Jamming Detection Research Articles

Deception Jamming Detection Based on Beam Scanning for Satellite Navigation Systems

A scheme for deception jamming detection based on beam scanning is proposed in this letter. This scheme detects the authenticity of the corresponding signal by forming a null in the direction of each visible satellite in turn. At the same time, the null direction is adjusted based on the attitude angle measured by the inertial unit to support the applicability of the scheme in scenarios where the attitude of the array changes continuously. Then, from an engineering point of view, an equalization scheme for arrays and radio frequency (RF) channels is proposed to improve the accuracy of the null direction. Finally, the effectiveness of the proposed scheme is verified by simulation.

완전 디지털 능동배열 레이다를 위한 빔 도메인 국부 적응 빔 형성 기법

Digital adaptive beamforming is applied to the radar system to ensure system performance in an interference environment. For real time application to an actual radar system with a large number of antenna elements, the dimensions of the adaptive beamforming coefficient must be reduced. In this paper, we propose a beam domain localized adaptive beamforming algorithm based on linear constrained minimum variance (LCMV) that considers system applicability while maintaining the performance of fully adaptive beamforming. Jammer detection and angle estimation are performed using a passive listening signal, and the dimensions are reduced by localizing the signal to the beam domain based on jammer information and pre-determined linear constraints. Estimation of the interference covariance matrix and adaptive beamforming are performed in the reduced beam domain to ensure real-time operation and applicability. To verify the performance of the proposed algorithm, we analyzed the results compared to the fully adaptive beamforming in terms of signal-to-interference plus noise ratio (SINR) loss and maximum side lobe level in the presence of a jamming signal.

Direction‐based jamming detection and suppression in mmWave massive MIMO networks

In this paper, we study the problem of physical layer security in the uplink of millimeter-wave massive multiple-input multiple-output (MIMO) networks and propose a jamming detection and suppression method. The proposed method is based on directional information of the received signals at the base station antenna array. The proposed jamming detection method can accurately detect both the existence and direction of the jammer using the received pilot signals in the training phase. The obtained information is then exploited to develop a channel estimator that excludes the jammer's angular subspace from received training signals. The estimated channel information is then used for designing a combiner at the base station that is able to effectively cancel out the deliberate interference of the jammer. By numerical simulations, we evaluate the performance of the proposed jamming detection method in terms of correct detection probability and false alarm probability and show its effectiveness when the jammer's power is substantially lower than the user's power. Also, our results show that the proposed jamming suppression method can achieve a very close spectral efficiency as the case of no jamming in the network

A Low-Cost Jamming Detection Approach Using Performance Metrics in Cluster-Based Wireless Sensor Networks.

Wireless Sensor Networks constitute an important part of the Internet of Things, and in a similar way to other wireless technologies, seek competitiveness concerning savings in energy consumption and information availability. These devices (sensors) are typically battery operated and distributed throughout a scenario of particular interest. However, they are prone to interference attacks which we know as jamming. The detection of anomalous behavior in the network is a subject of study where the routing protocol and the nodes increase power consumption, which is detrimental to the network’s performance. In this work, a simple jamming detection algorithm is proposed based on an exhaustive study of performance metrics related to the routing protocol and a significant impact on node energy. With this approach, the proposed algorithm detects areas of affected nodes with minimal energy expenditure. Detection is evaluated for four known cluster-based protocols: PEGASIS, TEEN, LEACH, and HPAR. The experiments analyze the protocols’ performance through the metrics chosen for a jamming detection algorithm. Finally, we conducted real experimentation with the best performing wireless protocols currently used, such as Zigbee and LoRa.

