Existing biometric systems are predominantly built upon 2D biometrics which are vulnerable to presentation attacks, and have a limited coverage of the biometric surface. 3D biometrics is emerging due to the rapid development of 3D sensing technology. 3D biometrics is effective in defending against spoofing attacks and potentially offer more robust performance under different conditions. However, there exist many challenges in developing effective 3D biometric systems in terms of 3D biometrics reconstruction and recognition. In this article, we present a systematic survey on the latest developments of 3D biometric systems guided by our proposed taxonomy of methods for 3D biometrics.

The swift growth of e-commerce has led to an increase in fraudulent activities, which results in significant financial losses for both suppliers and consumers. Current research on detecting fraudulent activities within e-commerce platforms primarily focuses on analyzing individual user behavioral patterns over time or examining the spatial relationships among users. However, considering temporal or spatial contexts alone is not sufficient for fraud detection since they may not exist in real scenarios. Additionally, the issue caused by the imbalance of the data to be classified has not been solved in the field of fraud identification. To address these challenges, a novel scheme is proposed for fraudulent user detection in this work. The main contribution lies in the spatiotemporal fusion of user behavior and the layer-by-layer selective aggregation of graph models. Specifically, we utilize a long short-term memory model and a multi-layer perceptron model to extract the discriminant features from time-dependent and time-independent user behavior, respectively. This approach enhances the model’s ability to detect fraudulent users with different behavioral characteristics, including time-correlated and/or time-independent fraud behavior. Furthermore, a shared classifier is added to general graph neural network, it reclassifies the output of each layer of the graph model and reconstructs the spatial neighbor relationship. This little trick makes minority class samples select similar samples with a greater probability to build their spatial neighbor relationships, which can alleviate the issue of data imbalance. In the numerical experiments, three real datasets are used to validate the proposed scheme. Experiment results, including performance evaluation, comparison with existing benchmark approaches and ablation analysis, are presented and discussed.

In the Segment Routing over IPv6 (SRv6) network, a wide range of network events (e.g., attacks, intrusions, violations, malicious route announcements) may occur. Network management requires real-time monitoring of untrusted and unreliable environments (e.g., unsafe components and devices). Early localization of abnormal links causing violations in the SRv6 network helps minimize the compensation required for service unavailability. However, the overhead of the state-of-the-art methods does not scale efficiently to large-scale SRv6 networks and exhibit poor robustness to addressing various disturbances from unreliable networks. To cope with these challenges, we propose Glint, an in-band network telemetry framework to localize abnormal links in SRv6 networks. The key idea of Glint is sampling part of the information while the overall information is known. Glint provides probabilistic in-band collection to gather segment-level telemetry data, reducing overhead and improving efficiency. Glint also proposes distributed verification-based detection to enhance the trustworthiness of security assessments, further improving robustness against disturbances. In addition, we design selective telemetry that reduces telemetry reports while preserving security-relevant visibility. Our evaluations demonstrate that, compared to the state-of-the-art frameworks, Glint significantly reduces header bandwidth overhead by 75.6% and memory overhead by 48.7% while reducing false positives. We also implement Glint on the Intel Tofino switch, achieving over a 50% reduction in hardware resource consumption compared to existing methods.

Federated learning, as a distributed machine learning framework, enables participants to collaboratively train models by uploading only local gradients instead of exchanging local data. However, the malicious server might infer the participants' private data from the uploaded gradients or return incorrect aggregated results to participants. To tackle the above issues, numerous privacy-preserving and verifiable federated learning schemes have been developed. However, only a few of these schemes address collusion-resistant verification, and none considers the potential disputes between participants and server. In this paper, we put forward a privacy-preserving federated learning scheme with collusion-resistant verification and fair arbitration (PFLVA). In PFLVA, a novel verification method is designed, in which non-colluding participants can verify the correctness of aggregated gradient, even if up to $N-1$ participants collude with the server, where $N$ denotes the total number of online participants. We propose an efficient gradient encryption method to ensure participants' privacy while substantially reducing the computational overhead. We introduce a smart contract to locate the compromised entity when disputes arise and to achieve fair arbitration. Additionally, PFLVA allows participants to go offline without incurring additional computational or communication overheads for the online participants. We provide a comprehensive security analysis to demonstrate the correctness, verifiability, privacy protection, and collusion resistance of PFLVA. Experimental results demonstrate that PFLVA maintains high model accuracy while significantly reducing the computational and communication overhead for participants compared to related schemes.

