Smart Jamming Research Articles

Adaptive Whitening and Feature Gradient Smoothing-Based Anti-Sample Attack Method for Modulated Signals in Frequency-Hopping Communication

Adaptive Whitening and Feature Gradient Smoothing-Based Anti-Sample Attack Method for Modulated Signals in Frequency-Hopping Communication

Smart binary phase‐coding for pulse‐compression radar: An electronic protection technique against repeater jamming

AbstractNowadays, modern radar systems increase their target detection capabilities by processing pulses coherently. On the other hand, digital radio frequency memory‐based modern jammers have the ability to work coherently and can deceive radars even with a very low effective radiated power. These jammers, which have the capability of storing the radar's pulse, can use the previous pulses that they have stored in their memory during the electronic deception, without waiting for the last pulse of the radar, in other words, before the new pulse is received. If the radar does not change its parameters from pulse to pulse, such smart jamming techniques applied in this way can be very effective. In this article, the authors propose to use a smart binary phase‐coding method for pulse compression radar as an electronic protection technique against repeater jamming. This approach further improves the target detection capability of modern radar systems, which use coherent integration in the receiver. The proposed method can provide high protection against digital radio frequency memory‐based repetitive range deception techniques without compromising the radar's target detection capability. In the simulations, the traditional approach in which the same code is used without changing from pulse‐to‐pulse, and the approach using code sets obtained by the smart binary phase‐coding method in intra‐pulse modulation are compared. The results show that the proposed method can significantly improve the isolation against deception jamming and the target detection capability simultaneously.

Against Jamming Attack in Wireless Communication Networks: A Reinforcement Learning Approach

When wireless communication networks encounter jamming attacks, they experience spectrum resource occupation and data communication failures. In order to address this issue, an anti-jamming algorithm based on distributed multi-agent reinforcement learning is proposed. Each terminal observes the spectrum state of the environment and takes it as an input. The algorithm then employs Q-learning, along with the primary and backup channel allocation rules, to finalize the selection of the communication channel. The proposed algorithm designs primary and backup channel allocation rules for sweep jamming and smart jamming strategies. It can predict the behavior of jammers while reducing decision conflicts among terminals. The simulation results demonstrate that, in comparison to existing methods, the proposed algorithm not only enhances data transmission success rates across multiple scenarios but also exhibits superior operational efficiency when confronted with jamming attacks. Overall, the anti-jamming performance of the proposed algorithm outperforms the comparison methods.

Optimization-Driven DRL-Based Joint Beamformer Design for IRS-Aided ITSN Against Smart Jamming Attacks

This paper investigates an intelligent reflecting surfaces (IRS) aided anti-jamming communication strategy in the integrated terrestrial-satellite network (ITSN), where the IRS is exploited to mitigate jamming interference and enhance the integrated system communication performance. In such a network, the terrestrial network and satellite network are co-existing with a spectrum-sharing scheme in the presence of a multi-antenna jammer. We aim at maximizing the weighted sum rate (WSR) of all users by jointly optimizing the terrestrial beamformers and IRS phase shifts while considering the signal-to-interference-plus-noise ratio (SINR) requirements of legitimate users. Different from the non-convex optimization techniques utilized in the IRS-related problem, a novel optimization-driven deep reinforcement learning (DRL) algorithm is proposed, which leverages both the robustness of model-free learning approaches and the efficiency of model-based optimization methods. In the optimization module of the proposed algorithm, we analyze the smart jammer under the unknown jamming model and derive a lower bound of the anti-jamming uncertainty, such that the IRS-aided anti-jamming problem can be solved by alteration method with second-order cone programming (SOCP) algorithm and semidefinite relaxation (SDR) technique. Simulation results demonstrate that the IRS can enhance the anti-jamming performance efficiently, and the proposed optimization-driven DRL algorithm can improve both the learning rate and the system performance compared with existing solutions.

Smart Jamming Attack and Mitigation on Deep Transfer Reinforcement Learning Enabled Resource Allocation for Network Slicing

Smart Jamming Attack and Mitigation on Deep Transfer Reinforcement Learning Enabled Resource Allocation for Network Slicing

Smart Jamming Cloak for Moving Targets With Full Polarization Response

Smart Jamming Cloak for Moving Targets With Full Polarization Response

Two-Dimensional Precise Controllable Smart Jamming Against SAR via Phase Errors Modulation of Transmitted Signal

Two-Dimensional Precise Controllable Smart Jamming Against SAR via Phase Errors Modulation of Transmitted Signal

IRS-Assisted Anti-Jamming Transmission for an Integrated Satellite-UAV-Terrestrial Network With Imperfect CSI: A Game-Based Perspective

