Due to delay and energy constraints, a cognitive radio may not be able to perform spectrum sensing in all available channels. Therefore, a sensing policy is needed to decide which channels to sense. The channel selection problem is the problem of designing such a sensing policy to maximize throughput while avoiding interference to primary users. The channel selection problem can be formulated as a reinforcement learning problem. Channel selection schemes that employ reinforcement machine learning algorithms are vulnerable to belief manipulation attacks that contaminate the knowledge base of the learning algorithms. In this paper, we analyze the security of channel selection algorithms that are based on reinforcement learning and propose mitigation techniques that make these algorithms more robust against belief manipulation attacks.
@cite_5: Spectrum sensing is required for many cognitive radio applications, including spectral awareness, interoperability, and dynamic spectrum access. Any approach is highly dependent on the signal environment in which it operates. Noise, multipath channel distortion, sample-rate offsets, and center-frequency mis-estimations can make feature-based signal detection difficult. Additionally, as these values change in a dynamic environment, statically-trained classifiers cannot track the evolving class statistics. Unsupervised learning allows a cognitive radio system to evolve its classifier as the radio environment evolves, without the need for expert-annotated signal samples. While this allows improved classifier performance, previous work has demonstrated that unsupervised techniques developed without security in mind suffer from classifier manipulation through transmission of carefully crafted signals that shift decision boundaries. This paper develops countermeasures to the class manipulation attacks that mitigates their efficacy, and shows the robustness of unsupervised learning for signal classification in an evolving RF environment.
@cite_12: The estimation of the communication performance achievable with respect to environmental factors and configuration parameters plays a key role in the optimization process performed by a Cognitive Radio according to the original definition by Mitola [1]. In this paper we propose the use of Multilayered Feedforward Neural Networks as an effective technique for real-time characterization of the communication performance which is based on measurements carried out by the device and therefore offers some interesting learning capabilities.
In the context of cognitive radios, security of machine learning algorithms that are used for signal classification are addressed in @cite_12 and @cite_5 , but the types of learning algorithms that are analyzed is different from the algorithms in this paper. To the best of our knowledge, this paper describes the first analysis of a reinforcement learning algorithm's vulnerability against belief manipulation attacks to cognitive radios.