Spoofing Attacks Research Articles

Asynchronous secure controller design for singularly perturbation stochastic semi-Markov jump CPSs with the memory-based dynamic event-triggered scheme against complex cyber-attacks

In this paper, the problem of asynchronous secure controller design for singular perturbation stochastic semi-Markov jump systems (SPSS-MJSs) against complicated cyber-attacks is addressed. Firstly, the unified framework for multi-channel spoofing attacks and DoS attacks is properly established for CPSs with the unreliable communication network. Then, by introducing a register, we propose the memory-based dynamic event-triggered scheme (MDETS), which can save resources for the bandwidth limited network to a certain degree. Since the mode information of Markov chain for the secure controller is often unavailable, a hidden Markov model (HMM) is used to formulate the asynchronous situation between the mode of original system and the constructed controller. Further, by constructing Lyapunov functionals and utilizing linear matrix inequality (LMI) techniques, the obtained closed-loop SPSS-MJSs are endowed with an exponential stability criterion. And the co-design of the asynchronous secure controller and MDETS is presented. Finally, a practical example of the specific small capacitance circuit system is offered to verify the feasibility and superiority of the developed method.

Generalized Spoof Detection and Incremental Algorithm Recognition for Voice Spoofing

Highly deceptive deepfake technologies have caused much controversy, e.g., artificial intelligence-based software can automatically generate nude photos and deepfake images of anyone. This brings considerable threats to both individuals and society. In addition to video and image forgery, audio forgery poses many hazards but lacks sufficient attention. Furthermore, existing works have only focused on voice spoof detection, neglecting the identification of spoof algorithms. It is of great value to recognize the algorithm for synthesizing spoofing voices in traceability. This study presents a system combining voice spoof detection and algorithm recognition. In contrast, the generalizability of the spoof detection model is discussed from the perspective of embedding space and decision boundaries to face the voice spoofing attacks generated by spoof algorithms that are not available in the training set. This study presents a method for voice spoof algorithms recognition based on incremental learning, taking into account data flow scenarios where new spoof algorithms keep appearing in reality. Our experimental results on the LA dataset of ASVspoof show that our system can improve the generalization of spoof detection and identify new voice spoof algorithms without catastrophic forgetting.

An encoded histogram of ridge bifurcations and contours for fingerprint presentation attack detection

In recent years, the exponential growth of internet technologies has made personal authentication an integral part of security applications. The fingerprint-based biometric systems are essentially used to safeguard the users' privacy and confidentiality. However, such systems are prone to spoof attacks by artificial replicas of the fingerprints. This paper presents an improved feature extractor called BiRi-PAD (Encoded Histogram of Ridge Bifurcations and Contours for fingerprint Presentation Attack Detection) that aims to enhance the accuracy of live fingerprint detection. The feature extraction process of the proposed BiRi-PAD consists of four steps. First, the fingerprint image undergoes a process of extracting 2-channel ridge contour maps (2-RC maps). The first channel of 2-RC maps consists of ridge contours extracted by a set of derivative filters in the spatial domain whereas the second channel of 2-RC maps consists of the ridge contours extracted by the maximum moments based on phase congruency in the frequency domain. Further, minutiae-based feature information i.e., ridge bifurcations are extracted by the minimum moments based on phase congruency covariance. Moreover, a fusion equation is proposed to integrate 2-RC maps and bifurcations into a single feature map. Second, an improved Comprehensive Local Phase Quantization (CLPQ) based on the well-known feature descriptor Rotation Invariant Local Phase Quantization (LPQri) is proposed to extract the phase information of ridges. CLPQ extracts the orientation of the ridges by using the complex parts of the significant frequency components of LPQri and monogenic filters. Third, the proposed BiRi-PAD quantizes the 2-RC maps into pre-determined intervals. Finally, both 2-RC maps and CLPQ features are integrated to generate a feature vector of a single fingerprint image. Performance evaluations of BiRi-PAD are conducted on three publicly available benchmarks from the LivDet competition, namely LivDet 2013, 2011, and 2015. Experimental evaluations demonstrate that the proposed BiRi-PAD achieves significant reductions in average rates compared to state-of-the-art techniques of fingerprint liveness detection. Specifically, on LivDet 2013, LivDet 2011, and LivDet 2015, the average rates are reduced to 1.92%, 4.39%, and 4.55%, respectively.

