Jamming Detection Research Articles

Multiple‐nodes based cooperative jamming detection for reactive follower jamming in ISAC

AbstractAccurate jamming detection is a prerequisite and important support for efficient anti‐jamming communication in integrated sensing and communications. In recent years, the reactive follower jamming, characterized by the high jamming efficiency and the difficulty of detection, is seen as the main opponent for frequency hopping spread spectrum communication, which poses a threat to the reliability of wireless communication. Based on the background, a multiple‐nodes based cooperative jamming detection scheme is proposed for accurately detecting the reactive follower jamming. Specifically, the secondary nodes send the average energy of the suspected attacked symbols to the primary user as the uplink data in each detection period, and then the detection result is provided by the detection threshold. The optimal detection threshold is derived for the primary user according to the uplink data and channel conditions. In addition, the theoretical probability of detection under the optimal threshold is derived, and the complexity of the scheme is analysed. Simulation results show that the proposed multiple‐nodes based cooperative jamming detection scheme for reactive follower jamming can effectively improve the probability of detection.

Stable route selection for adaptive packet transmission in 5G-based mobile communications

ABSTRACT The poor connectivity among mobile nodes introduces uncertainty in packet loss as the path link is not measured in this network. The focus is placed on communication cost to achieve valid packet transmission. Because the high-distance path selected for packet transmission incurs higher communication costs, it increases energy consumption and packet loss rate. So, the proposed dispersed path selection for communication (DPAC) method is constructed to obtain the best minimum distance routing path. This path operates with the help of queue variation is handled the data packets maintenance, and the time slot exceeds its limit. Packets are kept waiting to increase the packet broadcasting efficiency. A multipath jamming detection algorithm is constructed to provide a link-based path packet overload detection scheme to identify the packet overload. It also separates the path based on its characteristics to control overload. It reduces energy consumption and packet loss rate.

Bayesian Inference-Assisted Machine Learning for Near Real-Time Jamming Detection and Classification in 5G New Radio (NR)

Bayesian Inference-Assisted Machine Learning for Near Real-Time Jamming Detection and Classification in 5G New Radio (NR)

Reduction Jammer Detection and Recovery Algorithms for DSRC Safety Application in VANET

Intelligent Transportation Systems (ITS) encompasses technologies, services, and applications facilitating communication between vehicles (V2V) and between vehicles and fixed infrastructure (V2I and I2V). This mutual interaction constitutes a Vehicular Ad-Hoc Network (VANET) which supports a plethora of applications targeting critical transportation aspects, such as safety, mobility, and environmental considerations. Dedicated Short Range Communications (DSRC), operating on the 5.9 GHz band, is pivotal for such exchanges. We introduced an innovative algorithm designed to identify jamming attacks and transition the Safety Application to a secure fail-safe mode. This algorithm leverages a dual-metric strategy, incorporating both distance and PDR measurements. Field tests confirm that our algorithm adeptly recognizes the activities of deceptive jammers, ensuring a prompt shift of the safety application into its fail-safe state. This paper delves into these countermeasures, evaluating their efficiency via mathematical modeling, simulations, and on-ground testing. Findings acknowledge that these strategies bolster the reliability of safety applications in jamming scenarios. Furthermore, the approaches propounded align with ongoing standardization endeavors by relevant authorities, ensuring communication mediums remain unhindered.

Intelligent jamming detection‐based robust principal components analysis in communication system for security and defense

AbstractThreats to physical layer security from jamming attacks make wireless cognitive communication systems vulnerable. Global Positioning System signal is vulnerable to these attacks. Over the last decade, several types of jamming detection techniques have been proposed, antijamming‐based classical and machine learning (ML) techniques. Most of these techniques are inefficient in detecting jammers. Thus, there is a great need for efficient and quickest jamming detection technique‐based classifier using receiver operating characteristic (ROC) curve for different threshold values with high accuracy. In this work, we compare the efficiency of the proposed orthogonal distance (OD) and score distance (SD) method‐based robust principal component analysis (PCA) in ML classification in detecting jamming signals. Two hypotheses are proposed to distinguish between the presence and absence attack problem. Using this compressed data matrix obtained from modulated wideband converter (MWC) structure via centralized cooperation directly as input of the proposed classifier combined‐based ROC curve for real‐time detection scenarios. The performance of this proposed algorithm‐based robust PCA was evaluated and compared using the detection anomaly rate (DAR%), and false alarm rate (FAR%), area under curve (AUC), and accuracy. The performance of obtained results is good.

