Search Results (26)

Specific emitter identification (SEI) is a core technology for wireless device security that plays a crucial role in protecting wireless communication systems from various security threats. However, current deep learning-based SEI methods heavily rely on large amounts of labeled data for supervised training, facing challenges in non-cooperative communication scenarios. To address these issues, this paper proposes a novel contrastive asymmetric masked learning-based SEI (CAML-SEI) method, effectively solving the problem of SEI under scarce labeled samples. The proposed method constructs an asymmetric auto-encoder architecture, comprising an encoder network based on channel squeeze-and-excitation residual blocks to capture radio frequency fingerprint (RFF) features embedded in signals, while employing a lightweight single-layer convolutional decoder for masked signal reconstruction. This design promotes the learning of fine-grained local feature representations. To further enhance feature discriminability, a learnable non-linear mapping is introduced to compress high-dimensional encoded features into a compact low-dimensional space, accompanied by a contrastive loss function that simultaneously achieves feature aggregation of positive samples and feature separation of negative samples. Finally, the network is jointly optimized by combining signal reconstruction and feature contrast tasks. Experiments conducted on real-world ADS-B and Wi-Fi datasets demonstrate that the proposed method effectively learns generalized RFF features, and the results show superior performance compared with other SEI methods. Full article

Despite the widespread adoption of indoor positioning technology, the existing solutions still face significant challenges. On one hand, Wi-Fi-based positioning struggles to balance accuracy and efficiency in complex indoor environments and architectural layouts formed by pre-existing access points (APs). On the other hand, vision-based methods, while offering high-precision potential, are hindered by prohibitive costs associated with binocular camera systems required for depth image acquisition, limiting their large-scale deployment. Additionally, channel state information (CSI), containing multi-subcarrier data, maintains amplitude symmetry in ideal free-space conditions but becomes susceptible to periodic positioning errors in real environments due to multipath interference. Meanwhile, image-based positioning often suffers from spatial ambiguity in texture-repeated areas. To address these challenges, we propose a novel hybrid indoor positioning method that integrates multi-granularity and multi-modal features. By fusing CSI data with visual information, the system leverages spatial consistency constraints from images to mitigate CSI error fluctuations while utilizing CSI’s global stability to correct local ambiguities in image-based positioning. In the initial coarse-grained positioning phase, a neural network model is trained using image data to roughly localize indoor scenes. This model adeptly captures the geometric relationships within images, providing a foundation for more precise localization in subsequent stages. In the fine-grained positioning stage, CSI features from Wi-Fi signals and Scale-Invariant Feature Transform (SIFT) features from image data are fused, creating a rich feature fusion fingerprint library that enables high-precision positioning. The experimental results show that our proposed method synergistically combines the strengths of Wi-Fi fingerprints and visual positioning, resulting in a substantial enhancement in positioning accuracy. Specifically, our approach achieves an accuracy of 0.4 m for 45% of positioning points and 0.8 m for 67% of points. Overall, this approach charts a promising path forward for advancing indoor positioning technology. Full article

Popular Large Language Models (LLMs) access uses website browsing and also faces website fingerprinting attacks. Website fingerprinting attacks have increasingly threatened website users to the leakage of browsing privacy. In addition to the often-used network traffic analysis, interrupt tracing exploits the microarchitectural side channels to be a new compromising method and assists website fingerprinting attacks on non-LLM websites with up to 96.6% classification accuracy. More importantly, our observations show that LLM website access performs inherent defense and decreases the attack classification accuracy to 6.5%. This resistance highlights the need to develop new website fingerprinting attacks for LLM websites. Therefore, we propose a fine-grained LLM-oriented website fingerprinting attack via fusing interrupt trace and network traffic (LLM-WFIN) to identify the browsing website and the content type accurately. A prior-fusion-based one-stage classifier and post-fusion-based two-stage classifier are trained to enhance website fingerprinting attacks. The comprehensive results and ablation study on 25 popular LLM websites and varying machine learning methods demonstrate that LLM-WFIN using post-fusion achieves 97.2% attack classification accuracy with no defense and outperforms prior-fusion with 81.6% attack classification accuracy with effective defenses. Full article