An Analysis of Neighbor Discovery Protocol Attacks

Neighbor Discovery Protocol (NDP) is a network protocol used in IPv6 networks to manage communication between neighboring devices. NDP is responsible for mapping IPv6 addresses to MAC addresses and discovering the availability of neighboring devices on the network. The main risk of deploying NDP on public networks is the potential for hackers or attackers to launch various types of attacks, such as address spoofing attacks, denial-of-service attacks, and man-in-the-middle attacks. Although Secure Neighbor Discovery (SEND) is implemented to secure NDP, its complexity and cost hinder its widespread deployment. This research emphasizes the potential hazard of deploying IPv6 networks in public spaces, such as airports, without protecting NDP messages. These risks have the potential to crash the entire local network. To demonstrate these risks, the GNS3 testbed environment is used to generate NDP attacks and capture the resulting packets using Wireshark for analysis. The analysis results reveal that with just a few commands, attackers can execute various NDP attacks. This highlights the need to protect against the potential issues that come with deploying IPv6 on widely accessible public networks. In addition, the analysis result shows that NDP attacks have behavior that can be used to define various NDP attacks.

We Are Also Metabolites: Towards Understanding the Composition of Sweat on Fingertips via Hyperspectral Imaging

AI-empowered sweat metabolite analysis is an emerging and open research area with great potential to add a third category to biometrics: chemical. Current biometrics use two types of information to identify humans: physical (e.g., face, eyes) and behavioral (i.e., gait, typing). Sweat offers a promising solution for enriching human identity with more discerning characteristics to overcome the limitations of current technologies (e.g., demographic differential and vulnerability to spoof attacks). The analysis of a biometric trait’s chemical properties holds potential for providing a meticulous perspective on an individual. This not only changes the taxonomy for biometrics, but also lays a foundation for more accurate and secure next-generation biometric systems. This paper discusses existing evidence about the potential held by sweat components in representing the identity of a person. We also highlight emerging methodologies and applications pertaining to sweat analysis and guide the scientific community towards transformative future research directions to design AI-empowered systems of the next generation.

Enhancing Face Spoofing Attack Detection: Performance Evaluation of a VGG-19 CNN Model

With the wide use of facial verification and authentication systems, the performance evaluation of Spoofing Attack Detection (SAD) module in the systems is important, because poor performance leads to successful face spoofing attacks. Previous studies on face SAD used a pretrained Visual Geometry Group (VGG) -16 architecture to extract feature maps from face images using the convolutional layers, and trained a face SAD model to classify real and fake face images, obtaining poor performance for unseen face images. Therefore, this study aimed to evaluate the performance of VGG-19 face SAD model. Experimental approach was used to build the model. VGG-19 algorithm was used to extract Red Green Blue (RGB) and deep neural network features from the face datasets. Evaluation results showed that the performance of the VGG-19 face SAD model improved by 6% compared with the state-of-the-art approaches, with the lowest equal error rate (EER) of 0.4%. In addition, the model had strong generalization ability in top-1 accuracy, threshold operation, quality test, fake face test, equal error rate, and overall test standard evaluation metrics.

Restrictive Voting Technique for Faces Spoofing Attack

Face anti-spoofing has become widely used due to the increasing use of biometric authentication systems that rely on facial recognition. It is a critical issue in biometric authentication systems that aim to prevent unauthorized access. In this paper, we propose a modified version of majority voting that ensembles the votes of six classifiers for multiple video chunks to improve the accuracy of face anti-spoofing. Our approach involves sampling sub-videos of 2 seconds each with a one-second overlap and classifying each sub-video using multiple classifiers. We then ensemble the classifications for each sub-video across all classifiers to decide the complete video classification. We focus on the False Acceptance Rate (FAR) metric to highlight the importance of preventing unauthorized access. We evaluated our method using the Replay Attack dataset and achieved a zero FAR. We also reported the Half Total Error Rate (HTER) and Equal Error Rate (EER) and gained a better result than most state-of-the-art methods. Our experimental results show that our proposed method significantly reduces the FAR, which is crucial for real-world face anti-spoofing applications.

