Physical Layer Attacks Research Articles

Advanced Security Framework for 6G Networks: Integrating Deep Learning and Physical Layer Security

This paper presents an advanced framework for securing 6G communication by integrating deep learning and physical layer security (PLS). The proposed model incorporates multi-stage detection mechanisms to enhance security against various attacks on the 6G air interface. Deep neural networks and a hybrid model are employed for sequential learning to improve classification accuracy and handle complex data patterns. Additionally, spoofing, jamming, and eavesdropping attacks are simulated to refine detection mechanisms. An anomaly detection system is developed to identify unusual signal patterns indicating potential attacks. The results demonstrate that machine learning (ML) and hybrid models outperform conventional approaches, showing improvements of up to 85% in bit error rate (BER) and 24% in accuracy, especially under attack conditions. This research contributes to the advancement of secure 6G communication systems, offering details on effective defence mechanisms against physical layer attacks.

Risk and Security Attacks in Physical Layer of IoT Environment

The Internet of Things (IoT) is innovative, intensive technology and supports wide areas of application, such as Smart environment, Healthcare, Security, industrial environment, and massive technology that inaugurate associated with real things. It also improves the living standard of society. These smart things are revolutionary and helping human beings in different ways but on the other hand security threats are growing rapidly and attackers are influencing the life of a man. Whereas, when technologies become developed, it carries a weakness that are continuously exploited and targeted. Security risk and threats directly concern with IoT environment and one of the main challenges of today, numerous issues need to be addressed within a physical layer of IoT Environment. In this paper, we have discussed the risk assessments and specifically pointed out the physical layer. We proposed a detailed ontology and highlighted the challenges, threats and different types of attacks in the IoT environment. We briefly debate the importance of the physical layer and emphasis the challenges it faces in the IoT environment and how hackler can attack this layer using tapping and QoS attacks.

Artificial intelligence empowered physical layer security for 6G: State-of-the-art, challenges, and opportunities

With the commercial deployment of the 5G system, researchers from both academia and industry are moving attention to the blueprint of the 6G system. The space–air–ground–sea integrated 6G system is envisioned to support various new applications with very diverse requirements in terms of quality of service and security. Due to the open nature of the wireless channel, highly dynamic network topology, and mission-critical applications, 6G will face various security threats. The conventional upper-layer cryptography-based security mechanisms face multiple challenges to resist physical layer attacks in the context of large numbers computing, power-limited low-cost Internet of Thing (IoT) devices. The Physical layer security (PLS) mechanisms by exploiting the random nature of the wireless channel and intrinsic hardware imperfection provide complementary approaches to secure the 6G system. Meanwhile, the quick development and successful application of artificial intelligence facilitate the advancement of PLS solutions. In this paper, we make a comprehensive overview of machine learning-empowered PLS techniques toward 6G. We first introduce the vision of the 6G system, typical applications, key enabling radio technologies, security threats, and security requirements to 6G. Then typical machine learning (ML) methods and the driving forces for introducing intelligent PLS in 6G are presented. Afterward, the major academic works on PLS oriented to key 6G radio techniques including cell-free massive MIMO, visible light communication, Terahertz technology, configurable intelligent surfaces, molecular communication, and ambient backscatter communication are discussed. Then, the latest works on ML-enabled PLS solutions including physical layer key generation, secure wireless transmission, physical layer authentication, intelligent anti-jamming, and ML-enabled attack and detection are systematically reviewed. Finally, we identify important open issues and future research opportunities to inspire further studies to realize a more intelligent and secure 6G system.

CatBoost-based Intrusion Detection Method for the Physical Layer of Smart Agriculture

Agriculture holds a pivotal role in the progress of human society. The challenges stemming from a burgeoning population, land degradation, water scarcity, and urbanization have intensified the need for more efficient agricultural production. While smart farming brings significant benefits to farmers and agricultural output, it also introduces complex cybersecurity risks to agricultural production. The security of the physical layer in smart agriculture is intricately tied to crop growth and yield, with indirect implications for the security of the network and application layers. This paper introduces a novel intrusion detection scheme based on CatBoost for the physical layer and evaluates its effectiveness using the publicly available ToN_IOT dataset. In binary classification results, the scheme achieves a remarkable recognition accuracy of 99.94%, along with a precision and recall of 99.88%. In multi-classification results, the scheme outperforms other existing solutions across all metrics. The experimental findings clearly illustrate the exceptional recognition accuracy of this implemented method against physical layer attacks within the domain of smart agriculture. Furthermore, the system’s implementation ensures the security of input data for the smart agriculture network layer, cloud, and blockchain applications.