Malicious domains are part of the landscape of the internet but are becoming more prevalent and more dangerous both to companies and to individuals. They can be hosted on various technologies and serve an array of content, including malware, command and control and complex phishing sites that are designed to deceive and expose. Tracking, blocking and detecting such domains is complex, and very often it involves complex allowlist or denylist management or SIEM integration with open-source TLS fingerprinting techniques. Many fingerprinting techniques, such as JARM and JA3, are used by threat hunters to determine domain classification, but with the increase in TLS similarity, particularly in CDNs, they are becoming less useful. The aim of this paper was to adapt and evolve open-source TLS fingerprinting techniques with increased features to enhance granularity and to produce a similarity-mapping system that would enable the tracking and detection of previously unknown malicious domains. This was achieved by enriching TLS fingerprints with HTTP header data and producing a fine-grain similarity visualisation that represented high-dimensional data using MinHash and Locality-Sensitive Hashing. Influence was taken from the chemistry domain, where the problem of high-dimensional similarity in chemical fingerprints is often encountered. An enriched fingerprint was produced, which was then visualised across three separate datasets. The results were analysed and evaluated, with 67 previously unknown malicious domains being detected based on their similarity to known malicious domains and nothing else. The similarity-mapping technique produced demonstrates definite promise in the arena of early detection of malware and phishing domains. Full article

Indonesia is arguably a major player in worldwide rice production. Though white rice is the most predominantly cultivated, red, brown, and red rice are also very common. These types of rice are known to have different cooking properties that may be related to differences in their starch properties. Investigating the starch properties, especially the fine structure of their amylopectin, can help understand these differences. This study aims to investigate the starch characteristics of some Indonesian rice varieties by evaluating the starch granule morphology and size, molecular characteristics, amylopectin unit and internal chain profiles, and thermal properties. Starches were extracted from white rice (long grain (IR-64) and short grain (IR-42)), brown rice, red rice, and black rice cultivated in Java Island, Indonesia. IR-42 had the highest amylose content of 39.34% whilst the black rice had the least of 1.73%. The enthalpy of gelatinization and onset temperature of the gelatinization of starch granules were between 3.2 and 16.2 J/g and 60.1 to 73.8 °C, respectively. There were significant differences between the relative molar amounts of the internal chains of the samples. The two white rice and black rice had a significantly higher amount of A-chains, but a lower amount of B-chains and fingerprint B-chains (B_fp) than the brown and red rice. The average chain length (CL), short chain length (SCL), and external chain length (ECL) were significantly longer for the red rice and the black rice in comparison to both the white rice amylopectins. The long chain length (LCL) and internal chain length (ICL) of the sample amylopectins were similar. Rice starches were significantly different in the internal structure but not as much in their amylopectin unit chain profile. These results suggest the differences in their amylopectin clusters and building blocks. Full article

Aeolian sediments accumulated along the desert-loess transition zone of the Tengger Desert include heterogeneous textures and complex component structures in their grain-size distributions (GSD). However, the sources of these aeolian sediments have not been resolved due to the lack of large reference GSD sample datasets from adjacent regions that contain various types of sediments; such datasets could be used for fingerprinting based on grain-size properties. This lack of knowledge hinders our understanding of the mechanism of aeolian dust releases in these regions and the effects of forcing of atmospheric circulations on the transportation and accumulation of sediments in this region. In this study, we employed a multi-scale grain-size analysis method, i.e., a combination of the single-sample unmixing (SSU) and the parametric end-member modelling (PEMM) techniques, to resolve the component structures of sediments that had accumulated along the desert-loess transition zone of the Tengger Desert. We have also analyzed the component structures of GSDs of various types of sediments, including mobile and fixed sand dunes, lake sediments, and loess sediments from surrounding regions. Our results demonstrate that the patterns observed in coarser fractions of sediments (i.e., sediments with a mode grain size of >100 μm) from the transition zone match well with the patterns of component structures of several types of sediments from the interior of the Tengger Desert, and the patterns seen in the finer fractions (i.e., fine, medium, and coarse silts with a modal size of <63 μm) were broadly consistent with those of loess sediments from the Qilian Mountains. The deflation/erosion of loess from the Qilian Mountains by wind was the most important mechanism underlying the production of these finer grain-size fractions. The East Asia winter monsoon (EAWM) played a key role in transportation of the aeolian dust from these source regions to the desert-loess transition zone of the desert. Full article

With the rapid development of the internet of things (IoT), hundreds of millions of IoT devices, such as smart home appliances, intelligent-connected vehicles, and wearable devices, have been connected to the network. The open nature of IoT makes it vulnerable to cybersecurity threats. Traditional cryptography-based encryption methods are not suitable for IoT due to their complexity and high communication overhead requirements. By contrast, RF-fingerprint-based recognition is promising because it is rooted in the inherent non-reproducible hardware defects of the transmitter. However, it still faces the challenges of low inter-class variation and large intra-class variation among RF fingerprints. Inspired by fine-grained recognition in computer vision, we propose a fine-grained RF fingerprint recognition network (FGRFNet) in this article. The network consists of a top-down feature pathway hierarchy to generate pyramidal features, attention modules to locate discriminative regions, and a fusion module to adaptively integrate features from different scales. Experiments demonstrate that the proposed FGRFNet achieves recognition accuracies of 89.8% on 100 ADS-B devices, 99.5% on 54 Zigbee devices, and 83.0% on 25 LoRa devices. Full article