SecDedup: Secure data deduplication with dynamic auditing in the cloud

In the era of big data, data explosion has brought challenges to cloud storage management. To improve cloud storage efficiency and save network communication bandwidth, cloud data deduplication has emerged as a research hotspot, especially in the field of encrypted cloud data storage. How to enhance the security of encrypted data deduplication by resisting various attacks on deduplication has become an important research issue. However, existing solutions suffer from security flaws and are vulnerable to a series of attacks, e.g., duplicate faking attacks, file ownership spoofing attacks, and file tampering attacks. Besides, dynamic data operation is rarely considered or audited. To solve the above problems, we propose a novel scheme, named SecDedup, to enhance the security of encrypted cloud data deduplication with dynamic auditing. SecDedup applies a homomorphic authenticator and designs a multi-functional data tag with optimized storage to support deduplication and auditing at the same time with security guarantee against various attacks as mentioned above. In particular, We embed multi-set hash functions into data tags to achieve dynamic data auditing. In addition, SecDedup supports batch auditing with optimized computational cost for multiple deduplication auditing tasks. We formally prove the correctness and security of SecDedup, showing that it can successfully achieve our design goals for resisting the above listed attacks. We also analyze and evaluate the performance of SecDedup in terms of computation, communication, and tag storage overheads by comparing them with existing works. The results show its effectiveness and scalability.

Fuzzy-based acquisition in GPS receivers for spoofing mitigation

Spoofing attacks are one of the most dangerous threats in the application of the Global Positioning System (GPS). These attacks can influence different parts of GPS receivers such as acquisition, tracking, or navigation part. We focus on the acquisition stage. We propose a fuzzy-acquisition technique that aligns the value of the acquisition threshold by using the parameters affecting the acquisition performance in the presence of the spoofing attack. Then, the ratio between the correlation levels of different satellite signals is introduced as a new criterion for distinguishing the spoofing signal from the authentic signal. In order to verify the proposed algorithm, it is implemented in a measurement and four laboratory data sets. The results indicate that the proposed algorithm effectively mitigates spoofing attacks by an average of 96.61%. Lastly, the performance of the suggested method is compared against the wavelet-based method. The results show a 21.60% advantage of our method.

A Study Regarding the Impact of Navigation Data Manipulation on Unmanned Aerial Vehicle GNSS Sensors

Abstract Nowadays, Unmanned Aerial Vehicle systems have proven to be very useful in a variety of applications, such as monitoring and surveying an area of interest, mapping inaccessible terrain, real-time traffic monitoring, reconnaissance missions, transport of loads, and also for military purposes. The movement and stability in the air of these systems is dependent on the on-board sensors such as GNSS sensors, proximity sensors, accelerometer, gyroscope, light and temperature sensors. GNSS sensors are vital devices capable of processing navigation signals to determine the position, orientation and speed of the UAV system during operation. GNSS sensors are also used in many applications that require navigation data as well as highly accurate synchronization data. Both GNSS systems and the sensors that process their data have been shown to be vulnerable to jamming and spoofing attacks. GNSS sensors are of particular importance and the information provided by them requires protection. Considering these things, this paper aims to carry out a study in which several UAV systems will be exposed to signal samples with manipulated navigation data. This study will help us to assess the impact of exploiting GNSS sensor vulnerabilities and the effects it can have during the operation of UAV systems.

Robust Adversarial Attacks on Deep Learning-Based RF Fingerprint Identification

Deep learning (DL)-based radio frequency fingerprint identification (RFFI), despite its state-of-the-art capability in improving the security performance of communication networks, is still vulnerable to carefully crafted and imperceptible adversarial attack. However, conventional radio frequency (RF) adversarial attacks ignore the impact of the practical fading channels between the attacker and the sensor. In this letter, a generation adversarial perturbations (GAP) problem is formulated in a DL-based RFFI network. Both the case of ideally perfect channel state information (CSI) and the practical imperfect CSI are considered. The closed-form expression of the adversarial example based on the perfect CSI is provided. In order to address the intractable non-convex robust optimization problem under the imperfect CSI, a spoofing attack algorithm is proposed by exploiting the S-procedure. Simulation results demonstrate that our proposed scheme achieves a superior spoofing ratio compared with other benchmark schemes.

Spatial Sparsity-Based Pilot Attack Detection and Transmission Countermeasure for Cell-Free Massive MIMO System

In this article, we investigate the security performance of a cell-free (CF) massive multiple-input–multiple-output (MIMO) system that is composed of a large number of randomly distributed access points (APs) and multiple users. There may be an eavesdropper who launches an active pilot attack during uplink channel estimation stage to eavesdrop on information in data transmission stage, which threaten the security of the system. A spatial sparsity-based pilot attack detection (SS-based-PAD) method is proposed in this study to determine whether there is an active eavesdropper. Different data transmission schemes were developed according to the detection results. In the presence of an eavesdropper, rate maximization power allocation (SRM-PA) algorithm is proposed to maximize the security of the eavesdropped user. On the contrary, achievable rate equalization power allocation (ARE-PA) algorithm is proposed to provide fair services for all legitimate users. Moreover, an AP selection scheme and corresponding conjugate beamforming are used to enhance the security performance of the system and mitigate the impact of a pilot spoofing attack.