In this paper, an intelligent reflecting surface (IRS)-assisted integrated satellite-unmanned aerial vehicle (UAV)-terrestrial (SUT) Internet of Things (IoT) network faced with a smart jammer under imperfect channel state information (CSI) conditions is considered. We propose a Stackelberg game model to describe the adversarial relationship between the satellite, UAV, and IRS and the jammer, which are modeled as the leader and the follower, respectively. For the follower subgame, the jammer aims to minimize the jamming power while guaranteeing that the jamming energy efficiency surpasses a certain threshold. Under this setup, the angle of arrival (AoA)-based discretization method is utilized to address the imperfect CSI issue. Then, the use-and-then-forget method and the Lagrangian function are employed to obtain a closed-form expression for the jammer’s power. Finally, a feasible jamming power solution is obtained by means of the Cauchy-Schwarz inequality. For the leader subgame, we aim to optimize the hybrid beamforming design of the satellite, UAV and IRS, with the goal of minimizing the total transmit power, while guaranteeing that the downlink received signal-to-interference-plus-noise ratio (SINR) surpasses the minimum communication threshold. We propose an alternating optimization scheme, in which the Cauchy-Schwarz inequality, the AoA-based discretization method, and nonsmooth penalty functions are employed to alternately obtain the optimal satellite beamforming vector, UAV beamforming vector and IRS phase matrix when the other variables are fixed. Through our analytical and numerical results, the proposed beamforming scheme achieves a reduction of 16.9% in average power consumption compared with other benchmark schemes when obtaining the same anti-jamming performance.

Learning-Based Multi-Domain Anti-Jamming Communication with Unknown Information

Due to the open nature of the wireless channel, wireless networks are vulnerable to jamming attacks. In this paper, we try to solve the anti-jamming problem caused by smart jammers, which can adaptively adjust the jamming channel and the jamming power. The interaction between the legitimate transmitter and the jammers is modeled as a non-zero-sum game. Considering that it is challenging for the transmitter and the jammers to acquire each other’s information, we propose two anti-jamming communication schemes based on the Deep Q-Network (DQN) algorithm and hierarchical learning (HL) algorithm to solve the non-zero-sum game. Specifically, the DQN-based scheme aims to solve the anti-jamming strategies in the frequency domain and the power domain directly, while the HL-based scheme tries to find the optimal mixed strategies for the Nash equilibrium. Simulation results are presented to validate the effectiveness of the proposed schemes. It is shown that the HL-based scheme has a better convergence performance and the DQN-based scheme has a higher converged utility of the transmitter. In the case of a single jammer, the DQN-based scheme achieves 80% of the transmitter’s utility of the no-jamming case, while the HL-based scheme achieves 63%.

Smart Jamming Against SAR Based on Nonlinear Frequency-Modulated Signal

The traditional methods of jamming against synthetic aperture radar (SAR), which are implemented based on linear frequency-modulated (LFM) signals, are currently unable to meet the needs of electronic countermeasures. Compared with the LFM signal, the frequency of the nonlinear frequency-modulated (NLFM) signal varies nonlinearly with time. Because of this time-frequency structure, the NLFM signal is naturally mismatched with the linear matched filter of SAR; thus, a stronger and more flexible jamming effect is produced. In this paper, the NLFM signal is used in the SAR jamming field for the first time, and a smart jamming method against SAR based on the NLFM signal is proposed, which can produce the following two jamming effects: barrage and deceptive. First, the shaping factor is defined to generate the expected NLFM signal. The shaping factor controls the jamming range in the fast time direction for barrage jamming. For deceptive jamming, a jamming method involving piecewise weighting is proposed. The shaping factor is used to control the defocusing degree of false targets. Subsequently, the pulse compression results of the jamming signal are deduced, and the influence of different parameters on the jamming effect is theoretically analyzed. Experimental results based on simulation show that the method can control the jamming range and effect in the fast time direction while achieving a flexible deceptive jamming effect.

Intelligent deep learning‐aided future beam and proactive handoff prediction model in Unmanned Aerial Vehicle‐assisted anti‐jamming Terahertz communication system

SummaryWireless communications often suffer from legitimate transmissions regarding malicious jamming attacks launched through the smart jammer. The drone or unmanned aerial vehicle (UAV) communication networks derived with reconfigurable intelligent surfaces (RIS) increase the issues of beam selection and proactive handoff in terahertz (THz). Thus, a new heuristic strategy is designed for efficient and incorporated optimization of the beamforming vector and anti‐jamming transmit power allocation in undefined environments. Here, the transmit power allocation and beamforming matrix of UAV are optimized with the developed hybrid heuristic algorithm of the Hybrid Crow Black Widow Search Optimization (HCBWSO) algorithm for maximizing the system achievable rate. Here, the HCBWSO algorithm is implemented to integrate with the Crow search algorithm (CSO) and Black Widow Optimization (BWO). The second contribution is to adopt RIS into THz–UAV communications, a new Enhanced Deep Temporal Convolutional Network (EDTCN) for predicting the future beam and proactive handoff of UAVs based on their prior analysis of the UAV locations, where the HCBWSO algorithm is utilized for recommending EDTCN. Here, the training of the EDTCN needs to be done with the collection of UAV information from the DEEPMIMO dataset for predicting the future beams and, also, tracking the location of the UAV. EDTCN helps in increasing the possibility of expanding the UAV coverage and also increases the consistency of the THz communication system. Thus, the prediction of the future beam increases the coverage area of the UAV and also maximizes the system rate in the THz communication system.