Detecting CAN overlapped voltage attacks with an improved voltage-based in-vehicle intrusion detection system

The in-vehicle voltage-based intrusion detection system (VIDS) can recognize the identity of the ECUs and detect the physical layer attacks that cannot be detected by the content-based or frequency-based IDS. However, recent research in VIDS has discovered an overlapped voltage attack that can bypass the existing VIDS by generating overlapped voltage signals from two compromised ECUs to distort the fingerprint of the target ECU. The existing solutions need to change the communication protocol or network topology to mitigate the attack, which may involve additional development costs or scheduling mechanisms to coordinate the tasks. In this paper, motivated by not changing the network topology and protocols, we propose an improved VIDS that can efficiently detect the overlapped voltage attack of the in-vehicle CAN network. Before retraining the VIDS classifier model, we add the mechanism for detecting and filtering overlapped signals in the voltage samples by applying the Long Short-Term Memory (LSTM) autoencoder network to preserve the fingerprint characteristics of the benign waveform in the original samples. First, we establish an LSTM autoencoder model with benign voltages collected in a usual scenario and then apply the automatic thresholding approach to obtain the threshold to separate the overlapped and benign voltages. Next, we filter the overlapped voltages and extract the fingerprint features from the filtered voltage samples to predict the classification of the masquerade CAN packet during the attack. Finally, we evaluate the performance and attack success rate of the proposed VIDS on a CAN bus testbench. The results show that the proposed VIDS can effectively detect the overlapped voltage attack and identify the compromised ECU mimicking the distorted voltage fingerprints with an accuracy of 99.4%. The attack success rate of the proposed VIDS drops to 0∼18%, which is about 60∼95% for the existing VIDS.

Formal verification for security and attacks in IoT physical layer

Formal verification for security and attacks in IoT physical layer

Root Cause Analysis for Autonomous Optical Network Security Management

The ongoing evolution of optical networks towards autonomous systems supporting high-performance services beyond 5G requires advanced functionalities for automated security management. To cope with evolving threat landscape, security diagnostic approaches should be able to detect and identify the nature not only of existing attack techniques, but also those hitherto unknown or insufficiently represented. Machine Learning (ML)-based algorithms perform well when identifying known attack types, but cannot guarantee precise identification of unknown attacks. This makes Root Cause Analysis (RCA) crucial for enabling timely attack response when human intervention is unavoidable. We address these challenges by establishing an ML-based framework for security assessment and analyzing RCA alternatives for physical-layer attacks. We first scrutinize different Network Management System (NMS) architectures and the corresponding security assessment capabilities. We then investigate the applicability of supervised and unsupervised learning (SL and UL) approaches for RCA and propose a novel UL-based RCA algorithm called Distance-Based Root Cause Analysis (DB-RCA). The framework’s applicability and performance for autonomous optical network security management is validated on an experimental physical-layer security dataset, assessing the benefits and drawbacks of the SL- and UL-based RCA. Besides confirming that SL-based approaches can provide precise RCA output for known attack types upon training, we show that the proposed UL-based RCA approach offers meaningful insight into the anomalies caused by novel attack types, thus supporting the human security officers in advancing the physical-layer security diagnostics.

Providing Physical Layer Security for IoTs in the Last Mile

Communication security is one of the top security challenges for connected devices. Different from other links such as backhaul, the last mile technology also depends on the requirements of end users. Wireless technologies are generally selected for the mobility of users and ease of use. However, wireless medium has an open nature and thus wireless links are more prone to physical layer attacks compared to their wired counterparts. Moreover, simple end devices have constrained resources in both hardware and software, and it is not always feasible to apply conventional cryptographic approaches to provide security. We turn to chaos theory to provide security for simple devices at physical layer. The FM-DCSK and FM-CSK transmission system are built and implemented in the proposed secure communication system. The information message is embedded in wideband random-like signals, making the message remain covert. Transmission security is achieved by using the initial conditions and spreading factor as keys. To guard against active attacks, procedures for dynamic adjustment of initial conditions and other parameters are proposed. The scheme's cost effective features include the simplicity of communication setup and the low power consumption in generating and controlling the chaos signal. The sensitivity to initial condition and complex dynamic feature of chaotic function make it a promising approach for physical layer security.