Software Defined Network (SDN) has been widely used in modern network architecture. The integration of SDN into microgrid communication infrastructures offers enhanced flexibility, yet also introduces attack surfaces. As critical components of the Internet of Things (IoT) for energy systems, microgrid systems interact with numerous distributed sensors and controllers, making secure and reliable communications essential. It is well known that a labeled security dataset is indispensable for the community to validate the security solutions, in particular to the SDN intrusion detection systems. This study presents the SDN-MG25 dataset based on a realistic microgrid–SDN testbed, which is the first of its kind. This dataset contains benign network traffic generated from enterprise-level user activities, network flow records of microgrid communications, SDN activities, system call traces, and microgrid power measurements from an integrated SDN-based microgrid system. Additionally, a variety of SDN-related attack scenarios, such as fake link injection, flow rule tampering, and packet-in flooding, are implemented. A preliminary analysis is presented to evaluate the SDN-MG25 dataset. The SDN-MG25 dataset is publicly available for research in SDN-based microgrid and IoT energy environments.

Intrusion detection in microgrid systems is a cyber-physical task that requires correlating different data from networks, hosts, and endpoints to create actionable evidence. Existing approaches largely treat intrusion detection as a classification problem and provide explanations at the sample or feature level. However, these explanations lack physical interpretability and fail to reveal cross-modal interactions underlying system decisions. As a result, operators cannot reliably trace detected anomalies to the physical layer, limiting the ability to diagnose root causes. This leads to incorrect or delayed responses and potentially compromises the safety of microgrid operations. This work proposes a physical and data-link layer explainable intrusion detection framework via cross-modal evidence reasoning. This framework reformulates intrusion detection as an operation Q\&A task over structured multi-modal evidence, including network flows, Software-Defined Networking (SDN) states, system calls, and power measurements. By designing an evidence-based explanation mechanism, sample importance is aligned with structured evidence and aggregated into physical modalities to construct evidence representations. These representations are further transformed into structured features to build joint decision models, enabling the extraction of decision paths and their conversion into interpretable reasoning processes grounded in physical evidence. The proposed framework is evaluated on realistic cyber–physical microgrid datasets. It provides consistent and physically meaningful explanations, revealing distinct cross-modal evidence patterns across different cyber attacks. This work advances intrusion detection from samples to physical-layer reasoning, enabling trustworthy security analysis in microgrid systems.

The adoption of symmetric searchable encryption (SSE) has become increasingly common. However, many current SSE schemes assume an honest-but-curious cloud service provider (CSP) or necessitate significant overhead to manage a malicious CSP. Furthermore, most of these schemes are tailored for static datasets. Our paper presents an efficient SSE scheme that aims to address these challenges. To the best of our knowledge, this is the first scheme that supports dynamic datasets with forward and backward privacy, integrity verification of non-empty and empty search results, efficient search, non-interactive, light client, and both forward and inverted indexes simultaneously. In this paper, we present two novel approaches, Hexie and Jianding. Hexie implements secret sharing to conceal index entries, enabling dynamic updates, non-interactive interactions, and lightweight clients. To enhance the reliability of search results and address the problem of empty, incomplete, or inaccurate outcomes, we introduce the Jianding scheme as an extension of Hexie. It combines a chained MAC structure with a secret sharing scheme, which enables a client to verify the data integrity of the search result efficiently. Moreover, we propose graph-based dictionary sharding to enhance search efficiency. Finally, we conduct comprehensive experiments to validate the effectiveness of the proposed schemes.