Jamming Detection in Broadband Frequency Hopping Systems Based on Multi-Segment Signals Spectrum Clustering

Accurate jamming detection in the broadband frequency hopping (FH) systems plays a vital role in improving the anti-jamming performance of the communication systems. The traditional jamming detection algorithms are prone to misjudge FH communication signals as jamming signals, leading to inaccurate jamming detection. Therefore, this paper proposes a jamming detection algorithm for broadband FH systems based on multi-segment signals spectrum clustering (MSSC). According to the difference in the time-frequency characteristics between FH signals and jamming signals, the proposed algorithm accurately detects the jamming by clustering the spectral components of signals in multiple periods. In addition, a jamming detector suitable for broadband FH systems is designed and the theoretical solution to the threshold factor of signal detection based on the Welch spectrum is also given in this paper. The simulation results show that the MSSC-based jamming detection algorithm proposed in this paper can reduce the jamming false detection probability and significantly improve the estimation accuracy of jamming parameters, effectively overcoming the defects of the traditional FFT-based energy detection and the multi-hop accumulation algorithms whose detection accuracy is sensitive to FH signals.

Reactive Jamming and Attack Mitigation over Cross-Technology Communication Links

Recently, Cross-Technology Communication (CTC), allowing the direct communication among heterogeneous devices with incompatible physical layers, has attracted much research attention. Many efficient CTC protocols have been proposed to demonstrate its promise in IoT applications. However, the applications built upon CTC will be significantly impaired when CTC suffers from malicious attacks such as jamming or sniffing. In this article, we implement a reactive jamming system, JamCloak, that can attack most existing CTC protocols. To this end, we first propose a taxonomy of the existing CTC protocols. Then based on the taxonomy, we extract essential features to train a CTC detection model, and estimate the parameters that can efficiently jam CTC links. Experimental results show that JamCloak consistently achieves 94.7% of classification accuracy on average in both Line-of-Sight and Non-Line-of-Sight scenarios. We also apply JamCloak to attack three existing CTC protocols: WiZig, Esense and EMF. Results show that JamCloak can significantly reduce PDR (packet delivery ratio) by 80.8% on average in practical environments. In the meantime, JamCloak’s jamming gain is more than 1.78× higher than the existing reactive jammer. In addition, we propose a practical countermeasure against reactive jamming attacks over CTC links like JamCloak. Results show that our approach significantly improves the jamming detection accuracy by 91.2% on average than the existing approach, and effectively decreases the reduction in packet delivery ratio to 1.7%.

GNSS jamming detection of UAV ground control station using random matrix theory

Global navigation satellite systems (GNSS) are the main navigation and control systems in unmanned aerial vehicles (UAVs) and their ground control stations. Without the GNSS signals, the UAV and its ground control stations cannot follow the waypoints of the desired path in jamming environments. In this paper, two new methods for detection of GNSS signal jamming attack for UAV ground control station are proposed based on random matrix theory. By using limiting distribution of mean vector and asymptotic behavior of the defined test statistic, a hypothesis test is introduced and evaluated to detect presence of jamming signal. Simulation results show that the proposed methods have significant performance in terms of detection and false alarm probabilities. Compared to existing methods, at low jamming-to-signal ratio (JSR), more than 2.5 dB improvement is achieved.

Jamming Detection based on Doppler Shift Estimation in Vehicular Communications Systems

Since Doppler shift is one of the most important parameters in wireless propagation, the evaluation of the Doppler shift at the base station (BTS) in vehicular communications improves BTS in many aspects such as channel varying rate, jamming detection, and handover operations. Therefore, in this study, we propose a novel method at a base station based on the received user signal to estimate the channel Doppler shift seen by BTS. Utilizing the inherent information existed in common receivers, a level crossing rate (LCR) based Doppler shift estimation algorithm is developed without any excessive hardware. Moreover, a jamming detection algorithm is improved based on the proposed Doppler shift estimation scheme. The performance of the proposed scheme is evaluated in a Terrestrial Trunked Radio (TETRA) network, and comprehensive experimental results have shown superior performance in a wide range of velocities, signal to noise ratios and jammers.

