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.
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.
@cite_5 @cite_12
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A novel approach of training data augmentation and domain adaptation is presented to support machine learning applications for cognitive radio. Machine learning provides effective tools to automate cognitive radio functionalities by reliably extracting and learning intrinsic spectrum dynamics. However, there are two important challenges to overcome, in order to fully utilize the machine learning benefits with cognitive radios. First, machine learning requires significant amount of truthed data to capture complex channel and emitter characteristics, and train the underlying algorithm (e.g., a classifier). Second, the training data that has been identified for one spectrum environment cannot be used for another one (e.g., after channel and emitter conditions change). To address these challenges, a generative adversarial network (GAN) with deep learning structures is used to 1) generate additional synthetic training data to improve classifier accuracy, and 2) adapt training data to spectrum dynamics. This approach is applied to spectrum sensing by assuming only limited training data without knowledge of spectrum statistics. Machine learning classifiers are trained to detect signals using either limited, augmented or adapted training data. Results show that training data augmentation increases the classifier accuracy significantly and this increase is sustained with domain adaptation while spectrum conditions change. This paper describes a new class of attacks specific to cognitive radio networks. Wireless devices that can learn from their environment can also be taught things by malicious elements of their environment. By putting artificial intelligence in charge of wireless network devices, we are allowing unanticipated, emergent behavior, fitting a perhaps distorted or manipulated level of optimality. The state space for a cognitive radio is made up of a variety of learned beliefs and current sensor inputs. By manipulating radio sensor inputs, an adversary can affect the beliefs of a radio, and consequently its behavior. In this paper we focus primarily on PHY-layer issues, describing several classes of attacks and giving specific examples for dynamic spectrum access and adaptive radio scenarios. These attacks demonstrate the capabilities of an attacker who can manipulate the spectral environment when a radio is learning. The most powerful of which is a self-propagating AI virus that could interactively teach radios to become malicious. We then describe some approaches for mitigating the effectiveness of these attacks by instilling some level of "common sense" into radio systems, and requiring learned beliefs to expire and be relearned. Lastly we provide a road-map for extending these ideas to higher layers in the network stack. A Markov chain analysis for spectrum access in licensed bands for cognitive radios is presented and forced termination probability, blocking probability and traffic throughput are derived. In addition, a channel reservation scheme for cognitive radio spectrum handoff is proposed. This scheme allows the tradeoff between forced termination and blocking according to QoS requirements. Numerical results show that the proposed scheme can greatly reduce forced termination probability at a slight increase in blocking probability 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.