Improving anti-jamming decision-making strategies for cognitive radar via multi-agent deep reinforcement learning

Most of the existing anti-jamming decision-making methods overly rely on the subjective experience of radar operators. However, due to the rapid development of cognitive radar and modern electronic warfare, conventional anti-jamming decision-making methods can no longer adapt to the complex and changing electromagnetic environment. The advent of deep reinforcement learning (DRL) provides a new attractive solution for this issue. In this paper, an adversarial anti-jamming decision-making network for cognitive radar via multi-agent deep reinforcement learning (MDRL) is proposed, which has good self-learning ability and can meet the requirements of intelligent, dynamic and real-time in modern electronic warfare. Since competitive decision-makers are considered and these two confrontational sides are not able to obtain the completely accurate information of each other, the environment model is specifically constructed as a partially observable Markov decision process (POMDP). Then, a decision-making network is designed based on deterministic deep deterministic policy gradient (DDPG) algorithm to explore the competition between cognitive radar and smart jammer. In order to overcome the environment non-stationarity, the decision-making network is trained and tested in a special MDRL framework. The experimental results demonstrate that the proposed method is effective in anti-jamming decision-making system of cognitive radar. Furthermore, the two confrontational sides show high decision-making ability and perform well in the adversarial scenario by comparing with other training policies, which demonstrate that confrontational training with powerful opponents can improve the intelligence level of all agents.

Optimal Association Strategy of Multi-Gateway Wireless Sensor Networks Against Smart Jammers

The Internet of Things (IoT) immensely benefits from wireless sensor networks (WSNs). Unfortunately, WSNs, by virtue of being low-power, are vulnerable to physical jamming attacks. This letter focuses on WSNs with multiple gateways where potentially many sensors are under the attack of a smart jammer. The jammer can observe the communication links and selectively jam the transmission of some sensors such that the throughput of the WSN is minimized. The proposed defense method is to develop a gateway selection and sensors’ associations policy that is aware of the jammer’s existence. First, the conflict interaction between the WSN and the jammer is modeled as a Stackelberg game. Then the WSN’s strategy, leading to a Nash equilibrium, is derived which is both stable and optimal. The numerical results show that compared to existing alternative approaches, the proposed method, with only four gateways, can increase the throughput by more than 200% and saves around 30 dB in sensors’ power consumption.

Mitigating Smart Jammers in Multi-User MIMO

Wireless systems must be resilient to jamming attacks. Existing mitigation methods based on multi-antenna processing require knowledge of the jammer's transmit characteristics that may be difficult to acquire, especially for smart jammers that evade mitigation by transmitting only at specific instants. We propose a novel method to mitigate smart jamming attacks on the massive multi-user multiple-input multiple-output (MU-MIMO) uplink which does not require the jammer to be active at any specific instant. By formulating an optimization problem that unifies jammer estimation and mitigation, channel estimation, and data detection, we exploit that a jammer cannot change its subspace within a coherence interval. Theoretical results for our problem formulation show that its solution is guaranteed to recover the users' data symbols under certain conditions. We develop two efficient iterative algorithms for approximately solving the proposed problem formulation: MAED, a parameter-free algorithm which uses forward-backward splitting with a box symbol prior, and SO-MAED, which replaces the prior of MAED with soft-output symbol estimates that exploit the discrete transmit constellation and which uses deep unfolding to optimize algorithm parameters. We use simulations to demonstrate that the proposed algorithms effectively mitigate a wide range of smart jammers without a priori knowledge about the attack type.

A Cooperative Smart Jamming Attack in Internet of Things Networks

A Cooperative Smart Jamming Attack in Internet of Things Networks

DeepAntiJam: Stackelberg Game-Oriented Secure Transmission via Deep Reinforcement Learning

In this letter, we present a novel deep reinforcement learning-assisted anti-jamming transmission scheme (DeepAntiJam) to guarantee reliable and creditable transmission in the presence of one smart jammer and multiple eavesdroppers. Specifically, we formulate secure transmission as a Stackelberg game in which the jammer, as the leader, adaptively adjusts its jamming power while the transmitter, as the follower, selects its transmit power and secrecy rate accordingly. Furthermore, the existence and uniqueness of Stackelberg equilibrium are proved. To achieve the Stackelberg equilibrium when the prior knowledge of jammer is unknown, DeepAntiJam is proposed to improve the secrecy throughput, where a solver neural network is utilized to determine the optimal transmission parameter according to the transmission strategy obtained by deep reinforcement learning. Moreover, transfer learning is introduced into the initialization of DeepAntiJam to avoid unnecessary initial random exploration. Simulation results validate that DeepAntiJam can enhance secrecy throughput significantly under the coexistence of smart jammer.