GPS L1 C/A 신호를 위한 심층신경망을 이용한 단일 연속파 재밍 검출기법

In this paper, a novel deep neural network-based single continuous wave jamming detection scheme is proposed for Global Positioning System (GPS) L1 Coarse/Acquisition (C/A) signals, which are used most frequently in civilian satellite-based navigation systems. The main environmental variables considered are as follows: The effective jamming power-to-noise spectral density ratio (J/N0) has a range of 34 dB-Hz to 64 dB-Hz; the center frequency and Doppler frequency of a single continuous wave jamming occur uniformly within ± 1 KHz and within ± 10 KHz, respectively, based on the center frequency of the GPS L1 C/A signal. The designed deep neural network consists of six hidden layers and one SoftMax layer. In order to assess the performance of the proposed scheme, the energy detection scheme that is frequently used for the detection of single continuous wave jamming at pre-correlation level is considered as a conventional scheme. When the false alarm probability is set to 10<SUP>-5</SUP> via simulation, it was confirmed that the detection probability of the proposed scheme is approximately 98% or higher, while that of the conventional scheme is approximately 78% at an effective J/N0 of 61 dB-Hz.

VULNERABILITY ANALYSIS OF EMERGENCY RESPONSE SYSTEM BASED ON NAVIGATIONAL UNITS IN CASE OF VEHICLE ACCIDENTS

When we focusing Emergency response system based on navigational units vulnerability to intentional interference and an opportunity incentive to attackers who wants to fool or impair Emergency response system based on navigational units especially smartphones and drones then Spoofing interference is a considerably powerful attack than jamming cause of jammer attacks block GNSS legitimate communication signals immediately on several receivers this arise mitigation strategy and counter measures for efficient jamming detection before damages are done. However, spoofing attack is most dangerous type of interference where produces GNSS like signals and fool receiver without interrupting GNSS operations where receiver navigation system is not able to do any counter measures until the fatal point. Positioning unit for all applications are not immune to this kind of attacks. Form a general perspective, a positioning unit is made of several sensors providing the actual position information, with the GNSS sensor playing a core role, being usually the only one providing an absolute estimation of the position, and others aiding or refining such information.

GNSS cloud‐data processing technique for jamming detection, identification, and localisation

Low-cost signal jammers, which are illegal in many countries but continue to be traded in the black market, emit weak global navigation satellite system (GNSS) jamming signals. This study proposes a method to detect, identify, and localise these signals based on a cloud-data processing technique in which the jamming detection, identification, and localisation are performed sequentially after gathering received signals from low-cost GNSS receivers distributed in a monitoring network. This technique consists of two steps: two-dimensional image correlation in the time-frequency domain for detection and identification, and signal correlation in the time domain and relevant processing for localisation. For this, technical descriptions of the proposed cloud-data processing method are first presented with various jamming scenarios. Then, numerical simulations are performed to evaluate the proposed method by generating satellite and jamming signals. The authors are thus able to verify the feasibility of the technique and conclude that, in practical applications, it would allow efficient and extensive monitoring of low-cost signal jammers.

AFRL: Adaptive federated reinforcement learning for intelligent jamming defense in FANET

The flying ad-hoc network (FANET) is a decentralized communication network for the unmanned aerial vehicles (UAVs). Because of the wireless nature and the unique network properties, FANET remains vulnerable to jamming attack with additional challenges. First, a decision from a centralized knowledge base is unsuitable because of the communication and power constraints in FANET. Second, the high mobility and the low density of the UAVs in FANET require constant adaptation to newly explored spatial environments containing unbalanced data; rendering a distributed jamming detection mechanism inadequate. Third, taking model-based jamming defense actions in a newly explored environment, without a precise estimation of the transitional probabilities, is challenging. Therefore, we propose an adaptive federated reinforcement learning-based jamming attack defense strategy. We developed a model-free Q-learning mechanism with an adaptive exploration-exploitation epsilon-greedy policy, directed by an ondevice federated jamming detection mechanism. The simulation results revealed that the proposed adaptive federated reinforcement learning-based defense strategy outperformed the baseline methods by significantly reducing the number of en route jammer location hop counts. The results also showed that the average accuracy of the federated jamming detection mechanism, leveraged in the defense strategy, was 39.9% higher than that of the distributed mechanism verified with the standard CRAWDAD jamming attack dataset and the ns-3 simulated FANET jamming attack dataset.

