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

Coordination of Expectations in Asset Pricing Experiments (Revised June 2003)

We investigate expectation formation in a controlled experimental environment. Subjects are asked to predict the price in a standard asset pricing model. They do not have knowledge of the underlying market equilibrium equations, but they know all past realized prices and their own predictions. Aggregate demand of the risky asset depends upon the forecasts of the participants. The realized price is then obtained from market equilibrium with feedback from individual expectations. Each market is populated by six subjects and a small fraction of fundamentalist traders. Realized prices differ significantly from fundamental values. In some groups the asset price converges slowly to the fundamental price, in other groups there are regular oscillations around the fundamental price. In all groups participants coordinate on a common prediction strategy. The individual prediction strategies can be estimated and correspond, for a large majority of participants, to simple linear autoregressive forecasting rules.

Institutions, Information, and Trade Policy in Times of Crisis

The paper examines the role of international institutions in preventing the rise of protectionism in times of times of crisis. Economic crisis exacerbates uncertainty in the conduct of commercial relations and thus makes it more likely for countries to resort to "beggar-thy-neighbor" trade policies. The historical record of the Great Depression supports this argument, where global trade suffered a downward spiral as governments pursued protectionist trade policies as a response to domestic pressures. This paper argues that the current era of globalization is distinguishable from its earlier counterparts by the presence of an extensive network of international institutions, which serve as conveyors of information that help to mitigate the information problem that prevails in prisoner‘s dilemma settings. Specifically, international institutions such as the WTO, preferential trade agreements (PTAs) and other international economic organizations increase the flow of information among countries. In doing so, they alleviate coordination problems as well as facilitate the detection of violations in commitments to maintaining a liberal trade regime. We suggest that this mechanism may explain why the current crisis is not replicating the pattern of the Great Depression. Moreover, we explore the combined effect of membership in international organization and political variables, the latter including democracy, veto players, partisanship of government, and government effectiveness. We test this argument using a newly-compiled dataset of trade policies during the current economic crisis and membership in international organizations. The paper finds strong support for the informational role of international institutions as a key factor preventing the rise of protectionism in times of crisis. Conversely, there is mixed evidence that the combining effect of international organizations and domestic political variables matters in explaining protectionism during this crisis