Intelligent Jamming Research Articles

A multi-agent reinforcement learning-based collaborative jamming system: Algorithm design and software-defined radio implementation

In multi-agent confrontation scenarios, a jammer is constrained by the single limited performance and inefficiency of practical application. To cope with these issues, this paper aims to investigate the multi-agent jamming problem in a multi-user scenario, where the coordination between the jammers is considered. Firstly, a multi-agent Markov decision process (MDP) framework is used to model and analyze the multi-agent jamming problem. Secondly, a collaborative multi-agent jamming algorithm (CMJA) based on reinforcement learning is proposed. Finally, an actual intelligent jamming system is designed and built based on software-defined radio (SDR) platform for simulation and platform verification. The simulation and platform verification results show that the proposed CMJA algorithm outperforms the independent Q-learning method and provides a better jamming effect.

Jamming Attack Detection using Machine Learning Algorithms in Wireless Network

Due to technological improvements, a tremendous amount of data is generated every second. In the world of computing, data is being gathered constantly, from mouse clicks. In the system network, secure lines and servers must will be monitored. Such system were known as Intruder Detection System(IDS). A wireless network is the main target of such attacks, which result an undesirable denial of service. Fuzzy logic, game theory, channel surfing, and time series are several jamming detection methods that have been suggested over the past ten years. The majority of these methods are ineffective in locating intelligent jammers. Thus, effective, high-accurate and quick jamming detection approaches are highly required. For the purpose of detecting jamming attacks, a detailed analysis of certain ML algorithms including Random Forest, SVM, and NN, was performed and evaluate how well several machine learning algorithms detect jamming signals. In order to identify jamming signals, we looked into various signal characteristic types and produce a significant dataset and it will trained, analyzed, and tested using this dataset. The probability of detection, the probability of false alarm, the chance of miss detection, and accuracy were used to study and evaluate the performance of various algorithms. The simulation findings indicate that the random forest method used for jamming detection can identify jammers with high accuracy and detection probability, and low false alarm probability.

Efficient jamming attack against MIMO transceiver

This work addresses the issue of point-to-point intelligent jamming against MIMO architecture-based systems. The receiver uses an optimal Maximum Likelihood (ML) detector to extract the data stream from the received signal. Analyzing the receiver performance in the presence of a jamming signal, optimal attack strategies based on the pairwise error probability parameter as an objective function are designed, enabling the achievement of maximum degradation of the service quality. To perform the worst-case jamming of a legitimate transceiver, we assume that the jammer can eavesdrop on the Channel State Information (CSI). Depending on the jamming power budget constraint and the potential of jamming systems to estimate the CSI, different scenarios are envisaged, including imperfect CSI estimation cases. By minimizing the trace of Positive Semi-Definite (PSD) matrix products, the optimal jamming approaches that ensure maximum degradation of the legitimate link are designed. The disruption efficiency of the proposed approaches is comparable to the employment of the brute-force attack, but with a higher power level that is proportional to the number of jamming antennas in the case of partial knowledge of CSI, and inversely to the statistical expectation of the square root of the Wishart matrix's eigenvalues in the case of complete knowledge of CSI. To justify the relevance of the suggested policy, extensive simulations of the effect of these interventions are presented.

Enhanced Index Modulation-Based Frequency Hopping: Resist Power-Correlated Reactive Jammer

Reactive jammer has emerged as a relatively intelligent jamming with low energy cost and high jamming efficiency. Moreover, the jammer can obtain the power-correlated reactive jamming via the cooperation with a malicious user, which imposes a severe threat on the reliability of legitimate users. To solve this issue, this letter proposes an enhanced index modulation based frequency hopping spread spectrum (EIM-FHSS) scheme. Specifically, the strategy of power control in EIM-FHSS is provided where the main step of the strategy is formulated as a general optimization problem. Then we calculate a closed form solution for high signal to noise ratio (SNR), and propose a fast search algorithm for general case to obtain the approximate optimal power with low complexity. Simulation results show that EIM-FHSS can obtain enhanced anti-jamming performance as well as low-complexity compared with the benchmarks.

