Application of reinforcement learning for security enhancement in cognitive radio networks

Abstract

Please cite this article in press as: M.H. Ling, et al., Application of reinforcement learning for security enhancement in cognitive radio networks, Appl. Soft Comput. J. (2015), http://dx. a b s t r a c tCognitive 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.

A Comprehensive Review on Spectrum Management, Security and Energy-Efficient Cognitive Radio Networks

“…However, privacy preservation in this technique is still a concern. Lei et al, [97] Mee et al, [98] studied the use of Reinforcement Learning (RL), which promotes to attain optimum results for the enhancement of security by detecting the many malicious nodes and their attacks. The performance enhancements achieved by the intelligent approach of RL including low probability of false positive and missed detection, high detection rate, and utilization gain.…”

A proposal of privacy preserving reinforcement learning for secure multiparty computation

“…On the other hand, several results on privacy preserving have been obtained, but these are all studies on theory and application using encryption and homomorphic mapping. [18][19][20][21] Our method attempts to realize SMC by simple secret computation processing which does not use such complicated cryptographic processing and homomorphic mapping. The aim is to reduce the computational complexity of client while keeping the secret of data…”

“…In Miyajima, et al, [17,22] learning methods for SMC of BP, fuzzy system and VQ methods have been proposed and the validity of them has been proved. On the other hand, though there are some studies on privacy preserving with RL, [18][19][20][21] they are ones of cryptogram algorithms. It seems that there do not exist any studies with SMC.…”

Network Defense Strategy Selection with Reinforcement Learning and Pareto Optimization

“…The specific steps for an agent using Q-learning are visible in Figure 2 and defined as: -Determine the current state x t ; -Choose an action a t , either by exploring (with a probability of ε) or exploiting (with a probability of 1 − ε) the current Q-value; -Receive a reward r t for having performed the action; -Observe the resulting state x t+1 ; -Adjust the Q-value using a learning rate α, according to: Choose an action , either by exploring (with a probability of ) or exploiting (with a probability of 1 − ) the current Q-value; -Receive a reward for having performed the action; -Observe the resulting state ; -Adjust the Q-value using a learning rate , according to: Choosing an action by exploration just means randomly selecting one of the possible values. Choosing an action by exploitation means selecting the action with the highest Q-value [54].…”

“…In the context of a maximization function, superior means greater or Choosing an action by exploration just means randomly selecting one of the possible values. Choosing an action by exploitation means selecting the action with the highest Q-value [54].…”