New Analytical Channel Allocation Methodology for Multichannel Multi-Radio Wireless Networks

In wireless technologies, jamming attack is a main Research problem in multichannel multi-radio as they block communication in wireless networks. Malicious nodes block legitimate communication through jamming attacks that causes intentional interference. This paper presents the problem of jamming attack in multichannel multi-radio wireless network. First, Allocated the channel in Randomized manner and measure the Entropy rate. Second, defined the channel capacity. Third determined noiseless channel. Furthermore, Find the responses from channel and compare Response channel (H(S/R)) with Noise less channel (T). If H(S/R) ∈ T, Then to select the channel otherwise to reject the channel and assign (H(S/R)) = Attack. Finally, Based on the jammer detection algorithm, the result is compared with cyclic spectral analysis. But the proposed algorithm computation process is very simple than cyclic spectral analysis algorithm and Cyclostationary.Based jammer Detection Algorithm.

Antijamming Improvement for Frequency Hopping Using Noise-Jammer Power Estimator

In frequency-hopping spread-spectrum (FHSS) systems, jammer detection and mitigation are important but difficult. Each slot of the FHSS experiences frequency-selective fading and unequal transceiver-frequency gains that hinder the detection of jammed slots and result in a poor bit-error rate (BER). To increase BER performance, we first propose a noise-jammer power estimator (NJPE) that estimates noise and jammer powers regardless of different channel gains, and derived its normalized Cramér–Rao bound (NCRB). Second, we developed a jammer detector based on gamma distribution, and designed a restoration method combining all nonjammed slots. Computer simulations verified the derived NCRB of the proposed NJPE by normalized mean squared error (NMSE), and showed that the jammer-detection probability of the proposed jammer detector was better than that of conventional detectors. The BER performance of the proposed method was also shown to be better than that of conventional methods.

Reliability refinement in VANET with hybrid jamming attacks using novel index based voting algorithm

Vehicular Adhoc Network (VANET) constitutes a basis for the Intelligent Transportation Systems. VANET can be extensively used in safety and non-safety applications. DSRC Safety purposes are essential usages in VANET, have the basic objective of improving and enhancing safety through notifying drivers in hazards or impending accidents. The dynamic changes in topology are frequent in VANET, resulting in several jamming attacks. Hybrid jamming attacks involve the combination of the features of constant, deceptive and random jamming attacks. In this research work a novel Index Based Voting Approach (IBVA) has been proposed to avoid hybrid jamming as an integral component of devoted short range communication safety applications in VANET. The aim of this work focuses on hybrid jamming detection strategy in terms of node centric misbehaviour detection. The authenticities of vehicular nodes were determined by a new Recursive Candidate Elimination Algorithm (RCEA). Experimental results show that the time required to make decisions 0.3 s, which is reduced decision time compared to previous algorithm reported in the literature. The proposed IBVA algorithm in VANET improves the reliability in terms of malicious nodes detection and reducing the decision which in turn improves the network performance.

RETRACTED ARTICLE: Routing technique for data transmission under jammer in multi-hop wireless network

The value and general execution of jammers create them an indispensable hazard for wireless sensor networks. In the multi-hop wireless network, jammers be able to obstruct the broadcast of consumer messages in the transitional nodes alongside the way, one may utilize jamming insensible routing after that occupy first layer mechanisms (e.g. spread spectrum) to restrain the jamming. The novel technique is introduced hybrid ERS-RIP (efficient route selection—routing information protocol) for data transmission under jammer in multi hop wireless sensor networks. Efficient route selection is to maintain minimum cost to identify the routes. For route selection the energy cost and trustable path must be measured. RIP is a distance vector routing protocol presence in the application of OSI layer and is to measure the optimal path between sources to destination. RIP gives updating occasionally regarding network exchange. When the jammers are found, then the network area nodes are choose alternate path with secure and monitor the energy of these path and the packets are send safely with the help of routing protocols to destination node. This algorithm explains in which provides the route based on the jammer detection path. This ERS-RIP algorithm works based on the quality of the optimal link. The performance of the proposed methodology is estimated by its end-to-end delay, throughput, packet delivery rate, packet loss rate and node life time. Results show that the performance of hybrid ERS-RIP is considerably improved than the existing algorithms ERS, ESA, and DSR. The Performance analysis of existing techniques is 80.5, 81.6, 83.4 and hybrid ERS-RIP is 85.5.