Security of Intelligent Sensors and Their Collaborative Roles in Human Resource Management in the Development of Smart Cities

The study is aimed at solving the security problems caused by malicious network attacks in human resource management in the development of smart cities. First, the Stackelberg game theory model is used to describe the interaction between intelligent sensors and intelligent jammers, and the security of the information physics system is effectively evaluated. Second, a denoise autoencoder machine model that can be used in human resource management with demographic information is proposed to ensure the security of intelligent sensors. Finally, its performance is simulated and analyzed. The results show that the more packets successfully arrive at the estimator, the more favorable the estimation effect of the estimator is. The designed defense strategy is very effective in protecting the security of intelligent sensors. When the number of nearest neighbor K increases, MAE of four datasets first decreases and then tends to be stable. With the increase of K , MAE of the algorithm proposed decreases from 0.8232 to 0.8095, 0.8086 to 0.7897, and 0.8563 to 0.8351, respectively. It decreases from 1.0169 to 1.0091 and then keeps stable. The recommendation algorithm proposed shows high accuracy in rating prediction and can quickly and effectively extract the hidden features of users and adjust the sparsity of data. Therefore, it can be applied to human resource management. This study provides a new idea for the research on the security of intelligent sensors and human resource management in the development of smart cities.

Intelligent Jamming Defense Using DNN Stackelberg Game in Sensor Edge Cloud

To ensure an accurate power allocation against increasing intelligent jamming attacks on the offloading link of computation tasks, we investigate interactions between a cluster head node and an intelligent jammer using a Stackelberg game framework, under the constraint of the total power to use and the limited knowledge of its own channel gain for each player. In this game, the intelligent jammer gathers channel gain information and processes it using a deep neural network (DNN) to infer the accurate jamming power as an attack strategy. The cluster head node also exploits DNN to infer an accurate transmission power as a defense strategy according to the varying channel gain. We model the optimization of the attack and defense strategies using single channel jamming DNN (SJnet), multiple channel jamming DNN (MJnet), single channel sensor DNN (SSnet), and multiple channel sensor DNN (MSnet) for the single (multiple) channel jamming attacks. In addition, we extend the design to the scenario where the intelligent jammer can launch a hybrid mode jamming attack, and propose a DNN Stackelberg game-based defense scheme. Numerical simulation results demonstrate that our proposed mechanism is superior to other power allocation mechanisms under different scenarios in the sensor edge cloud.

Entice to Trap: Enhanced Protection against a Rate-Aware Intelligent Jammer in Cognitive Radio Networks

Anti-jamming in cognitive radio networks (CRN) is mainly accomplished using machine learning techniques in the domains of frequency, coding, power and rate. Jamming is a major threat to CRN because it can cause severe performance damage such as network isolation, network application interruption and even physical damage to infrastructure simple radio devices. With the improvement in communication technologies, the capabilities of adversaries are also increased. The intelligent jammer knows the rate at which users transmit data, which is based on the attractiveness factor of each user. The higher the data rate for a secondary user, the more attractive it is to the rate-aware jammer. In this paper, we present a dummy user in the network as a honeypot of the jammer to get the jammer’s attention. A new anti-jamming deceiving theoretical method based on rate modifications is introduced to increase the bandwidth efficiency of the entire cognitive radio-based communication system. We employ a defensive anti-jamming deception mechanism of the Pseudo Secondary User (PSU) to as an entice to trap the attacker by providing thus enhanced protection for the rest of the network from the impact of the attacker. Our analytical simulation results show a significant improvement in performance using the proposed solution. The utility of the proposed intelligent anti-jamming algorithm lies in its applications to support the secondary wireless sensor nodes.

The impact of intentional interference on the performances of ML detector in MIMO systems

Here we address the problem of performing the resilience of Multiple Input Multiple Output (MIMO) architecture against intentional and unintentional interferences. We investigate the performance of a non-linear receiver based on the Maximum Likelihood (ML) detector in MIMO systems over Gaussian fading channels in the presence of interfering signals. Using the properties of the Gaussian matrix, a finite expression of the Probability Density Function (PDF) for the Signal to Interference plus Noise Ratio (SINR) is obtained as a function of the brute jammer power budget and the number of affected antennas. By considering a particular closed-form of intelligent jamming strategies against MIMO architecture presented in the literature, approximated upper limits of the Bit Error Rate (BER) are performed under different jamming scenarios depending on the Channel State Information (CSI) availability. These results enable us to characterize the consequences of such conflicting attacks on the quality of the legitimate link. Furthermore, extensive simulations are carried out to justify the performance of the ML detector and validate the obtained results.