LiVoAuth: Liveness Detection in Voiceprint Authentication With Random Challenges and Detection Modes

Voiceprint authentication provides great con- venience to users in many application scenarios. However, it easily suffers from spoofing attacks including speech synthesis, speech conversion and speech replay. Liveness detection is an effective way to resist these attacks. But existing methods suffer from many disadvantages, such as extra deployment costs due to precise data collection, environmental disturbance, high computational overhead and operational complexity. A uniform platform that can offer Voiceprint Authentication as a Service (VAaS) over the cloud is also lacked. Hence, it is imperative to design an economic and effective method for liveness detection in voiceprint authentication. In this paper, we propose a novel liveness detection method named LiVoAuth for voiceprint authentication. It applies a randomly generated vector se- quence as Liveness Detection Mode (LDM), corresponding to a random challenge code used for authentication. We implement LiVoAuth and conduct a series of user studies to evaluate its performance in terms of accuracy, stability, efficiency, security, and user acceptance. Experimental re- sults demonstrate its advantages compared with cutting- edge methods.

EScope: Effective Event Validation for IoT Systems Based on State Correlation

Typical Internet of Things (IoT) systems are event-driven platforms, in which smart sensing devices sense or subscribe to events (device state changes), and react according to the preconfigured trigger-action logic, as known as, automation rules. “Events” are essential elements to perform automatic control in an IoT system. However, events are not always trustworthy. Sensing fake event notifications injected by attackers (called event spoofing attack) can trigger sensitive actions through automation rules without involving authorized users. Existing solutions verify events via “event fingerprints” extracted by surrounding sensors. However, if a system has homogeneous sensors that have strong correlations among them, traditional threshold-based methods may cause information redundancy and noise amplification, consequently, decreasing the checking accuracy. Aiming at this, in this paper, we propose “EScope”, an effective event validation approach to check the authenticity of system events based on device state correlation. EScope selects informative and representative sensors using an Neural-Network-based (NN-based) sensor selection component and extracts a verification sensor set for event validation. We evaluate our approach using an existing dataset provided by Peeves. The experiment results demonstrate that EScope achieves an average 67% sensor amount reduction on 22 events compared with the existing work, and increases the event spoofing detection accuracy.

A multimodal face antispoofing method based on multifeature vision transformer and multirank fusion

SummaryFace antispoofing (FAS) is attracting increasing attention from researchers because of its important role in preventing facial recognition systems from face spoofing attacks. With the advancement of various new convolutional neural network structures and the construction of various face antispoofing databases, the deep learning‐based face antispoofing algorithm has become the main method in the FAS field. However, the generalization performance of the current multimodal face antispoofing algorithm is poor, and the recognition performance of the model on different datasets is quite different. Therefore, we design a multimodal face antispoofing framework based on a multifeature transformer (MFViT) and multirank fusion (MRF). First, we use a vision transformer structure, MFViT, for multimodal face antispoofing and a combination of modalities to capture the distinguishing characteristics in each modality. Second, we design a multidimensional multimodal fusion module, MRF, according to the various modal fusion characteristics obtained by the MFViT to fuse modal information in different dimensions more effectively. Evaluation results indicate that framework we designed achieves an average classification error rate (ACER) of 1.61% on the CASIA‐SURF dataset and an ACER of 6.5% on the CASIA‐SURF CeFA dataset.

Trusted fingerprint localization for multimedia devices based on blockchain

The localization process is necessary for intelligent multimedia systems to provide appropriate service, but it is still a significant challenge to provide localization services for devices without active localization ability. The vulnerability of existing localization frameworks is that the incredibility of the information provided can not be verified. This remains the localization process vulnerable to forgery attacks, which can cause false localization results and impact the performance of intelligent multimedia systems. To address this problem, a blockchain-based fingerprint localization scheme is proposed and evaluated in this article. Firstly, A real-time database of verified and temper-proof electromagnetic fingerprints is established by collecting the information from contributors through blockchain. Secondly, a distributed algorithm is designed to obtain a trusted localization based on shared electromagnetic fingerprint information. Lastly, to save on-chain computing resources and defend against spoofing attacks and collusion attacks, an on-chain/off-chain scheme is proposed. The simulations show the practicability and robustness of the proposed scheme. The results are analyzed based on localization accuracy and resistance to malicious activities. It is shown that devices can be located without increasing location errors in malicious environments where 20% of data is uploaded by malicious nodes, and the localization results become more accurate as the decentralized database spontaneously established is expanded.

