Improving Resilience Against Node Capture Attacks in Wireless Sensor Networks Using ICmetrics

Wireless Sensor Networks (WSNs) have the potential of being employed in a variety of applications ranging from battlefield surveillance to everyday applications such as smart homes and patient monitoring. Security is a major challenge that all applications based on WSNs are facing nowadays. Firstly, due to the wireless nature of WSNs, and secondly due to their ability to operate in unattended environments makes them even more vulnerable to various sorts of attacks. Among these attacks is node capture attack in WSNs, whose threat severity can range from a single node being compromised in the network to the whole network being compromised as a result of that single node compromise. In this paper, we propose the use of ICMetric technology to provide resilience against node compromise in WSN. ICmetrics generates the security attributes of the sensor node based on measurable hardware and software characteristics of the integrated circuit. These properties of ICmetrics can help safeguard WSNs from various node capture attacks

Application of reinforcement learning for security enhancement in cognitive radio networks

Cognitive radio network (CRN) enables unlicensed users (or secondary users, SUs) to sense for and opportunistically operate in underutilized licensed channels, which are owned by the licensed users (or primary users, PUs). Cognitive radio network (CRN) has been regarded as the next-generation wireless network centered on the application of artificial intelligence, which helps the SUs to learn about, as well as to adaptively and dynamically reconfigure its operating parameters, including the sensing and transmission channels, for network performance enhancement. This motivates the use of artificial intelligence to enhance security schemes for CRNs. Provisioning security in CRNs is challenging since existing techniques, such as entity authentication, are not feasible in the dynamic environment that CRN presents since they require pre-registration. In addition these techniques cannot prevent an authenticated node from acting maliciously. In this article, we advocate the use of reinforcement learning (RL) to achieve optimal or near-optimal solutions for security enhancement through the detection of various malicious nodes and their attacks in CRNs. RL, which is an artificial intelligence technique, has the ability to learn new attacks and to detect previously learned ones. RL has been perceived as a promising approach to enhance the overall security aspect of CRNs. RL, which has been applied to address the dynamic aspect of security schemes in other wireless networks, such as wireless sensor networks and wireless mesh networks can be leveraged to design security schemes in CRNs. We believe that these RL solutions will complement and enhance existing security solutions applied to CRN To the best of our knowledge, this is the first survey article that focuses on the use of RL-based techniques for security enhancement in CRNs

Trust-based Incentive Mechanisms for Community-based Multiagent Systems

In this thesis we study peer-based communities which are online communities whose services are provided by their participant agents. In order to improve the services an agent enjoys in these communities, we need to improve the services other agents offer. Towards this goal, we propose a novel solution which allows communities to share the experience of their members with other communities. The experience of a community with an agent is captured in the evaluation rating of the agent within the community, which can either represent the trustworthiness or the reputation of the agent. We argue that exchanging this information is the right way to improve the services the agent offers since it: i) exploits the information that each community accumulates to allow other communities to decide whether to accept the agent while it also puts pressure on the agent to behave well, since it is aware that any misbehaviour will be spread to the communities it might wish to join in the future, ii) can prevent the agent from overstretching itself among many communities, since this may lead the agent to provide very limited services to each of these communities due to its limited resources, and thus its trustworthiness and reputation might be compromised. We study mechanisms that can be used to facilitate the exchange of trust or reputation information between communities. We make two key contributions. First, we propose a graph-based model which allows a particular community to determine which other communities to ask information from. We leverage consistency of past information and provide an equilibrium analysis showing that communities are best-off when they truthfully report the requested information, and describe how payments should be made to support the equilibrium. Our second contribution is a promise-based trust model where agents are judged based on the contributions they promise and deliver to the community. We outline a set of desirable properties such a model must exhibit, provide an instantiation, and an empirical evaluation