A hidden anti-jamming method based on deep reinforcement learning

Most of the current anti-jamming algorithms for wireless communications only consider how to avoid jamming attacks, but ignore that the communication waveform or frequency action may be obtained by the jammers. Although existing anti-jamming methods can guarantee temporary communication effects, the long-term performance of these anti-jamming methods may be depressed when intelligent jammers are capable of learning from historical communication activities. Aiming at this issue, a hidden anti-jamming method based on the idea of reducing the jammer's sense probability is proposed. Firstly, the sensing probability of the jammer is obtained by calculating the correlation between the actions of the jammer and the user. Later, a deep reinforcement learning framework is designed, which aims at not only maximizing the communication throughput but also minimizing the action's correlation between the jammer and the user. Finally, a hidden anti-jamming algorithm is proposed, which links the instantaneous return with the communication quality of users and the correlation between users and jammer. The simulation result shows that the proposed algorithm not only avoids being sensed by the jammer but also improves its anti-jamming performance compared to the current algorithm that only considers jamming avoidance.

A smart reactive jamming approach to counter reinforcement learning‐based antijamming strategies in GEO SATCOM scenario

SummaryReinforcement learning (RL) is being considered for future SATCOM systems due to its inherent capability to self‐learn the optimum decision‐making policy under different scenarios. This capability enables SATCOM systems to manage their resources judiciously and mitigate jamming attacks autonomously without prior jammer type classification. We propose a novel smart reactive SATCOM jamming approach that would not only counter these RL based anti‐jamming strategies but would also be effective against conventional anti‐jamming schemes, that is, FHSS and DSSS. The proposed jamming approach exploits the limitations in learning patterns of Q‐learning‐based RL agent and achieves effective jamming while conserving considerable amount of jamming power. To achieve this, we propose an intelligent jamming engine (IJE) along with few potent jamming algorithms and evaluate their performance in terms of throughput degradation of victim SATCOM link, jamming power conservation, and design complexity of the jammer. Software simulations successfully demonstrate the effectiveness of our proposed smart reactive jamming approach which outperforms the standard reactive jammer against RL‐based antijamming schemes.

A double network structure anti-jamming algorithm based on deep reinforcement learning

At this stage, when dealing with intelligent jammer with environmental cognition, most anti-jamming methods only consider how to maximize avoidance of it. However, the user’s previous waveform and frequency actions have been leaked. As the intelligent jammer continues to learn, the performance of anti-jamming will decline. Existing literature has proposed the idea of hidden anti-jamming. The user uses rational communication action-making to prevent the jammer from obtaining the information of the user so that the jammer cannot target the user for jamming, but its model is relatively simple and cannot cope with complex jamming types. This paper designs a kind of decision related judgment module between jammer and user based on Generative Adversarial Networks (GAN), which uses the environment state received by the user to reversely infer the user’s decision whether to avoid jammer’s sense. Specifically, the neural network is used to fit the environment state under known user information, and calculate the loss between it and the real environment state to evaluate whether the user’s decision is to avoid jammer’s sense. Then, this paper proposes a deep reinforcement learning algorithm with double-network structure, which can deal with various types of complex jamming while ensuring anti-jamming performance. The final simulation results show that the improved deep reinforcement learning algorithm proposed in this paper can improve the anti-jamming performance, achieve hidden anti-jamming, and have a wider range of applications.

Exploring UAV's multi-domain joint anti-jamming intelligent decision algorithm

To understand the complex communication environment of a UAV in battlefield, its unknown channel statistics information and poor intelligent jamming and anti-jamming capability, the multi-domain anti-jamming problem is studied, and a multi-domain joint anti-jamming intelligent decision algorithm is proposed. First, the channel selection method is adopted to deal with jamming in the frequency domain. A multi-arm slot machine's channel selection model is established, and the channel interference level is judged. Secondly, the moderate interference channel is suppressed in the power domain, and the model of its Stackelberg game is established. The game equalization is solved to obtain the best transmission power and reduce the overhead caused by channel switching. The simulation results show that the long-term rewards of the intelligent decision algorithm are significantly higher than those of the traditional multi-arm slot machine's algorithm and the random selection algorithm and that the average throughput of the communication system of the UAV is improved, thus proving the superiority of the intelligent decision algorithm.