Computer Network: An Implementation of MAC Spoofing

An exponential growth has observed of network or internet users due to diverse resource and information sharing services. Contrary, network uses also increased in different kinds of attacks. Means network is vulnerable for many types of attacks. Computer network may exploit in different contexts such as denial of service, ping death, malfunction routing, flooding, man in the middle and spoofing attack. Among of these MAC spoofing is kind of attack spoofing attack that target to MAC or physical address of the network host or router. It tampers original address to any other random or user defined address. The aim of the study is to present MAC address and its types. With this, MAC spoofing attack also presented. Implementation environment and method for the MAC spoofing also presented. MAC spoofing is implemented in the KALI Linux operating system with the help of MACCHANGER tool.

Pedestrian Localization with Stride-Wise Error Estimation and Compensation by Fusion of UWB and IMU Data.

Indoor positioning enables mobile machines to perform tasks (semi-)automatically, such as following an operator. However, the usefulness and safety of these applications depends on the reliability of the estimated operator localization. Thus, quantifying the accuracy of positioning at runtime is critical for the application in real-world industrial contexts. In this paper, we present a method that produces an estimate of the current positioning error for each user stride. To accomplish this, we construct a virtual stride vector from Ultra-Wideband (UWB) position measurements. The virtual vectors are then compared to stride vectors from a foot-mounted Inertial Measurement Unit (IMU). Using these independent measurements, we estimate the current reliability of the UWB measurements. Positioning errors are mitigated through loosely coupled filtering of both vector types. We evaluate our method in three environments, showing that it improves positioning accuracy, especially in challenging conditions with obstructed line of sight and sparse UWB infrastructure. Additionally, we demonstrate the mitigation of simulated spoofing attacks on UWB positioning. Our findings indicate that positioning quality can be judged at runtime by comparing user strides reconstructed from UWB and IMU measurements. Our method is independent of situation- or environment-specific parameter tuning, and as such represents a promising approach for detecting both known and unknown positioning error states.

ЗАБЕЗПЕЧЕННЯ СТІЙКОЇ РОБОТИ СУПУТНИКОВОЇ НАВІГАЦІЇ НА МОРІ

The article examines the challenges of global navigation satellite systems (GNSS) functioning at sea under unintentional and intentional interference. The article reviews the vulnerability of GNSS, the methods of protection against interference and the ways of mitigating their impact based on the marine concept of positioning, navigation and time synchronization (PNT). The main goal of this concept is the guaranteed obtaining of reliable data on coordinates, navigation and exact time due to the combined use and comparison of indication for disparate systems and sensors under the influence of natural or intentional interference (attacks) on ship's GNSS equipment. The article analyzes various open sources of information to identify two methods of ensuring the integrity and accuracy of PNT data according to IMO documents and standards. The first method is the detection and direct countermeasures against attacks on shipboard GNSS equipment. The article determines that the most common and easy-to-implement type of attacks are jamming attacks of satellite signals, unlike the more complex and challenging spoofing attacks. The main approach to protection against jamming attacks is spatial signal processing using adaptive marine antenna arrays with a controlled pattern. Examples of modern practical developments of adaptive antenna arrays are given. The second method to ensure reliable PNT data is the use of alternative navigation systems, redundant capabilities and non-traditional methods using the existing systems. Technical solutions in this method have limitations due to the requirements for the vessel’s conventional navigation and radio communication installation. IMO has suggested structures of multisensor and multisystem receivers for obtaining reliable PNT data. These structures combine primary data from different systems based on different principles, such as satellite, terrestrial and augmented correction systems, vessel navigation data and reference systems. The processed PNT data must be accompanied by accuracy and integrity indicators. Keywords: positioning, navigation, time synchronization, jamming, spoofing, spatial processing, antenna arrays, cyber risks, information protection.

Detection of GNSS spoofing using the Fourier transform of correlator outputs and mitigation using an autoencoder neural network

Present study focuses on defense against carry-off-type spoofing attacks which often cause distortion in the correlation function profile. Investigation of the frequency characteristics of the correlation function is proposed to detect the presence of the spoofing signal. Having detected the spoofing signal, it is suggested to use an autoencoder neural network to deal with the impacts of the spoofing. The autoencoder neural network removes distortions caused by the spoofing signal from the correlation function. Results demonstrate that the proposed detection method achieves a higher than 98% detection rate and autoencoder-based approach mitigates spoofing attacks by an average of 92.64%.