Jamming Detection Research Articles

Efficiency and Detectability of Random Reactive Jamming in Carrier Sense Wireless Networks

A natural basis for the detection of a wireless random reactive jammer (RRJ) is the perceived violation by the detector (typically located at the access point (AP)) of the carrier sensing protocol underpinning many wireless random access protocols (e.g., WiFi). Specifically, when the wireless medium is perceived by a station to be busy, a carrier sensing compliant station will avoid transmission while a RRJ station will often initiate transmission. However, hidden terminals (HTs), i.e., activity detected by the AP but not by the sensing station, complicate the use of carrier sensing as the basis for RRJ detection since they provide plausible deniability to a station suspected of being an RRJ. The RRJ has the dual objectives of avoiding detection and effectively disrupting communication, but there is an inherent performance tradeoff between these two objectives. In this paper we capture the behavior of both the RRJ and the compliant stations via a parsimonious Markov chain model, and pose the detection problem using the framework of Markov chain hypothesis testing. Our analysis yields the receiver operating characteristic of the detector, and the optimized behavior of the RRJ. While there has been extensive work in the literature on jamming detection, our innovation lies in leveraging carrier sensing as a natural and effective basis for detection.

Intelligent ECCM technology via cognition and agility for the airborne radar

The electronic counter countermeasures (ECCM) technology is crucial to the modern airborne radar. In this study, opposed to the intelligent electronic countermeasure system, an intelligent anti-jamming technology is developed, which adjusts the working parameters and ECCM steps in real time and optimises the radar performance in various jamming scenarios. The framework of the proposed intelligent anti-jamming technology includes the cognitive jamming detection and suppression, adaptive frequency agility, and waveform diversity. The application of the proposed technology in the airborne radar is analysed via numerical simulations.

Detection of Jamming Attack in Non-Coherent Massive SIMO Systems

In recent studies, a simple non-coherent communication scheme based on energy detection is proposed in massive single-input multiple-output (SIMO) systems. Before data transmission, the transmitter sends pilots to the receiver for the purpose of estimating the channel statistics. However, this training phase unintentionally provides opportunity for a malicious jammer to attack legitimate communication. In order to secure the legitimate communication, this paper proposes a jamming detection method in non-coherent SIMO systems, in which the information of channel statistics is not required. First, the transmitter sends pilots to the receiver, then the receiver sends the conjugate of its received signal (which may contain jammer signal) back to the transmitter, where the final decision on jamming detection is made. According to the likelihood ratio test principle, two detectors based on variance and standard variance normalization are proposed. The performance analysis indicates that these two detectors are of similar detection performance but of different complexity. Furthermore, it is revealed that the probability of detection initially grows with the number of receive antennas but converges quickly then, whereas the channel statistics from the jammer to the receiver always greatly influences the performance. Finally, the numerical simulations are carried out to validate the proposed detection method.

Detection of Jamming Attacks in a Cluster WSN using Statistical Approach

In this paper, a new jamming detection approach for Wireless Sensor Networks is produced in the hostile environment. Significantly there are many types of attacks are present which damage the sensor nodes in the network. Among these jamming is a highly risky attack which completely block data transmission in the wireless network. As the Wireless Sensor Networks are mainly applied in the place where crisis occurs and in war field applications, consistent and secure transmission are major things which help for success of communication. Therefore it is necessary to detect and eliminate the jamming attack immediately for the successful mission on WSN. Various approaches are proposed for the detection and mitigation of jamming attacks, but the proposed methods are not suitable and unusable because of certain limitations. A novel mechanism for the detection of jamming in WSN is proposed, which is a statistical approach based on the network performance parameters. The proposed approach employs several network performance parameters, which differentiates our approach from most existing detection algorithms, since three parameters such as PLR, ENERGY, and RSSI is commonly used as a detection decision. The simulation model of proposed detection algorithm is implemented in ns-2 network simulator.