Reinforcement learning based a non-zero-sum game for secure transmission against smart jamming

Smart jammer and smart anti-jammer have always been attacked and defensed in a contradictory way. In fact, there exists fundamental trade-off between all evolved parties. It is well known that only through interactive training with powerful opponents can the strategy optimization ability in actual combat be improved. In the process of electronic countermeasures between the communication system and the jammer, the traditional electronic warfare attacker uses an open-loop jamming method, that is, the opponent's information cannot be obtained after the jamming, which greatly reduces the combat effect. In order to improve the strategic optimization capabilities of the combatant, cognition and intelligence are introduced into electronic countermeasures. In this paper, we start from modeling a non-zero-sum game, and analyze the Nash equilibrium (NE) of the static secure game and the conditions for its existence. Then, we design a multi-agent reinforcement learning framework with a optimal power control strategy in the dynamic game between the smart jammer and the trained base station (BS). Finally, due to the non-cooperative hostile relationship between the two sides in the actual combat, we add the eavesdropping function and the jamming effect evaluation modular to build a cognitive closed-loop. The experiment shows that the intelligent jammer with the eavesdropping function can seriously reduce the performance of the interfered communication party. However, the intelligent BS after training can effectively combat smart jamming. It can be demonstrated that confrontation training can improve the intelligence level of agents.

Anti-Jamming Game to Combat Intelligent Jamming for Cognitive Radio Networks

Cognitive Radio (CR) provides a promising solution to the spectrum scarcity problem in dense wireless networks, where the sensing ability of cognitive users helps acquire knowledge of the environment. However, cognitive users are vulnerable to different types of attacks, due to its shared medium. In particular, jamming is considered as one of the most challenging security threats in CR networks. In jamming, an attacker jams the communication by transmitting a high power noise signal in the vicinity of the targeted node. The jammer could be an intelligent entity that is capable of exploiting the dynamics of the environment. In this work, we provide a machine-learning-based anti-jamming technique for CR networks to avoid a hostile jammer, where both the jamming and anti-jamming processes are formulated based on the Markov game framework. In our framework, secondary users avoid the jammer by maximizing its payoff function using an online, model-free reinforcement learning technique called Q-learning. We consider a realistic mathematical model, where the channel conditions are time-varying and differ from one sub-channel to another, as in practical scenarios. Simulation results show that our proposed approach outperforms existing approaches to combat jamming over a wide range of scenarios.

Design and implementation of reinforcement learning‐based intelligent jamming system

Here the intelligent jammer issue is studied. With the rapid development of cognitive radio technology, current cognitive terminals can adaptively or intelligently switch channel by spectrum sensing and decision-making. Most of the traditional jamming methods, such as swept jamming and comb jamming, generally work in a relatively fixed pattern, which are not able to effectively jam the terminals empowered with cognition and spectrum decision-making capability. In view of this problem, the authors propose an intelligent jamming decision-making system based on reinforcement learning. First, in order to jam a pair of transmitter and receiver with adaptive frequency hopping capability, a jammer with spectrum sensing, offline training and learning scheme is proposed. Second, a reinforcement learning-based algorithm for jamming decision-making is proposed and simulated. A special feature of the proposed scheme is that considering the reward is difficult to obtain in the actual communication system, a virtual jamming decision-making method is used to enable the jammer to learn and jam efficiently without the user's prior information. Finally, the proposed jamming model and algorithm are implemented and verified on Universal software radio peripheral testbed.