A node pairing approach to secure the Internet of Things using machine learning

Although the reception of Internet of Things (IoT) services increased efficiency and allowed companies to automate the production through remote and intelligent control, it also increases the risk of potential threats and security attacks. The attackers can compromise the security of the IoT node and join it to a botnet, manipulate sensitive data or take control of a full network. Securing the IoT nodes from security attacks remains a challenge, especially under the multiple attack models where several attacks can be launched simultaneously. The objective of this paper is to prevent a type of multiple attacks that are the foundation to manipulate sensor nodes’ data that are: clone attack, tamper attack, false data injection, node malfunctioning, and physical layer attack. A solution is proposed to secure the IoT nodes against these attacks. The proposed solution is based on a novel sensor pairing approach to evaluate the trustworthiness of IoT nodes’ sensor data using multiple machine learning models. In particular, we propose a threat model and the pairing algorithm to pair each sensor node with the sensor node located in the neighbor. We compare the performance of three machine learning models, which are decision tree, Support Vector Machine (SVM), and k-Nearest Neighbors (KNN) by using two publically available real-world datasets in terms of attack detection accuracy, training time, and testing time. Our proposed system achieved up to 100% accuracy rate and nearly twice faster training and testing time as compared to the existing solutions. We provide the memory and energy consumption analysis to show that the proposed method has lower communication and memory overhead on IoT nodes than previous solutions against IoT security attacks. Our dataset just contains the data of a pair of sensors and is reduced up to eight times smaller in size than the actual dataset.

Efficient and lightweight protocol for anti-jamming communications in wireless body area networks

Wireless Body Area Networks (WBANs) have emerged as a new technology in healthcare. However, wireless interfaces make it more vulnerable to acute physical layer attacks. Indeed, jamming attacks on WBAN communications have become a significant challenge to address. This paper proposes a new security protocol against jamming attacks in healthcare and medical environments, combining Frequency-Hopping Spread Spectrum (FHSS) and Direct Sequence Spread Spectrum (DSSS), termed CFD-BD: Chaotic FH/DS Based Dissimulation. Our proposition is based on both the chaotic signal and dissimulation. The combination of these two techniques allows us to ensure an effective solution against jamming attacks and meet the challenges resulting from the constraints related to the limited resources of WBANs. The simulation results indicate that our proposal is lightweight in communication load, complexity, and storage.

Signal Emulation Attack and Defense for Smart Home IoT

Internet of Things (IoT) is transforming every corner of our daily life and plays important roles in the smart home. Depending on different requirements on wireless transmission, dedicated wireless protocols have been adopted on various types of IoT devices. Recent advances in Cross-Technology Communication (CTC) enable direct communication across those wireless protocols, which will greatly improve the spectrum utilization efficiency. However, it incurs serious security concerns on heterogeneous IoT devices. In this paper, we identify a new physical-layer attack, cross-technology signal emulation attack, where a WiFi device eavesdrops a ZigBee packet on the fly, and further manipulates the ZigBee device by emulating a ZigBee signal. To defend against this attack, we propose two defense strategies with the help of a commonly found WiFi router. Particularly, the passive defense strategy focuses on misleading the ZigBee signal eavesdropping, while the proactive approach develops a real-time detection mechanism on distinguishing between a common ZigBee signal and an emulated signal. We implement the complete attacking process and defense strategies with TI CC26x2R LaunchPad, USRP-N210 platform, and a self-designed prototype. Extensive experiments have demonstrated the existence of the attack, and the feasibility, effectiveness, and accuracy of the proposed defense strategies.