Jamming detection approach based on fuzzy assisted multicriteria decision‐making system for wireless sensor networks

SummaryThe jamming detection approach based on fuzzy assisted multicriteria decision‐making system (JDA) is proposed to detect the presence of jamming in downstream communication for Cluster based Wireless Sensor Network (CWSN). The proposed approach is deployed in cluster head (CH). The JDA functions in two aspects: First, the CH periodically measures the jamming detection metrics namely Packet Delivery Ratio (PDR) and Received Signal Strength Indicator (RSSI) of every node in the cluster to determine the behavior of the sensor nodes. In order to determine the behavior of members in the cluster, the CH compares the measured PDR with the PDR threshold. If the measured PDR is lesser than the PDR threshold, then CH applies the TOPSIS method on the PDR and RSSI metrics to determine the presence of jamming. These metrics are considered as the criteria and the nodes or the members are considered to be the alternatives. Next, the fuzzy logic is applied on the results obtained from the TOPSIS method to optimize the jamming detection metrics and identify the presence of jamming accurately. The proposed jamming detection approach detects well and arrives at 99.6% jamming detection rate as shown in simulation.

GPS 재밍탐지를 위한 기계학습 적용 및 성능 분석

GPS 재밍탐지를 위한 기계학습 적용 및 성능 분석

On the Evaluation of the Total-Cost-of-Ownership Trade-Offs in Edge vs Cloud Deployments: A Wireless-Denial-of-Service Case Study

We are witnessing an explosive growth in the number of Internet-connected devices and the emergence of several new classes of Internet of Things (IoT) applications that require rapid processing of an abundance of data. To overcome the resulting need for more network bandwidth and low network latency, a new paradigm has emerged that promotes the offering of Cloud services at the Edge, closer to users. However, the Edge is a highly constrained environment with limited power budget for servers per Edge installation which, in turn, limits the number of Internet-connected devices, such as sensors, that an installation can service. Consequently, the limited number of sensors leads to a reduction in the area coverage provided by them and puts in question the effectiveness for deploying IoT applications at the Edge. In this paper, we investigate the benefits of running an emerging security focused IoT application, (jamming detection), at the Edge vs. the Cloud by developing a Total Cost of Ownership (TCO) model, which considers the application's requirements as well as the Edge's constraints. For the first time, we build such a model based on realistic performance and energy-efficiency measurements obtained from commodity 64-bit ARM based micro-servers that are excellent candidates for supporting Cloud services at the Edge. Such servers represent the type of devices that can provide the right balance between power and performance, without requiring any complicate cooling and power supply infrastructure, which will not be available at the de-centralized deployments. Aiming at improving the energy efficiency, we exploit the pessimistic design margins adopted conventionally in such devices and investigate their operation under lower than nominal supply voltage and memory refresh-rate. Our results show that the jamming detection application deployed at an Edge environment is superior to a Cloud based solution by up to 2.13 times in terms of TCO. Moreover, when servers operate below nominal conditions, we can achieve up to 9 percent power savings which enables in several situations 100 percent gains in the TCO/area-coverage metric, i.e double area can be served with the same TCO.

A jamming detection method for multi-hop wireless networks based on association graph

In this paper, a jamming detection algorithm based on association graph is put forward based on the observation that different jamming attacks will cause different network status changes in multi-hop wireless networks (MHWNs). The proposed algorithm consists of two phases, i.e., learning and detection phases. At the learning phase, different symptoms are extracted through learning from various samples collected in both jamming and jamming-free scenarios. Then, a symptom-attack association graph is built. At the detection phase, the association graph is adopted to detect the jamming attacks that lead to the observed symptoms. A series of simulation experiments on NS3 have validated that the proposed method can efficiently detect and classify typical jamming attacks, including reactive, random and constant jamming attacks.

A jamming detection method for multi-hop wireless networks based on association graph

A jamming detection method for multi-hop wireless networks based on association graph

An adaptive neuro-fuzzy logic based jamming detection system in WSN

Wireless sensor network (WSN) is employed in variety of applications ranging from agriculture to military. WSN is vulnerable to various security attacks, in which jamming attacks obstruct and disturb the exchange of information between sensor nodes in WSN by transmitting signals to jam legitimate transmission to cause a denial of service. Hence, it is essential to secure the sensor networks from jamming attacks. In this paper, two approaches: fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS)-based jamming detection system are proposed for detecting the presence of jamming by computing two jamming detection metrics, namely, packet delivery ratio and received signal strength indicator. FIS approach is based on Takagi–Sugeno fuzzy logic which optimizes the jamming detection metrics. ANFIS approach combines fuzzy logic and learning ability of the neural network to optimize the metrics for detecting various types of jamming. The proposed approaches are compared with existing system and themselves. The simulation result shows that the proposed ANFIS approach detects the jamming attacks as high as true detection ratio.