Artificial Intelligence Aided Electronic Warfare Systems- Recent Trends and Evolving Applications

Electronic warfare (EW) is one of the most important characteristics of modern battles. EW can affect a military force’s use of the electromagnetic spectrum to detect targets or to provide information. Recent developments in artificial intelligence (AI) suggest that this emerging technology will have a deterministic and potentially transformative influence on military power. AI driven algorithms can be very effective in diverse domain of EW like processing of radar signals for efficient recognition and classification of emitters, detection of jammer and its characteristics and for developing efficient anti-jamming algorithms. AI techniques can also enable an EW system to operate autonomously. This paper provides a description of various branches of EW, the role of AI in EW systems and different AI techniques that have been deployed in EW systems.

A Countermeasure against Smart Jamming Attacks on LTE Synchronization Signals

Long-Term Evolution (LTE) is one of the most frequently used wireless communication technology. As every wireless network, LTE is vulnerable to physical layer (PHY) jamming attacks due to the broadcast nature of channels. Since the jammer attacks are getting smarter and energy efficient, they can target a specific region or physical channel instead of entire band. Targeting the physical LTE downlink Synchronization Signals (SS) could be the most dangerous objective. In this paper, we investigate LTE PHY jamming attack against only primary and secondary synchronization signals. Jammer detection is performed by using Neyman-Pearson theorem. Then, a countermeasure method is proposed. Simulation results show that the proposed countermeasure can achieve lower pollution and better correct cell id performances during smart jamming attack against SS.

Rapid jamming detection approach based on fuzzy in WSN

SummaryIt is necessary for securing wireless sensor networks from jamming, since these networks are easily attacked by jammers. Jamming may be made by either external jammer node or internal node, which may become as an adversary in future. In this paper, two approaches are proposed in the cluster‐based sensor network to detect the maliciousness level of nodes to secure sensor networks from jamming attacks. First approach detects maliciousness level of nodes using two modules, namely, certification module and monitoring module. Certification module defends the network from the jammers. Monitoring module discovers the sensor nodes that are jammed by a jammer. Second approach uses fuzzy logic for optimizing the jamming metrics to determine the occurrence of jamming accurately. The proposed system achieves 99.58% detection ratio to determine node's maliciousness level.

Transmission Early-Stopping Scheme for Anti-Jamming Over Delay-Sensitive IoT Applications

Time-critical, wireless Internet of Things applications have been drawing increasing attention lately. The most characterizing feature of such applications is that a packet has to be delivered within a certain deadline. Not meeting the deadline could result in severe consequences. Jamming attacks utilize the shared nature of the wireless medium to corrupt transmitted packets. Although a corrupted packet could be retransmitted several times until successful delivery, this would add latency and hence could lead to missing the deadline. In this paper, we propose a jamming detection scheme that uses the packet transmission time as a statistic to make detection decisions. The key insight behind our proposed scheme is that, a long transmission/retransmission time for a certain packet indicates an abnormal condition, such as jamming. Therefore, we devise an optimal transmission-time threshold that, when exceeded, a jammer is detected. Unlike most existing detection schemes where, in case of a detection error, retransmission could continue until the deadline is reached, our scheme aims to detect the jammer earlier than the deadline so that the remaining time (until the deadline) could be utilized in retransmitting the packet over a safe channel. The proposed detection scheme is a general framework that can be applied to many situations. After conducting a thorough analysis, we apply the proposed early-stop jamming detection framework to the distributed coordinated function medium access mechanism specified by the 802.11 standard. Our simulation results show significant performance gains achieved by the proposed scheme.