The redactable blockchain has emerged as a promising technique in mobile crowdsensing, allowing users to break immutability in a controlled manner selectively. Unfortunately, current fine-grained redactable blockchains suffer two significant limitations in terms of security and functionality, which severely impede their application in mobile crowdsensing. For security, the transparency of the blockchain allows anyone to access both the data and policy, which consequently results in a breach of user privacy. Regarding functionality, current solutions cannot support error tolerance during policy matching, thereby limiting their applicability in various situations, such as fingerprint-based and face-based identification scenarios. This paper presents a privacy-preserving fine-grained redactable blockchain with policy fuzzy matching, named PRBFM. PRBFM supports fuzzy policy matching and partitions users’ privileges without compromising user privacy. The idea of PRBFM is to leverage threshold linear secret sharing based on the Lagrange interpolation theorem to distribute the decryption keys and chameleon hash trapdoors. Additionally, we have incorporated a privacy-preserving policy matching delegation mechanism into PRBFM to minimize user overhead. Our security analysis demonstrates that PRBFM can defend against the chosen-ciphertext attack. Moreover, experiments conducted on the FISCO blockchain platform show that PRBFM is at least 7.8 times faster than existing state-of-the-art solutions. Full article

Although most indoor positioning systems use radio waves, such as Wi-Fi, Bluetooth, or RFID, for application in department stores, exhibition halls, stations, and airports, the accuracy of such technology is easily affected by human shadowing and multipath propagation delay. This study combines the earth’s magnetic field strength and Wi-Fi signals to obtain the indoor positioning information with high availability. Wi-Fi signals are first used to identify the user’s area under several kinds of environment partitioning methods. Then, the signal pattern comparison is used for positioning calculations using the strength change in the earth’s magnetic field among the east–west, north–south, and vertical directions at indoor area. Finally, the k-nearest neighbors (KNN) method and fingerprinting algorithm are used to calculate the fine-grained indoor positioning information. The experiment results show that the average positioning error is 0.57 m in 12-area partitioning, which is almost a 90% improvement in relation to that of one area partitioning. This study also considers the positioning error if the device is held at different angles by hand. A rotation matrix is used to convert the magnetic sensor coordinates from a mobile phone related coordinates into the geographic coordinates. The average positioning error is decreased by 68%, compared to the original coordinates in 12-area partitioning with a 30-degree pitch. In the offline procedure, only the northern direction data are used, which is reduced by 75%, to give an average positioning error of 1.38 m. If the number of reference points is collected every 2 m for reducing 50% of the database requirement, the average positioning error is 1.77 m. Full article

Indoor localization is one of the key techniques for location-based services (LBSs), which play a significant role in applications in confined spaces, such as tunnels and mines. To achieve indoor localization in confined spaces, the channel state information (CSI) of WiFi can be selected as a feature to distinguish locations due to its fine-grained characteristics compared with the received signal strength (RSS). In this paper, two indoor localization approaches based on CSI fingerprinting were designed: amplitude-of-CSI-based indoor fingerprinting localization (AmpFi) and full-dimensional CSI-based indoor fingerprinting localization (FuFi). AmpFi adopts the amplitude of the CSI as the localization fingerprint in the offline phase, and in the online phase, the improved weighted K-nearest neighbor (IWKNN) is proposed to estimate the unknown locations. Based on AmpFi, FuFi is proposed, which considers all of the subcarriers in the MIMO system as the independent features and adopts the normalized amplitudes of the full-dimensional subcarriers as the fingerprint. AmpFi and FuFi were implemented on a commercial network interface card (NIC), where FuFi outperformed several other typical fingerprinting-based indoor localization approaches. Full article