GNSS jamming detection using attention-based mutual information feature selection

Global navigation satellite systems (GNSS) are extensively utilized for military and civilian applications. Unfortunately, because of the signal weakness, GNSS is susceptible to interference, fading, and jamming, which reduces the position accuracy. Therefore, it would be beneficial to have a simple and highly accurate model for detecting the jamming signals to improve the GNSS receiver accuracy. In this paper, we propose a hybrid deep learning (DL) model for predicting jamming signals. Initially, we utilize a feature selection algorithm that combines mutual information (MI) with the minimal redundancy maximum relevance (mRMR) technique to identify the most crucial features. Subsequently, the model undergoes training using a soft attention-double-layer bidirectional long short-term memory (A-DBiLSTM) model. This particular model has shown outstanding performance in comparison to other DL models when applied to datasets collected from both kinematic and static jamming scenarios. To assess the effectiveness and efficiency of the proposed MI feature selection algorithm, we evaluate its performance through the calculation of confusion matrices and conducting numerical simulations. The simulation results of the A-DBiLSTM model demonstrate higher accuracy, precision, recall, and F1Score\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathrm {F1_{Score}}$$\\end{document} of 98.82%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$98.82\\%$$\\end{document}, 98.4%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$98.4\\%$$\\end{document}, 98.68%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$98.68\\%$$\\end{document}, and 98.36%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$98.36\\%$$\\end{document}, respectively. By employing the MI feature selection algorithm, dimensionality reduction is achieved. Moreover, the MI feature selection algorithm reduces 19%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$19\\%$$\\end{document} of the learning time with almost the same accuracy.

Federated Learning-Enabled Smart Jammer Detection in Terrestrial and Non-Terrestrial Heterogeneous Joint Sensing and Communication Networks

Federated Learning-Enabled Smart Jammer Detection in Terrestrial and Non-Terrestrial Heterogeneous Joint Sensing and Communication Networks

LSTM-Based Jamming Detection and Forecasting Model Using Transport and Application Layer Parameters in Wi-Fi Based IoT Systems

LSTM-Based Jamming Detection and Forecasting Model Using Transport and Application Layer Parameters in Wi-Fi Based IoT Systems

Intensive Interrupted Sampling Repeater Jamming Detection Based on Transformer-CFAR Fusion Detection Model

Intensive Interrupted Sampling Repeater Jamming Detection Based on Transformer-CFAR Fusion Detection Model

Federated Learning-Enabled Jamming Detection for Stochastic Terrestrial and Non-Terrestrial Networks

Federated Learning-Enabled Jamming Detection for Stochastic Terrestrial and Non-Terrestrial Networks

Securing the Future: A Resourceful Jamming Detection Method Utilizing the EVM Metric for Next-Generation Communication Systems

This paper addresses the escalating threat of malicious jamming in next-generation communication systems, propelled by their continuous advancement in speed, latency, and connectivity. Recognizing the imperative for communication security, we propose an efficient jamming detection method with distinct innovations and contributions. Motivated by the growing sophistication of jamming techniques, we advocate the adoption of the error vector magnitude (EVM) metric, measured in IQ symbols, deviating from traditional received signal strength and bit error rate-based measurements. Our method achieves enhanced jamming detection sensitivity, surpassing existing approaches. Furthermore, it introduces low complexity, ensuring resource-effective detection. Crucially, our approach provides vital jammer frequency information, enhancing counteraction capabilities against jamming attacks. It demonstrates stable results against varying system parameters, such as modulation type and code rate, thereby contributing to adaptability. Emphasizing practicality, the method seamlessly integrates into 5G and LTE systems without imposing additional overhead. Versatility is demonstrated through successful operations in diverse scenarios that are run by extended simulation conditions. Theoretical analysis substantiates these advantages, reinforcing the validity of our methodology. The study’s success is further validated through laboratory experiments, providing empirical evidence of its effectiveness. The proposed method represents a significant step toward fortifying next-generation communication systems against evolving jamming threats.