Deceptive Jamming Detection for SAR Based on Cross-Track Interferometry

Deceptive jamming against synthetic aperture radar (SAR) can create false targets or deceptive scenes in the image effectively. Based on the difference in interferometric phase between the target and deceptive jamming signals, a novel method for detecting deceptive jamming using cross-track interferometry is proposed, where the echoes with deceptive jamming are received by two SAR antennas simultaneously and the false targets are identified through SAR interferometry. Since the derived false phase is close to a constant in interferogram, it is extracted through phase filtering and frequency detection. Finally, the false targets in the SAR image are obtained according to the detected false part in the interferogram. The effectiveness of the proposed method is validated by simulation results based on the TanDEM-X system.

Signal Classification and Jamming Detection in Wide-Band Radios Using Naïve Bayes Classifier

This correspondence proposes a new technique for signal classification and jamming detection in wide-band (WB) radios. Theory of compressed sensing is exploited to recover the sparsely populated WB spectrum from sub-Nyquist samples, thus reducing the very high-rate sampling requirements at the receiver analog to digital converter. From the recovered WB, key spectral features of each narrow-band (NB) signal are extracted. These spectral features are then used to train a simple yet powerful classifier, the naive Bayes classifier (NBC). The trained NBC is then used not only to classify various NB signals into their respective modulations but also to detect the jamming on different NB signals, which are the main contributions of this letter. The proposed algorithm is then evaluated under different empirical setups and is shown to perform better when compared to a recently proposed feature-based jamming detection algorithm.

Jamming attack detection in a pair of RF communicating vehicles using unsupervised machine learning

Wireless radio frequency (RF) jamming, both intentional and unintentional, poses a serious threat for wireless networks and wireless communications in general. Vehicular ad-hoc networks (VANET) are a subset of the wireless networks that incorporate modern safety-critical applications, that are vulnerable to jamming attacks. To preserve the secure communication and to increase its robustness against that type of attacks, an accurate detection scheme must be adopted. In this paper we present a jamming detection approach for wireless vehicular networks that leverages the use of unsupervised machine learning. The proposed method, utilizes a new metric, that is the variations of the relative speed between the jammer and the receiver, along with parameters that can be obtained from the on-board wireless communication devices at the receiver vehicle. Through unsupervised learning with clustering, we are able to differentiate intentional from unintentional jamming as well as identify the unique characteristics of each jamming attack. The proposed method is applied to three different real-life scenarios with extensive simulations being presented.

Detection and elimination method for deception jamming based on an antenna array

A global positioning system is an important way of locating an aircraft, while deception jamming can affect the positioning accuracy of such navigation. Considering this, a detection and elimination method for deception jamming is proposed based on a specially designed array for the aircraft. The jamming is detected by comparing the double-difference observation of the carrier phases of two different signals to a certain threshold derived according to the measurement errors of the receiver. To estimate the jamming direction with high accuracy, meanwhile considering the feasibility of airborne installation, a novel configurated array combining medium-length baseline with short baseline is designed, and a fast method to solve the integer ambiguity is discussed. After jamming detection, the nulling of the array beam is pointed to the jamming source through the orthogonal vector weighting to suppress jamming. The validity of the method is verified by computer simulations.

Fuzzy logic–based jamming detection algorithm for cluster‐based wireless sensor network

SummaryIn this paper, a fuzzy logic–based jamming detection algorithm (FLJDA) is proposed to detect the presence of jamming in downstream data communication for cluster‐based wireless sensor networks. The proposed FLJDA keeps an eye on the existing nodes and new node to determine their behavior by applying fuzzy logic on measured jamming detection metrics. To monitor the behavior of the nodes, the FLJDA computes the jamming detection metrics, namely, packet delivery ratio and received signal strength indicator. The major features of this paper are the following: (1) The jamming detection algorithm is specifically implemented for downstream data communication, (2) cluster head estimates jamming detection metrics for detecting the jamming unlike the existing algorithms where individual nodes explicitly collect the jamming detection metrics, and (3) the proposed algorithm optimizes the jamming detection metrics on the basis of fuzzy logic unlike the existing approaches, which uses merely jamming detection threshold alone for jamming detection. The simulation results of the proposed system provide the true detection ratio as high as 99.89%.

