For example, when agents optimise their collective behaviour through simple exchanges of information as is done in an ant colony optimisation [MN09], the purpose is to achieve a desired end-state, that is, an optimised system, rather than to simulate a dynamic process for its own sake.
Therefore, the choice of ABMS in this research resulted from the following requirements and properties:
• The problem consists of interacting agents.