Federated Learning-Enabled Jamming Detection and Waveform Classification for Distributed Tactical Wireless Networks

In this paper, we propose a federated learning (FL)-based jammer detection and waveform classification algorithm for distributed tactical wireless networks (TWNs). More specifically, we consider a distributed TWN with multiple clusters and various types of waveforms used in the presence of a mobile jammer. We analyze the frequency domain of the waveforms received on local servers to extract the unique cyclic frequency from each waveform’s spectral correlation function (SCF). The method is used to detect the peak values in the frequency-cyclic frequency plane. The primary signal’s SCF exhibits peaks at the unique cyclic frequency and the zero cyclic frequency. These features are then used to train local convolutional neural networks (CNNs) to detect the jamming attacks and classify the waveforms. Moreover, a practical distributed TWN is considered in which each cluster head has a partial observation of the TWN with insufficient data samples, and the proposed algorithm exploits the distributed learning feature of FL, i.e., global learning aggregation to detect the existence of jammers and distinguish the types of waveforms received throughout the TWN. We implement a rigorous TWN simulation using MATLAB toolboxes and our proposed algorithm in TensorFlow Federated. The numerical results show that our proposed algorithm outperforms the standalone local SCF-CNN algorithm. We further demonstrate that the SCF feature yields more accuracy than the In-phase and Quadrature features.

Physical-Layer Jammer Detection in Multihop IoT Networks

Physical-Layer Jammer Detection in Multihop IoT Networks

Detection of Jamming Attacks for the Physical-Layer Authentication

This paper concerns the problem of defending against the jamming attack for the Physical-Layer Authentication (PLA). The problem is important due to the fact that jamming attacks can make the legitimate receiver reject a legitimate signal or accept an impersonating signal. In this paper, we propose two jamming detection schemes to defend against jamming attacks for the PLA. The first scheme is the Jamming-Attack Detection (JAD) scheme, which exploits the difference of noise variances between the handshaking and communication stages. The second scheme is the Composite Jamming-Attack Detection (CJAD) scheme, which exploits the difference of noise variances between Alice and Bob. Moreover, we provide the theoretical analysis of the proposed schemes over wireless fading channels and derive their closed-form expressions. We implement our schemes and conduct extensive performance comparisons. Our theoretical analysis and simulation results show that if security is the priority, the CJAD scheme is the best option. If we further consider the overhead, the JAD scheme is the best option under the strategy of jamming attacks and the CJAD scheme may be a better option under the strategy of enhanced jamming attacks, especially for the larger power of a jamming signal.

Robust data-driven design of a jamming detection filter for airborne electromechanical actuators

The electrification of the actuations on aircraft is a technological trend towards a greener aviation. Electromechanical Actuators (EMAs) are a key item in the more electric aircraft concept. However, EMAs are still prone to the jamming of their mechanical transmission, leading to possible incidents when flight surfaces can no longer be controlled. Jamming detection algorithms are thus envisaged. In this paper, we conduct a large experimental campaign on a 1:1 scale airborne EMA and design a robust data-driven jamming detection algorithm. The designed algorithm is compared with a traditional non-robust model-based jamming detection algorithm design. The benefits of robust data-driven approach are experimentally evaluated on real faulty data with several performance indexes.

Machine Learning-Based Jamming Detection for Safety Applications in Vehicular Networks: Individual Detection?

Intelligent transportation system (ITS) refers to advanced applications to make transportation safer and more intelligent. The dynamic and diverse natures of the system have been creating many challenges in ITS deployment and security. The progression in recent years of machine learning provides potentially strong methods to exploit data sources from transportation networks. Machine learning-based approaches promise to deal with various challenges in networks thanks to their ability to adapt to the changing network topology and network scale. This paper investigates the application of machine-learning models in jamming detection by analyzing how the approach works on individual observations of vehicles in different scenarios. We propose a machine learning-based approach that explores hidden rules of how the observation changes under a reactive jamming attack. Our proposed machine learning-based approach improves detection accuracy compared to existing methods. The performance of the proposed method depends closely on dataset selection. Though we evaluate our approach with synthetic data, the data are generated with justifiable simulations calibrated to match a real-referenced dataset. Our analysis provides a connection between the machine-learning domain and vehicular networks by utilizing specific domain techniques for jamming detection.

Machine-Learning-Assisted Cyclostationary Spectral Analysis for Joint Signal Classification and Jammer Detection at the Physical Layer of Cognitive Radio.