Jamming Detection in Massive MIMO Systems

This letter considers the physical layer security of a pilot-based massive multiple-input multiple-output (MaMIMO) system in presence of a multi-antenna jammer. We propose a new jamming detection method that makes use of a generalized likelihood ratio test over some coherence blocks. Our proposed method utilizes intentionally unused pilots in the network. The performance of the proposed detector improves by increasing the number of antennas at the base station, the number of unused pilots and also by the number of the coherence blocks that are utilized. Simulation results show that in the MaMIMO regime, perfect detection is achievable even with a small number of unused pilots.

AI-Based Malicious Network Traffic Detection in VANETs

Inherent unreliability of wireless communications may have crucial consequences when safety-critical C-ITS applications enabled by VANETs are concerned. Although natural sources of packet losses in VANETs such as network traffic congestion are handled by decentralized congestion control (DCC), losses caused by malicious interference need to be controlled too. For example, jamming DoS attacks on CAMs may endanger vehicular safety, and first and foremost are to be detected in real time. Our first goal is to discuss key literature on jamming modeling in VANETs and revisit some existing detection methods. Our second goal is to present and evaluate our own recent results on how to address the real-time jamming detection problem in V2X safety-critical scenarios with the use of AI. We conclude that our hybrid jamming detector, which combines statistical network traffic analysis with data mining methods, allows the achievement of acceptable performance even when random jitter accompanies the generation of CAMs, which complicates the analysis of the reasons for their losses in VANETs. The use case of the study is a challenging platooning C-ITS application, where V2X-enabled vehicles move together at highway speeds with short inter-vehicle gaps.

Cyclostationary-based jammer detection for wideband radios using compressed sensing and artificial neural network

Cognitive radio is a promising technology for frequency allocation to improve the spectrum utilization efficiency of licensed bands. However, in recent years, the attention of the researchers is fo...

Localization of multiple jamming attackers in vehicular ad hoc network

In vehicular ad hoc network, wireless jamming attacks are easy to be launched in the control channel and can cause serious influence on the network performance which may cause further safety accidents. In order to address the issue of wireless jamming attacks, a new technique which localizes the jamming attackers and prevents vehicles from jamming through human intervention is proposed. In this article, we propose a range-free approach to localize the source of the attacker and determine the number of jamming attackers. The data set is the locating information and the jamming detection information associated with each vehicle. Then, we formulate the problem of determining the number of attackers as a multiclass detection problem. We define the incorrectly classified area and use it to measure the distance between samples and centroids in fuzzy c-means algorithm. We further determine the number of jamming attackers through the coverage rate of beaconing circles and utilize weight-based fuzzy c-means to classify the data set. When the data set is classified as acceptable, we further explore the means of using particle swarm optimization algorithm to calculate the positional coordinates of each attacker. We simulate our techniques in MATLAB, and both urban traffic area and open area are considered in our simulation. The experimental results suggest that the proposed algorithm can achieve high precision when determining the number of attackers while the result of the classified performance is always satisfying. Our localization results lead to higher accuracy than other existing solutions. Also, when the data set is limited, the chances of taking accurate localization are higher than other measures.

An Experimental Evaluation of Low-Cost GNSS Jamming Sensors

This paper presents experimental results obtained during live GNSS jamming tests performed in a road environment. Different jamming detection strategies are considered with specific focus on signal agnostic approaches where no stringent model assumptions are made for the detector design. A measurement station made of a commercial GPS high-sensitivity receiver and an SDR front-end was deployed and used to collect measurements in the presence of jamming. The tests were performed using three different jammers and considering the effects introduced by a vehicle moving at different speeds. The advantages and shortcomings of the different detection approaches are analyzed, and the most effective detection strategies are selected. Copyright © 2017 Institute of Navigation