Graph matching, as an important query technology, has been widely applied in various fields. With the increasing of graph data, users choose to encrypt a large number of graphs and store them in the cloud. Existing solutions to graph matching query over encrypted graphs require the user to execute a lot of time-consuming subgraph isomorphism (NP-complete problem) operations to extract the matched graphs, which inevitably brings heavy computational burden to the user. Therefore, how to reduce the number of subgraph isomorphisms is crucial for releasing the user from the heavy workload in a graph matching query scheme over encrypted graphs. In this article, we propose a secure and efficient scheme for graph matching query over encrypted graphs. The main idea is to classify the query graph into frequent subgraph and infrequent subgraph, and adopt different strategies to perform the matching query. We design the novel secure index based on the frequent subgraphs and the edge labels to reduce the number of subgraph isomorphisms. When the query graph is a frequent subgraph, the proposed scheme can directly produce the exact result owing to this secure index. The user does not need to perform any subgraph isomorphism in this case. When the query graph is an infrequent subgraph, the proposed scheme can return a set of data graphs very close to the exact result. As a result, the proposed scheme reduces the number of subgraph isomorphisms substantially. Formal security proof is provided. Extensive experiments on real-world data sets show that the proposed scheme reduces nearly 90% subgraph isomorphism.

One of the major challenges of microgrid systems is the lack of comprehensive Intrusion Detection System (IDS) datasets specifically for realistic microgrid systems' communication. To address the unavailability of comprehensive IDS datasets for realistic microgrid systems, this article presents a UNSW-MG24 dataset based on realistic microgrid testbeds. This dataset contains synthesized benign network traffic from different campus departments, network flow of attack activities, system call traces, and microgrid-specific data from an integrated Festo LabVolt microgrid system. Additionally, pivoting attacks and mimicry attacks are implemented to increase this dataset's heterogeneity for intrusion detection. Comprehensive features such as network flow attributes, system call parameters, and power measurement metrics are extracted from the generated dataset. Finally, a premiminary analysis is presented to elaborate the UNSW-MG24 dataset.This dataset is publicly available for research purposes at UNSW-MG24 dataset.

Due to increases in communication speed, computation, the liberalization of the electrical service business, and the environmental impact of traditional power generation technologies, Distributed Energy Resources (DERs) power systems such as microgrids are gaining in popularity. It is therefore imperative to develop resilient microgrid systems capable of withstanding cyber physical threats. The capacity to integrate Machine Learning (ML) and Deep Learning (DL) to analyze energy data has created opportunities for businesses and academia to explore the possibilities of enhancing the cybersecurity of microgrid systems. This study surveys and discusses recent developments, challenges, and opportunities in cybersecurity for microgrid systems, from both attack and defense perspectives. In this paper, we address the current state and future directions in cybersecurity in industrial communication networks, and endpoint security in distributed control systems. This paper discusses attack types including Man-In-The-Middle (MITM), False Data Injection (FDI), and Distributed Denial of Service (DDoS) attacks, alongside defensive mechanisms including AI-based detection and multilayered security frameworks. Furthermore, this survey offers comprehensive insights into benchmark datasets and open-source tools frequently utilized in experimental research and practical applications. It includes an in-depth comparison, discussion, and opportunities for future research to guide the research community's focus and advancing progress in the field.

Privacy-preserving combinatorial auctions, also known as sealed-bid combinatorial auctions, allow bidders to place bids on combinations of homogeneous or heterogeneous items without revealing the bidding prices (except for the winning ones) to any individual party. There is a significant lack of literature addressing this crucial and practical issue. We are bridging this gap by introducing two novel protocols for centralized and distributed auction systems. In the centralised approach, we integrate homomorphic encryption and DGK/Veugen secure comparison in a 2-server protocol for sealed-bid auctions. In the distributed approach we introduce a novel protocol based on homomorphic encryption and perturbation to allow an auction issuer and bidders to collaborate without any third party.

