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Cognitive radio has become an emerging advanced wireless communication technology to achieve maximal spectrum efficiency. In cognitive radio networks, the threat of radio jamming attack arises as a big issue due to the vulnerability of radio transmission. Therefore, anti-jamming is an active research topic for a long time. Recently, with the success of deep learning, deep reinforcement learning algorithms have been applied to solve the dynamic spectrum access and anti-jamming problem. In this paper, we propose a Deep Double-Q learning-based method to learn an efficient communication policy including channel access and transmission power for tackling different jamming scenarios. The proposed scheme uses observed spectral information as input and Q-function is approximated by a neural network. Simulation results show that Double-Q learning algorithm with Convolutional Neural Network achieves effective communication strategies to avoid various jamming patterns compared with other traditional methods.

Secondary Surveillance Radars (SSR) have been employed substantially in both military and civilian domains. In military applications, the SSR technology has been used for distinguishing between friendly and hostile targets. In civilian applications, the SSR technology has been implemented in air traffic control (ATC) and collision avoidance between airplanes. Today, the majority of civilian airplanes have been equipped with Automatic Dependent Surveillance - Broadcasting (ADS-B) transponders for reporting their identity, position, velocity and additional information. Traditional methods for tracking multiple civilian airplanes consist of detecting ADS-B preamble pulses, decoding ADS-B waveforms and parsing ADS-B messages. In this paper, we provide an alternative solution for passive tracking of multiple civilian airplanes using Time Difference of Arrival (TDOA) localization of mode-S signals. Though being more complicated than classical methods, the proposed solution provides numerous advantages such as the ability to track a larger number of targets and the robustness to jamming attacks.

The sequential monitoring of SCADA systems against cyber/physical attacks is considered in this paper. The SCADA systems are described by the discrete-time state space models in the presence of random noises. The cyber/physical attacks are modeled as additive signals of short duration impacted both the state evolution and the sensor measurement equations. The detection of attacks is formulated as the problem of sequential transient change detection in stochastic-dynamical systems. The steady-state Kalman filter and the fixed-size parity space are utilized for generating the sequence of residuals. The unified statistical model is developed to describe the residual generation by both methods. Based on this statistical model, the Variable Threshold Window Limited CUmulative SUM (VTWL CUSUM) algorithm is designed to detect the transient changes. Taking into consideration the detection criterion, which aims at minimizing the worst-case probability of missed detection subject to a given value on the worst-case probability of false alarm, the thresholds are tuned for optimizing the VTWL CUSUM algorithm. It is shown that the optimal choice of thresholds leads to the simple Finite Moving Average (FMA) detection rule. The proposed algorithms are applied to detect the covert attack on a simple SCADA water distribution network.

In this paper, we propose a novel pre-processing method for the direction-of-arrival (DOA) estimation of multiple sources in multipath environments. In such environments, radio signals impinging on an antenna array can be either uncorrelated, partially correlated or fully correlated (or coherent signals). The proposed pre-processing technique consists of receiving the radio signals at different time instants while moving the uniform circular array (UCA) in predefined paths. The signal covariance matrix is rendered full rank by averaging the sample covariance matrix estimated from the radio signals impinging on the moving UCA. As a result, conventional super-resolution algorithms such as MUSIC can be applied. The simulation results have shown the superiority of the proposed method in comparison with classical pre-processing techniques.

In spectrum monitoring applications, radio signals are often sampled at the speed of several hundreds of MHz by high-performance analog-to-digital converters (ADC). These wideband IQ samples are generally stored in hard drives for offline monitoring purposes such as direction finding or position location. In such scenarios, the lossless compression and decompression of wideband IQ samples helps in reducing storage space as well as increasing reading and writing speeds. In this paper, we propose a novel algorithm for compressing and decompressing wideband IQ samples. The proposed method is based on an elegant combination of the well-known Linear Predictive Coding (LPC) filter and the simple Frame of Reference (FOR) scheme. The experimental results have demonstrated the superior performance of the proposed algorithm in compressing and decompressing wideband IQ samples over conventional data compression techniques.

This paper deals with the problem of detecting cyber-physical attacks on Supervisory Control And Data Acquisition (SCADA) systems. The discrete-time state space model is used to describe the systems. The attacks are modeled as additive signals of short duration on both state evolution and sensor measurement equations. The steady-state Kalman filter is employed to generate the sequence of innovations. Next, these independent random variables are used as entries of the Variable Threshold Window Limited CUmulative SUM (VTWL CUSUM) test. It has been shown that the optimal choice of thresholds with respect to (w.r.t.) the transient change detection criterion leads to the Finite Moving Average (FMA) test. The main contribution of this paper is a sensitivity analysis of the FMA test. This analysis is based on a numerical calculation of the probabilities of wrong decision under the variation of operational parameters. Theoretical results are applied to the detection of an attack scenario on a SCADA water network.

International audience; This paper addresses the problem of detecting cyber/physical attacks on Supervisory Control And Data Acquisition (SCADA) systems. The detection of cyber/physical attacks is formulated as the problem of detecting transient changes in stochastic-dynamical systems in the presence of unknown system states (often regarded as the nuisance parameter). The Variable Threshold Window Limited CUmulative SUM (VTWL CUSUM) test is adapted to the detection of transient changes of known profiles in the presence of nuisance parameter. Taking into account the performance criterion of the transient change detection problem, which minimizes the worst-case probability of missed detection for a given value of the worst-case probability of false alarm, the thresholds are tuned for optimizing the VTWL CUSUM algorithm. The optimal choice of thresholds leads to the simple Finite Moving Average (FMA) algorithm. The proposed algorithms are utilized for detecting the covert attack on a simple water distribution system, targeting at stealing water from the reservoir without being detected.