Jamming Attacks Research Articles

A Chaos-FFH Based Approach Against Illegal Intelligent Reflecting Surface (IIRS) Based Jamming Attacks

Illegal intelligent reflecting surfaces (I- IRSs), i.e., the illegal deployment and utilization of IRSs, impose serious harmful impacts on wireless networks. The existing I-IRS-based illegal jammer (IJ) requires channel state information (CSI) or extra power or both, and therefore, the I-IRS- based IJ seems to be difficult to implement in practical wireless networks. Due to continued sharing of resources, wireless networks often come under security attacks, most common of which are eavesdropping attacks. In the case of eavesdropping attacks, deliberately designed random eavesdropping data is added to the channel. These eavesdropping along with noise result in packet losses and low throughput, degrading the overall performance of the cognitive network. In this work, a security aware jamming and eavesdropping rejection mechanism is proposed which detects suspicious signals in the channel frequency response and employs discrete equalization to recover transmitted data. The proposed approach uses a chaos based FFH protocol for possible adversarial eavesdropping attacks. The error rate and average sum rate show that the proposed system outperforms the exiting systems in terms of the performance metrics. Keywords:- Illegal Intelligent Reflecting Surface (IIRS), Jamming Attack, Packet Intercept. Error Rate, Sum Rate

OPTIMIZING EFFICIENT AND RELIABLE PACKET TRANSMISSION OVER WIRELESS NETWORKS

Wireless Networks are becoming an increasingly important technology that is bringing the world closer together. In this type of network environment there could be more chances of attacks. The packets cannot be easily transferred over the network. It affects network performance degrade. While eavesdropping and message injection can be prevented using cryptographic methods, jamming attacks are much harder to counter. They have been shown to actualize severe Denial-of- Service (DoS) attacks against networks. In Simplest form adversary blocks the packets that are transmitted over wireless network. Typically, jamming attacks has been considered under an external threat model, in which the jammer is not part of the network. To overcome the above problem of network traffic and performance in this paper we have considered a packet hiding methods that can be securely transmit packets over the network. We are addressing the problem of jamming attacks under internal threat model and two schemes are proposed that prevent real-time packet classification of packets by combining hiding scheme based on cryptographic primitives. Keywords: Selective Jamming, Denial-of-Service, Wireless Networks, Packet Classification.

An Extensive Classification of 5G Network Jamming Attacks

The paper introduces a pioneering taxonomy for 5G jamming attacks, meticulously crafted through diverse research methodologies, including literature review, case studies, incident database analysis, and network architecture assessment. This taxonomy is based on six dimensions: attack classification, attack techniques, jammer characteristics, impact and target analysis, countermeasure strategies, and security layers. It gives a full picture of what is needed to protect 5G networks from new security threats, especially jamming attacks. Real‐world case studies from regions like Ukraine, the South China Sea, and Cuba validate the taxonomy’s efficacy in analyzing varied jamming scenarios and offer practical insights into cyber threats and mitigation strategies. By looking closely at attack details, network effects, countermeasure strategies, and security layers, this research makes a big difference in improving the security of 5G networks. It gives everyone involved, the tools and knowledge they need to protect themselves from jamming attacks and make networks more resilient in a world where technology and connectivity are changing quickly. The key findings of this research include identifying key 5G network vulnerabilities to jamming attacks and presenting a detailed six‐dimensional taxonomy: attack classification, techniques, jammer characteristics, impact analysis, countermeasures, and security layers. It emphasizes the need for strong security strategies to improve 5G network resilience against evolving threats, which case studies from Cuba, the South China Sea, and Ukraine have validated.

Disco Intelligent Reflecting Surfaces: Active Channel Aging for Fully-Passive Jamming Attack

Due to the open communications environment in wireless channels, wireless networks are vulnerable to jamming attacks. However, existing approaches for jamming rely on knowledge of the legitimate users’ (LUs’) channels, extra jamming power, or both. To raise concerns about the potential threats posed by illegitimate intelligent reflecting surfaces (IRSs), we propose an alternative method to launch jamming attacks on LUs without either LU channel state information (CSI) or jamming power. The proposed approach employs an adversarial IRS with random phase shifts, referred to as a “disco” IRS (DIRS), that acts like a “disco ball” to actively age the LUs’ channels. Such active channel aging (ACA) interference can be used to launch jamming attacks on multi-user multiple-input single-output (MU-MISO) systems. The proposed DIRS-based fully-passive jammer (FPJ) can jam LUs with no additional jamming power or knowledge of the LU CSI, and it can not be mitigated by classical anti-jamming approaches. A theoretical analysis of the proposed DIRS-based FPJ that provides an evaluation of the DIRS-based jamming attacks is derived. Based on this detailed theoretical analysis, some unique properties of the proposed DIRS-based FPJ can be obtained. Furthermore, a design example of the proposed DIRS-based FPJ based on one-bit quantization of the IRS phases is demonstrated to be sufficient for implementing the jamming attack. In addition, numerical results are provided to show the effectiveness of the derived theoretical analysis and the jamming impact of the proposed DIRS-based FPJ.

Deep Reinforcement Learning for EH-Enabled Cognitive-IoT Under Jamming Attacks

Deep Reinforcement Learning for EH-Enabled Cognitive-IoT Under Jamming Attacks

Exploring PTS method in MIMO advanced waveforms signal for 5G network security analysis

Peak-to-average power ratio (PAPR) issues lead to a reduction in the scheme’s throughput. In this research, we propose a beneficial method for lowering the high PAPR of an orthogonal frequency division multiplexing (OFDM) system, called partial transmission sequence (PTS). The PTS method of PAPR reduction in advanced waveforms will depend on the usage of PAPR reduction technologies in 5G waveforms. This paper presents the simulation of an OFDM PTS reduction technique. The simulation results demonstrate that the proposed approach performs better than the conventional PAPR reduction approach. Further. it is also seen that enhances network security by increasing data diversity, reducing interference, and improving resilience against eavesdropping and jamming attacks.