Cognitive radio technology was introduced as a possible solution for spectrum scarcity by exploiting dynamic spectrum access. In the last two decades, most researchers focused on enabling cognitive radios for managing the spectrum. However, due to their intelligent nature, cognitive radios can scan the radio frequency environment and change their transmission parameters accordingly on-the-fly. Such capabilities make it suitable for the design of both advanced jamming and anti-jamming systems. In this context, our work presents a novel, robust algorithm for spectrum characterisation in wideband radios. The proposed algorithm considers that a wideband spectrum is sensed by a cognitive radio terminal. The wideband is constituted of different narrowband signals that could either be licit signals or signals jammed by stealthy jammers. Cyclostationary feature detection is adopted to measure the spectral correlation density function of each narrowband signal. Then, cyclic and angular frequency profiles are obtained from the spectral correlation density function, concatenated, and used as the feature sets for the artificial neural network, which characterise each narrowband signal as a licit signal with a particular modulation scheme or a signal jammed by a specific stealthy jammer. The algorithm is tested under both multi-tone and modulated stealthy jamming attacks. Results show that the classification accuracy of our novel algorithm is superior when compared with recently proposed signal classifications and jamming detection algorithms. The applications of the algorithm can be found in both commercial and military communication systems.

Detection of Jamming Attacks via Source Separation and Causal Inference

Jamming attacks to hinder communication capabilities are becoming a critical aspect of wireless networks. A challenging issue is the detection of reactive jammers that perform spectrum sensing and attack the network only when legitimate communication is in progress. In this scenario, we introduce a novel framework for reactive jamming detection using a patrol of radio-frequency (RF) sensors external to the network to be protected. The solution relies on two key components: i) a novel underdetermined blind source separation (UBSS) method that, starting from the signal mixtures observed by the RF patrollers, is capable of separating the jamming temporal profile from the network nodes’ transmission profiles; ii) a new jamming detection based on causal inference called all-versus-one transfer entropy (AvOTE). The framework is then applied to a case study where the victim network is a Long Range (LoRa)-based internet of things (IoT) system with star topology. The solution outperforms a state-of-the-art method and an approach that attempts to find the causal relationship via time series correlation, exhibiting very good performance in the presence of shadowing. Indeed, a detection probability of 90% is achieved with a false alarm probability of 6% in the presence of nuisances such as collisions and severe shadowing.

Investigation on jamming detection in WSN using optimal decision rule

A jamming attack is a special case of a Denial of Service (DoS) attack that completely blocks the data transmission in Wireless Sensor Networks (WSNs). When sensor nodes are distributed in the field, numerous attacks, such as collision, black hole, selective forwarding, jamming, etc., caused by the presence of malicious nodes have the potential to cause network damage. Jamming is a highly risky attack that completely blocks data transmission within the wireless network. The existing technique for detecting jamming attacks are based on predetermined hopping-sequence, cryptographic, or random frequency hopping techniques. However, these mechanisms are more complex and frequently have energy constraints and high overhead. A novel jamming detection method based on a statistical approach that provides high network performance measures is proposed. It is a technique that uses energy-based clustering with a Received Signal Strength Indicator (RSSI). The selection of thresholds used for the detection of jamming is analyzed. The proposed approach employs three detection performance metrics for investigating the jamming attack, namely, Packet to Delivery Ratio (PDR), ENERGY, and RSSI. The jamming node is identified using the Optimal Decision Rule (ODR), which is determined by the hypothesis rule. If the hypothesis is not satisfied, then jamming exists; otherwise, there is no jamming. The novel technique is implemented using a Network Simulator, and various performance metrics such as PDR, Energy consumption, Network throughput, Routing overhead, network, and node lifetime are evaluated to conclude that the statistical approach outperforms the timestamp and IEWMA approaches.

A Hybrid Jamming Detection Algorithm for Wireless Communications: Simultaneous Classification of Known Attacks and Detection of Unknown Attacks

In this paper, we propose a novel machine learning based jamming detection algorithm that can classify known attacks used for training and detect unknown attacks not used for training. The proposed algorithm has a hybrid structure of simple classification and anomaly detection models, which are decision tree (DT) and isolation forest (IF), respectively. After a test data passes through a DT that only classifies the data as normal or one of the known attacks, it enters an IF algorithm that determines if the DT’s decision is indeed correct. Furthermore, an ensemble method is applied to reduce the deviation. The proposed algorithm is evaluated on real datasets from wireless modems operating in the C-band under static and mobile environments with a total of four types of jamming attacks. For the simultaneous classification and detection task, the proposed algorithm is shown to achieve superior performance over a baseline algorithm for all the cases of jamming distances, the number of known jamming attacks, and mobility scenarios.