The manner of walking (gait) is a powerful biometric that is used as a unique fingerprinting method, allowing unobtrusive behavioral analytics to be performed at a distance without subject cooperation. As opposed to more traditional biometric authentication methods, gait analysis does not require explicit cooperation of the subject and can be performed in low-resolution settings, without requiring the subject’s face to be unobstructed/clearly visible. Most current approaches are developed in a controlled setting, with clean, gold-standard annotated data, which powered the development of neural architectures for recognition and classification. Only recently has gait analysis ventured into using more diverse, large-scale, and realistic datasets to pretrained networks in a self-supervised manner. Self-supervised training regime enables learning diverse and robust gait representations without expensive manual human annotations. Prompted by the ubiquitous use of the transformer model in all areas of deep learning, including computer vision, in this work, we explore the use of five different vision transformer architectures directly applied to self-supervised gait recognition. We adapt and pretrain the simple ViT, CaiT, CrossFormer, Token2Token, and TwinsSVT on two different large-scale gait datasets: GREW and DenseGait. We provide extensive results for zero-shot and fine-tuning on two benchmark gait recognition datasets, CASIA-B and FVG, and explore the relationship between the amount of spatial and temporal gait information used by the visual transformer. Our results show that in designing transformer models for processing motion, using a hierarchical approach (i.e., CrossFormer models) on finer-grained movement fairs comparatively better than previous whole-skeleton approaches. Full article

Epidote group minerals are one of the three most abundant kinds of heavy minerals in orogenic sediments, the other two being amphibole and garnet. They resist diagenesis better than amphibole and resist weathering in soils better than garnet. Their chemical composition and optical properties vary markedly and systematically with temperature and pressure conditions during growth. Useful information on the metamorphic grade of source rocks can thus be obtained by provenance analysis. In this study, we combine optical, SEM–EDS, and Raman analyses of nine standard crystals of epidote group minerals collected from different rock units exposed in the European Alps and Apennines and develop a Raman library for efficient discrimination of epidote, clinozoisite, zoisite, and allanite by establishing clear user-oriented relationships among optical properties, chemical composition, and Raman fingerprint. This new library allows us to distinguish and reliably determine, directly from their Raman spectrum, the chemical compositions of epidote group minerals during routine heavy mineral analyses of sand/sandstone and silt/siltstone samples down to the size of a few microns. The validity of the approach is illustrated by its application to 41 Bengal Fan turbidites collected from five cores during IODP Expedition 354 and ranging in grain size from medium sand to fine silt. Full article

Previous network feature extraction methods used for network anomaly detection have some problems, such as being unable to extract features from the original network traffic, or that they can only extract coarse-grained features, as well as that they are highly dependent on manual analysis. To solve these problems, this paper proposes a fine-grained and highly practical dynamic application fingerprint extraction method. By putting forward a fine-grained high-utility dynamic fingerprinting (Huf) algorithm to build a Huf-Tree based on the N-gram (every substring of a larger string, of a fixed length n) model, combining it with the network traffic segment-IP address transition (IAT) method to achieve dynamic application fingerprint extraction, and through the utility of fingerprint, the calculation was performed to obtain a more valuable fingerprint, to achieve fine-grained and efficient flow characteristic extraction, and to solve the problem of this method being highly dependent on manual analysis. The experimental results show that the Huf algorithm can realize the dynamic application of fingerprint extraction and solve the existing problems. Full article

A large number of IoT devices access the Internet. While enriching our lives, IoT devices bring potential security risks. Device identification is one effective way to mitigate security risks and manage IoT assets. Typical identification algorithms generally separate data capture and target identification into two parts. As a result, it is inefficient and coarse-grained to evaluate the results only once the identification process is complete and then adjust the data capture strategy afterward. To solve this problem, we propose a fine-grained probe-scheduling approach based on information feedback. First, we model the probe surface as three layers for IoT devices and define their relationships. Then, we improve the policy gradient algorithm to optimize the probe policy and generate the optimal probe sequence for the target device. We implement a prototype system and evaluate it on 53,000 IoT devices across various categories to show its wide applicability. The results indicate that our approach can achieve success rates of 96.89%, 93.43%, and 83.71% for device brand, model, and firmware version, respectively, and reduce the identification time by 55.96%. Full article

Channel state information (CSI) provides a fine-grained description of the signal propagation process, which has attracted extensive attention in the field of indoor positioning. The CSI signals collected by different fingerprint points have a high degree of discrimination due to the influence of multi-path effects. This multi-path effect is reflected in the correlation between subcarriers and antennas. However, in mining such correlations, previous methods are difficult to aggregate non-adjacent features, resulting in insufficient multi-path information extraction. In addition, the existence of the multi-path effect makes the relationship between the original CSI signal and the distance not obvious, and it is easy to cause mismatching of long-distance points. Therefore, this paper proposes an indoor localization algorithm that combines the multi-head self-attention mechanism and effective CSI (MHSA-EC). This algorithm is used to solve the problem where it is difficult for traditional algorithms to effectively aggregate long-distance CSI features and mismatches of long-distance points. This paper verifies the stability and accuracy of MHSA-EC positioning through a large number of experiments. The average positioning error of MHSA-EC is 0.71 m in the comprehensive office and 0.64 m in the laboratory. Full article