Anti-Jamming Beam Alignment in Millimeter-Wave MIMO Systems

In millimeter-wave (MMW) multiple-input multiple-output (MIMO) communications, users and their corresponding base station (BS) have to align their beam during both initial access and data transmissions to compensate for the high propagation loss. The beam alignment (BA) procedure specified for 5th Generation (5G) New Radio (NR) has been designed to be fast and precise in the presence of non-malicious interference and noise. A smart jammer might exploit this weakness and may launch an attack during the BA phase in order to degrade the accuracy of beam selection and, thus, adversely impacting the end-to-end performance and quality-of-service experienced by the users. In this paper, we study the effects of a jamming attack at MMW frequencies during the BA procedure used to perform initial access for idle users and adaptation/recovery for connected users. We show that the BA procedure adopted in 5G NR is extremely vulnerable to a smart jamming attack and, consequently, we propose a countermeasure based on the idea of randomized probing, which consists of randomly corrupting the probing sequence transmitted by the BS in order to reject the jamming signal at the UE via a subspace-based technique based on orthogonal projections and jamming cancellation. Numerical results corroborate our theoretical findings and show the very satisfactory accuracy of the proposed anti-jamming approach.

Robust Design for RIS-Assisted Anti-Jamming Communications With Imperfect Angular Information: A Game-Theoretic Perspective

This paper utilizes a reconfigurable intelligent surface (RIS) to enhance the anti-jamming performance of wireless communications, due to its powerful capability of constructing smart and reconfigurable radio environment. In order to establish the practical interactions between the base station (BS) and the jammer, a Bayesian Stackelberg game is formulated, where the BS is the leader and the jammer acts as the follower. Specifically, with the help of a RIS-assisted transmitter, the BS attempts to reliably convey information to users with maximum utilities, whereas the smart jammer tries to interfere the signal reception of users with desired energy efficiency (EE) threshold. Since the BS and the jammer are not cooperative parties, the practical assumption that neither side can obtain the other's strategies is adopted in the proposed game, and the angular information based imperfect channel state information (CSI) is also considered. After tackling the practical assumption by using Cauchy-Schwarz inequality and the imperfect angular information by using the discretization method, the closed-form solution of both sides can be obtained via the duality optimization theory, which constitutes the unique Stackelberg equilibrium (SE). Numerical results demonstrate the superiority and validity of our proposed robust schemes over the existing approaches.

Dynamic Interference Management for UAV-Assisted Wireless Networks

We investigate a transmission mechanism aiming to improve the data rate between a base station (BS) and a user equipment (UE) through deploying multiple relaying UAVs. We consider the effect of interference incurred by another established communication network, which makes our problem challenging and different from the state of the art. We aim to design the 3D trajectories and power allocation for the UAVs to maximize the data flow of the network while keeping the interference on the existing communication network below a threshold. We utilize the mobility feature of the UAVs to evade the (un)-intended interference caused by (un)-intentional interferers. To this end, we propose an alternating-maximization approach to jointly obtain the 3D trajectories and the UAVs transmission powers. We handle the 3D trajectory design by resorting to spectral graph theory and subsequently address the power allocation through convex optimization techniques. We also approach the problem from the intentional interferer’s perspective where smart jammers chase the UAVs to effectively degrade the data flow of the network. We also extend our work to the case for multiple UEs. Finally, we demonstrate the efficacy of our proposed method through extensive simulations.

Time Allocation and Mode Selection for Secure Communications in Internet of Things

A joint optimization scheme of both time allocation and mode selection is proposed for defending smart jammer in the Internet of Things (IoT). Specifically, we employ multiple modes to deal with the potential eavesdropping and jamming attacks in wireless sensor networks (WSNs), respectively, as wireless-powered relaying (WPR), ambient backscatter relaying (ABR), friendly jamming (FJ) modes. Assuming a specific frame structure, time allocation between energy harvesting and transmission duration is first optimized for three modes based on transmission and security data maximization. In addition, considering that a single mode may fail to guarantee the performance demand due to the uncertainty of network conditions and adversary attacks, mode selection is carried out for adapting characteristics of different modes. Moreover, according to the available knowledge of jamming detection, for enhancing the robustness in secure transmission, the process of mode selection is further divided into two phases. Specifically, game theory is also utilized to achieve the Nash equilibrium for mode selection. Finally, numerical results verify the effects and exhibit the advantages of the proposed scheme.