Derogation of Physical Layer Security Breaches in Maturing Heterogeneous Optical Networks

The evolution journey of optical network (ON) towards heterogeneous and flexible frameworks with high order of applications is continued from the last decade. Furthermore, the prominence of optical security, amount of transmitted data, bandwidth, and dependable presentation are heightened. The performance of ON is degraded in view of various natures of attacks at the physical layer, such as service disrupting and access to carrier data. In order to deal with such security breaches, new and efficient ON must be identified. So, this paper elaborates a detailed structure on physical layer security for heterogeneous ON. Possible mechanisms, such as Elliptic-curve Diffie–Hellman (ECDH), are used to treat a physical layer attack, and an efficient framework is proposed in this paper for 64 quadrature amplitude modulation-based orthogonal frequency division multiplex (64QAM-OFDM) ONs. Finally, theoretical and simulation validations are presented, and the effective results of the proposed method and viewpoint are concluded.

Cognitive Radio Jamming Attack Detection Using an Autoencoder for CRIoT Network

IoT network-connected devices are increasing day by day. It is impossible to allocate a spectrum for all IoT devices. This spectrum scarcity can be solved by cognitive radio-based dynamic spectrum sharing, which is referred to as Cognitive Radio Internet of Things (CRIoT). The jamming physical layer attack in CRIoT resulting in a denial of cognitive radio services and make spectrum underutilization. Continuous jamming can be detected quickly based on time delay on spectrum access, but discrete random jamming detection is challenging. This article proposes an autoencoder-based jamming attack detection in cognitive radio. The jamming detection problem is modeled as anomaly detection. The autoencoder is used to detect the anomaly signal component and the jammer. The simulation of random jamming attack detecting with time instant of attack is carried out to mitigate it. The proposed mechanism able to detect the jammer with 89% of accuracy.

A Lightweight and Scalable Physical Layer Attack Detection Mechanism for the Internet of Things (IoT) Using Hybrid Security Schema

The Internet of Things, also known as the IoT, refers to the billions of devices around the world that are now connected to the Internet, collecting and sharing data. The amount of data collected through IoT sensors must be completely securely controlled. To protect the information collected by IoT sensors, a lightweight method called Discover the Flooding Attack-RPL (DFA-RPL) has been proposed. The proposed DFA-RPL method identifies intrusive nodes in several steps to exclude them from continuing routing operations. Thus, in the DFA-RPL method, it first builds a cluster and selects the most appropriate node as a cluster head in DODAG, then, due to the vulnerability of the RPL protocol to Flooding attacks, it uses an ant colony algorithm (ACO) using five steps to detect attacks. Use Flooding to prevent malicious activity on the IoT network. In other words, if it detects a node as malicious, it puts that node on the detention list and quarantines it for a certain period of time. The results obtained from the simulation show the superiority of the proposed method in terms of Packet Delivery Rate, Detection Rate, False Positive Rate, and False Negative Rate compared to IRAD and REATO methods.

Who Is in Control? Practical Physical Layer Attack and Defense for mmWave-Based Sensing in Autonomous Vehicles

With the wide bandwidths in millimeter wave (mmWave) frequency band that results in unprecedented accuracy, mmWave sensing has become vital for many applications, especially in autonomous vehicles (AVs). In addition, mmWave sensing has superior reliability compared to other sensing counterparts such as camera and LiDAR, which is essential for safety-critical driving. Therefore, it is critical to understand the security vulnerabilities and improve the security and reliability of mmWave sensing in AVs. To this end, we perform the end-to-end security analysis of a mmWave-based sensing system in AVs, by designing and implementing practical physical layer attack and defense strategies in a state-of-the-art mmWave testbed and an AV testbed in real-world settings. Various strategies are developed to take control of the victim AV by spoofing its mmWave sensing module, including adding fake obstacles at arbitrary locations and faking the locations of existing obstacles. Five real-world attack scenarios are constructed to spoof the victim AV and force it to make dangerous driving decisions leading to a fatal crash. Field experiments are conducted to study the impact of the various attack scenarios using a Lincoln MKZ-based AV testbed, which validate that the attacker can indeed assume control of the victim AV to compromise its security and safety. To defend the attacks, we design and implement a challenge-response authentication scheme and a RF fingerprinting scheme to reliably detect aforementioned spoofing attacks.