Development of Frequency GSM Signal Jammer

This paper gives an explanation to the concept of mobile Jamming and explores jamming in the two popular mobile networks: Global system for mobile communication (GSM) and digital cellular network (DCS). The designed intelligent jamming system blocks the controller channel only, also it operates only if an active mobile is in the controlled area. The ADS - advance Design System for Agilent software package is used analyzed and simulated the mobile jamming system. The Mobile Detector Phone Jammer successfully jammed all the four operators but the radius of the range did not get as expected in the designed, this project only focuses on blocking the signal transmission between the ranges 935 to 960 MHz when tested and performed well. This can be effectively find its use in School Examination hall and religion worship centers. Keywords—Jammer, Mobile Phone, Network, Signal, Frequency

Two-Way Gaussian Networks With a Jammer and Decentralized Control

We consider the existence and structure of (zero-sum game) Nash equilibria for a two-way network in the presence of an intelligent jammer capable of tapping the channel signals in both directions. We assume that the source and channel noise signals are all Gaussian random variables, where the source signals are independent of each other, while the noise signals are arbitrarily correlated. We show that for fixed jammer power constraints, a Nash equilibrium exists with respect to the system wide mean square error, and equilibrium jamming policies are always Gaussian. We derive the equilibrium policies in closed form under various system parameters. Finally for two system scenarios, we analytically determine the optimal power allocation levels the jammer can deploy in each channel link, when allowed to operate under an overall power constraint.

On the Performance of Deep Reinforcement Learning-Based Anti-Jamming Method Confronting Intelligent Jammer

With the development of access technologies and artificial intelligence, a deep reinforcement learning (DRL) algorithm is proposed into channel accessing and anti-jamming. Assuming the jamming modes are sweeping, comb, dynamic and statistic, the DRL-based method through training can almost perfectly avoid jamming signal and communicate successfully. Instead, in this paper, from the perspective of jammers, we investigate the performance of a DRL-based anti-jamming method. First of all, we design an intelligent jamming method based on reinforcement learning to combat the DRL-based user. Then, we theoretically analyze the condition when the DRL-based anti-jamming algorithm cannot converge, and provide the proof. Finally, in order to investigate the performance of DRL-based method, various scenarios where users with different communicating modes combat jammers with different jamming modes are compared. As the simulation results show, the theoretical analysis is verified, and the proposed RL-based jamming can effectively restrict the performance of DRL-based anti-jamming method.

A heterogeneous information fusion deep reinforcement learning for intelligent frequency selection of HF communication

The high-frequency(HF) communication is one of essential communication methods for military and emergency application. However, the selection of communication frequency channel is always a difficult problem as the crowded spectrum, the time-varying channels, and the malicious intelligent jamming. The existing frequency hopping, automatic link establishment and some new anti-jamming technologies can not completely solve the above problems. In this article, we adopt deep reinforcement learning to solve this intractable challenge. First, the combination of the spectrum state and the channel gain state is defined as the complex environmental state, and the Markov characteristic of defined state is analyzed and proved. Then, considering that the spectrum state and channel gain state are heterogeneous information, a new deep Q network (DQN) framework is designed, which contains multiple sub-networks to process different kinds of information. Finally, aiming to improve the learning speed and efficiency, the optimization targets of corresponding sub-networks are reasonably designed, and a heterogeneous information fusion deep reinforcement learning (HIF-DRL) algorithm is designed for the specific frequency selection. Simulation results show that the proposed algorithm performs well in channel prediction, jamming avoidance and frequency channel selection.

A design and development of an intelligent jammer and jamming detection methodologies using machine learning approach

Nowadays, the utilization of mobile phones has increased rapidly. Due to this evolution schools, mosques, prisons, etc., require silence and security. This is achieved by using the mobile phone jammers. An intelligent mobile jammer is designed for allowing only the emergency calls by utilizing the microcontroller. Here, the jammer utilizes the successive approximation for reducing the transmission power. However, it requires few modifications. Therefore in this paper, an improved successive approximation based on divide and conquer algorithm is proposed in order to design the improved intelligent mobile jammer and reduce transmission power. Subsequently, the proposed jammer is analysed based on the different scenarios and frequency bands to illustrate their performance effectiveness. Furthermore, the normal activities are distinguished from jamming by using machine learning-based detection system according to the various parameters. Finally, the proposed algorithm is compared with conventional algorithms to demonstrate its performance efficiency in terms of detection accuracy.