An adversarial machine learning approach is introduced to launch jamming attacks on wireless communications and a defense strategy is presented. A cognitive transmitter uses a pre-trained classifier to predict the current channel status based on recent sensing results and decides whether to transmit or not, whereas a jammer collects channel status and ACKs to build a deep learning classifier that reliably predicts the next successful transmissions and effectively jams them. This jamming approach is shown to reduce the transmitter’s performance much more severely compared with random or sensing-based jamming. The deep learning classification scores are used by the jammer for power control subject to an average power constraint. Next, a generative adversarial network is developed for the jammer to reduce the time to collect the training dataset by augmenting it with synthetic samples. As a defense scheme, the transmitter deliberately takes a small number of wrong actions in spectrum access (in form of a causative attack against the jammer) and therefore prevents the jammer from building a reliable classifier. The transmitter systematically selects when to take wrong actions and adapts the level of defense to mislead the jammer into making prediction errors and consequently increase its throughput.
This article investigates the physical-layer security of CR networks, which are vulnerable to various newly arising attacks targeting the weaknesses of CR communications and networking. We first review a range of physical-layer attacks in CR networks, including primary user emulation, sensing falsification, intelligence compromise, jamming, and eavesdropping attacks. Then we focus on the physical-layer security of CR networks against eavesdropping and examine the secrecy performance of cognitive communications in terms of secrecy outage probability. We further consider the use of relays for improving CR security against eavesdropping and propose an opportunistic relaying scheme, where a relay node that makes CR communications most resistant to eavesdropping is chosen to participate in assisting the transmission from a cognitive source to its destination. It is illustrated that the physical- layer secrecy of CR communications relying on opportunistic relaying can be significantly improved by increasing the number of relays, showing the security benefit of exploiting relay nodes. Finally, we present some open challenges in the field of relay-assisted physical-layer security for CR networks. Wireless sensor networks (WSNs) monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002–2013 of machine learning methods that were used to address common issues in WSNs. The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.  We study the adaptation of convolutional neural networks to the complex-valued temporal radio signal domain. We compare the efficacy of radio modulation classification using naively learned features against using expert feature based methods which are widely used today and e show significant performance improvements. We show that blind temporal learning on large and densely encoded time series using deep convolutional neural networks is viable and a strong candidate approach for this task especially at low signal to noise ratio. In this paper, we investigate cooperative spectrum sensing (CSS) in a cognitive radio network (CRN) where multiple secondary users (SUs) cooperate in order to detect a primary user (PU) which possibly occupies multiple bands simultaneously. Deep cooperative sensing (DCS), which constitutes the first CSS framework based on a convolutional neural network (CNN), is proposed. In DCS, instead of the explicit mathematical modeling of CSS which is hard to compute and also hard to use in practice, the strategy for combining the individual sensing results of the SUs is learned with a CNN using training sensing samples. Accordingly, an environment-specific CSS which considers both spectral and spatial correlation of individual sensing outcomes, is found in an adaptive manner regardless of whether the individual sensing results are quantized or not. Through simulation, we show that the performance of CSS can be improved by the proposed DCS with low complexity even when the number of training samples is moderate. Jamming games between a cognitive radio enabled secondary user (SU) and a cognitive radio enabled jammer are considered, in which end-user decision making is modeled using prospect theory (PT). More specifically, the interactions between a user and a smart jammer regarding their respective choices of transmit power are formulated as a game under the assumption that the end-user decision making under uncertainty does not follow the traditional objective assumptions stipulated by expected utility theory, but rather follows the subjective deviations specified by PT. Two PT-based static jamming games are formulated to describe how subjective SU and jammer choose their transmit power to maximize their individual signal-to-interference-plus-noise ratio (SINR)-based utilities under uncertainties regarding the opponent’s actions and channel states, respectively. The Nash equilibria of the games are presented under various channel models and transmission costs. Moreover, a PT-based dynamic jamming game is presented to investigate the long-term interactions between a subjective and a smart jammer according to a Markov decision process with uncertainty on the SU’s future actions and the channel variations. Simulation results show that the subjective view of an SU tends to exaggerate the jamming probabilities and decreases its transmission probability, thus reducing the average SINR. On the other hand, the subjectivity of a jammer tends to reduce its jamming probability, and thus increases the SU throughput. This letter presents our initial results in deep learning for channel estimation and signal detection in orthogonal frequency-division multiplexing (OFDM) systems. In this letter, we exploit deep learning to handle wireless OFDM channels in an end-to-end manner. Different from existing OFDM receivers that first estimate channel state information (CSI) explicitly and then detect recover the transmitted symbols using the estimated CSI, the proposed deep learning-based approach estimates CSI implicitly and recovers the transmitted symbols directly. To address channel distortion, a deep learning model is first trained offline using the data generated from simulation based on channel statistics and then used for recovering the online transmitted data directly. From our simulation results, the deep learning based approach can address channel distortion and detect the transmitted symbols with performance comparable to the minimum mean-square error estimator. Furthermore, the deep learning-based approach is more robust than conventional methods when fewer training pilots are used, the cyclic prefix is omitted, and nonlinear clipping noise exists. In summary, deep learning is a promising tool for channel estimation and signal detection in wireless communications with complicated channel distortion and interference. For achieving optimized spectrum usage, most existing opportunistic spectrum sensing and access protocols model the spectrum sensing and access problem as a partially observed Markov decision process by assuming that the information states and or the primary users' (PUs) traffic statistics are known a priori to the secondary users (SUs). While theoretically sound, the existing solutions may not be effective in practice due to two main concerns. First, the assumptions are not practical, as before the communication starts, PUs' traffic statistics may not be readily available to the SUs. Second and more serious, existing approaches are extremely vulnerable to malicious jamming attacks. By leveraging the same statistic information and stochastic dynamic decision-making process that the SUs would follow, a cognitive attacker with sensing capability can sense and jam the channels to be accessed by SUs, while not interfering PUs. To address these concerns, we formulate the antijamming, multichannel access problem as a nonstochastic multi-armed bandit problem. By leveraging probabilistically shared information between the sender and the receiver, our proposed protocol enables them to hop to the same set of channels with high probability while gaining resilience to jamming attacks without affecting PUs' activities. We analytically show the convergence of the learning algorithms and derive the performance bound based on regret . We further discuss the problem of tracking the best adaptive strategy and characterize the performance bound based on a new regret . Extensive simulation results show that the probabilistic spectrum sensing and access protocol can overcome the limitation of existing solutions and is highly resilient to various jamming attacks even with jammed acknowledgment (ACK) information. 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.