Optical network security management: requirements, architecture, and efficient machine learning models for detection of evolving threats [Invited

As the communication infrastructure that sustains critical societal services, optical networks need to function in a secure and agile way. Thus, cognitive and automated security management functionalities are needed, fueled by the proliferating machine learning (ML) techniques and compatible with common network control entities and procedures. Automated management of optical network security requires advancements both in terms of the performance and efficiency of ML approaches for security diagnostics, as well as novel management architectures and functionalities. This paper tackles these challenges by proposing what we believe to be a novel functional block called the security operation center, describing its architecture, specifying key requirements on the supported functionalities, and providing guidelines on its integration with optical-layer controller. Moreover, to boost efficiency of ML-based security diagnostic techniques when processing high-dimensional optical performance monitoring data in the presence of previously unseen physical-layer attacks, we combine unsupervised and semi-supervised learning techniques with three different dimensionality reduction methods and analyze the resulting performance and trade-offs between the ML accuracy and run-time complexity.

On the Bilevel Optimization to Design Control Plane for SDONs in Consideration of Planned Physical-Layer Attacks

In the network planning of software-defined optical networks (SDONs), the control plane design is of great importance because it directly affects the performance and reliability of network control and management (NC&M). In this article, we consider the planned physical-layer attacks from a rational attacker, which can analyze the control plane of an SDON and target its attacks to the most vulnerable part. To address such attacks, we model the control plane design as a bilevel optimization, where the upper-level optimization is for the network planner to design the control plane whose vulnerability to planned attacks is minimized, while the lower-level optimization is for the attacker to plan its attacks such that the control plane can be disturbed as severely as possible. We first develop two approaches to solve the bilevel model exactly. Specifically, we first leverage the cutting plane method to solve it directly, and then transform it into a single-level mixed integer linear programming (MILP) model with the Bellman method for problem solving. To improve the time efficiency for large-scale problems, we also propose a polynomial-time approximation algorithm based on linear programming (LP) relaxation and randomized rounding. Extensive simulations with various physical topologies verify the effectiveness of our proposals.

An Overview and Future Directions on Physical-Layer Security for Cognitive Radio Networks

Cognitive radio networks (CRNs) have emerged as a reliable technology to address the problem of spectrum under-utilization. Cognitive radio (CR) is known to be intelligent, as it can sense, learn, and change its transmitting and receiving parameters according to changes in the surrounding environment. These operations are characterized by the CR cognition cycle. During the three stages of cognition, users are vulnerable to several types of attacks on the physical layer. Various types of physical-layer attacks can lead to loss of data security over CRNs. In this article, an overview of physical-layer security (PLS) for CRNs is provided. Some of the major attacks over the physical layer of CRN are presented, such as primary user emulation attack (PUEA), sensing falsification, jamming, and eavesdropping. Moreover, some of the suggested methods to combat these attacks and ensure data privacy are provided. We focus on presenting certain methods to combat eavesdropping. Current challenges and future research directions are included.

Secure optical communication using a quantum alarm

Optical fibre networks are advancing rapidly to meet growing traffic demands. Security issues, including attack management, have become increasingly important for optical communication networks because of the vulnerabilities associated with tapping light from optical fibre links. Physical layer security often requires restricting access to channels and periodic inspections of link performance. In this paper, we report how quantum communication techniques can be utilized to detect a physical layer attack. We present an efficient method for monitoring the physical layer security of a high-data-rate classical optical communication network using a modulated continuous-variable quantum signal. We describe the theoretical and experimental underpinnings of this monitoring system and the monitoring accuracy for different monitored parameters. We analyse its performance for both unamplified and amplified optical links. The technique represents a novel approach for applying quantum signal processing to practical optical communication networks and compares well with classical monitoring methods. We conclude by discussing the challenges facing its practical application, its differences with respect to existing quantum key distribution methods, and its usage in future secure optical transport network planning.

Primary user emulation attack mitigation using neural network

The spectrum sensing scheme suffers from a physical layer attack of Primary User Emulation Attack (PUEA). The resolution is to mitigate the cognitive radio user from the PUEA under the physical layer. Detecting the PUEA attack in real-time is a challenging one. The traditional Location-based PUEA detection requires the primary user's location knowledge, which may not be possible practically. This research focuses on developing a reliable spectrum sensing mechanism in the presence of PUEA attack and rapid change in the wireless channel. This reliable spectrum sensing framework is developed using the neural network-based PUEA detector excluding the location information. The Software-Defined Radio (SDR) called Universal Software Radio Peripheral (USRP) 2943R is used to implement the proposed mechanism for analyzing performance in real-time. The real-time experimental results show that PUEA detection can be achieved with 97% accuracy.