Dealing With Jamming Attacks in Uplink Pairwise NOMA Using Outage Analysis, Smart Relaying, and Redundant Transmissions

Dealing With Jamming Attacks in Uplink Pairwise NOMA Using Outage Analysis, Smart Relaying, and Redundant Transmissions

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

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

Subnetwork-Lossless Robust Watermarking for Hostile Theft Attacks in Deep Transfer Learning Models

Recently, considerable progress has been made in providing solutions to prevent intellectual property (IP) theft for deep neural networks (DNNs) in ideal classification or recognition scenarios. However, little work has been dedicated to protecting the IP of DNN models in the context of transfer learning. Moreover, knowledge transfer is usually achieved through knowledge distillation or cross-domain distribution adaptation techniques, which will easily lead to the failure of the IP protection due to the high risk of the underlying DNN watermark being corrupted. To address this issue, we propose a subnetwork-lossless robust DNN watermarking (SRDW) framework, which can exploit out-of-distribution (OOD) guidance data augmentation to boost the robustness of watermarking. Specifically, we accurately seek the most rational modification structure (i.e., core subnetwork) using the module risk minimization, and then calculate the contrastive alignment error and the corresponding hash value as the reversible compensation information for the restoration of carrier network. Experimental results show that our scheme has superior robustness against various hostile attacks, such as fine-tuning, pruning, cross-domain matching, and overwriting. In the absence of malicious jamming attacks, the core subnetwork can be recovered without any loss. Besides that, we investigate how embedding watermarks in batch normalization (BN) layers affect the generalization performance of the deep transfer learning models, which reveals that reducing the embedding modifications in BN layers can further promote the robustness to resist hostile attacks.

LDSAE: LeNet deep stacked autoencoder for secure systems to mitigate the errors of jamming attacks in cognitive radio networks

LDSAE: LeNet deep stacked autoencoder for secure systems to mitigate the errors of jamming attacks in cognitive radio networks

Jamming Attack Mitigation in Wireless Federated Learning Networks using Bayesian Games

Jamming Attack Mitigation in Wireless Federated Learning Networks using Bayesian Games

Adversarial Network Model Based on Feature Fusion Learner for Intrusion Detection in Sensor Networks

The adversarial machine learning approaches are modelled to provide a defence mechanism during the prediction of cloning and jamming attacks launched over the wireless communication process. The transmitter is supplied with a pre-trained classifier to analyze the status of the channel based on the sensing nature and determine the other transmission process. The learning method gathers all acknowledgements and fusion made between nodes and the channel's current state to build a learning model that can accurately identify the succeeding transmission constraint caused by network jamming. In this instance, compared to random jamming procedures, an inventive anti-clone detection strategy aims to minimize the number of clones and jamming found throughout the network model. The transmitter analyzes the power restrictions over the sensor networks using the learning-based fisher score (FS). Here, an adversarial network model (ANM-FS) is fused to diminish the computational time to collect the training dataset by examining the incoming samples. With this defence mechanism, the transmitter intends to predict the false prediction rate (FPR) and design a better model for providing a reliable classifier. Systematically, the transmitter identifies the floating of attacks over the network model and adopts the defending mechanism to mislead the injected clone, enhancing the throughput and reducing the prediction error.

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

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

Random Slope Coding for Jamming Attack Mitigation in FMCW Radars

Random Slope Coding for Jamming Attack Mitigation in FMCW Radars

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.

Optimal energy allocation based on SINR under DoS attack

According to signal to interference plus noise ratio (SINR) of channel, we consider optimal attack schedule against remote state estimation in cyber–physical systems (CPSs) under denial of service (DoS) attack. Each physical process has a smart sensor that acquires the measurement of the system and transmits its local state estimation to the remote estimation terminal through a wireless channel. At each time step, a DoS attacker can send a jamming attack to one of the two channels. On the side of the attacker, this paper aims to search an optimal strategy, which maximizes the estimation error of the remote estimator. We propose an algorithm to obtain the optimal attack schedule policy by using of Markov decision process (MDP). A numerical example is provided to demonstrate the effectiveness of the proposed methods.

Opportunistic Wiretapping/Jamming: A New Attack Model in Millimeter-Wave Wireless Networks

While the millimeter-wave (mmWave) communication is less susceptible against the conventional wiretapping attack due to its short transmission range and directivity, this paper proposes a new opportunistic wiretapping and jamming (OWJ) attack model in mmWave wireless networks. With OWJ, an attacker can opportunistically conduct wiretapping or jamming to initiate a more hazardous attack based on the instantaneous costs of wiretapping and jamming. We also provide three realizations of the OWJ attack, which are mainly determined by the cost models relevant to distance, path loss and received power, respectively. To understand the impact of the new attack on mmWave network security, we first develop novel approximation techniques to characterize the irregular distributions of wiretappers, jammers and interferers under three OWJ realizations. With the help of the results of node distributions, we then derive analytical expressions for the secrecy transmission capacity to depict the network security performance under OWJ. Finally, we provide extensive numerical results to illustrate the effect of OWJ and to demonstrate that the new attack can more significantly degrade the network security performance than the pure wiretapping or jamming attack.

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.

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.

A Countermeasure Against RIS Jamming Attack in Physical-Layer Key Generation

A Countermeasure Against RIS Jamming Attack in Physical-Layer Key Generation