Blockchain provides a secure, decentralized, and distributed ledger system. However, scalability is a key limitation of blockchain, particularly for high-throughput applications. Direct Acyclic Graphs (DAGs) offer a promising solution by enabling the simultaneous processing of multiple transactions. However, achieving efficient and stable consensus in DAG-based systems is a challenge, as conventional linear ordering mechanisms do not fully address the complexities of DAG structures. In this article, we present a novel consensus protocol, specifically designed for DAG-based distributed ledgers, known as the Score-Based Periodic Ordering and Consensus Protocol. Our approach builds on the protocols of Phantom and GHOSTForge, focusing on enhanced order stability and scalability. It introduces score-based ordering at periodic checkpoints for individual minors. This selective checkpoint mechanism reduces computational overhead by limiting block reevaluation. The protocol also uses a score-based consistency check to ensure finality and resilience against double-spending. Our experiments show significant efficiency gains with stable order and reduced reconsideration across various checkpoint intervals. Furthermore, the experimental results demonstrate that global consensus and order convergence are achieved among miners. Stable order converges without extra communication, even under network heterogeneity. In addition, the evaluation further highlights resilience, with minimal uncommitted blocks even under varying network conditions.

Community search on attributed graphs has gained significant attention in recent years for its ability to provide meaningful and personalized results. Given a query community, a similar community search aims to identify the communities that are similar in structural and attributed characteristics to the query community. As real-world networks continue to grow in complexity and size, outsourcing graph data and search tasks to cloud servers not only saves local storage space but also significantly enhances search efficiency. Nonetheless, this inevitably raises concerns about data privacy since cloud servers are not completely trustworthy. In this paper, we research on privacy-preserving similar community search on graphs. We propose a privacy-preserving closest similar community search scheme for attributed graphs that leverages cloud servers to enhance search efficiency while safeguarding the sensitive information in the graph. We consider packaging communities using center vertices to evaluate relationships across communities without accessing details within the communities. To achieve this, we design a centrality score function that integrates attribute contribution and closeness centrality to identify the center vertex of a community. To ensure the security of sensitive information in the attribute graph, we construct three secure indexes for the original graph utilizing diverse cryptographic primitives. By searching secure indexes, cloud servers can answer the closest similar community searches without possessing any sensitive information about the attribute graph. We employ Paillier homomorphic cryptosystem and related protocols to support efficient and secure evaluation of the distance and similarity between two communities on secure indexes. The security analysis confirms that the proposed scheme can be against adaptive chosen-query attacks so as to achieve CQA2-security and experimental results demonstrate the efficiency of the proposed scheme.

3D fingerprint-based recognition and identification have several advantages compared to conventional 2D recognition systems. It is hygienic and secure due to its contactless sample collection. In addition to surface pattern, depth, curvature, and shape information can also be retrieved from 3D fingerprints which can be implemented for designing a more accurate and secure authentication system. While significant progress has been made recently, one of the great challenging issues is the fingerprint pixel's topological height makes it difficult to extract fingerprint ridge/valley patterns. To address this issue, in this article, the 3D fingerprint represented in 3D point cloud format is flattened using the B-spline curve fitting technique to reduce the impact of the pixel's topological height. The flattened point cloud is converted to a gray-scale image by using the relative height of the points in the flattened 3D point cloud. The generated gray-scale image is used for recognition via using a conventional 2D fingerprint identification method. The proposed method achieved an Equal Error Rate of 0.2974%, 0.28%, and 0.24% in three experiments, respectively, which is significantly more accurate than the existing methods.

Biometrics-based authentication mechanisms can address the built-in weakness of conventional password or token-based authentication in identifying genuine users. However, 2D-based fingerprint biometrics authentication faces the problem of sensor spoofing attacks. In addition, most 2D fingerprint sensors are contact-based, which can boost the spread of deadly diseases such as the COVID-19 virus. Three-dimensional fingerprint-based recognition is the emerging technology that can effectively address the above issues. A 3D fingerprint is captured contactlessly and can be represented by a 3D point cloud, which is strong against sensor spoofing attacks. To apply conventional 2D fingerprint recognition methods to 3D fingerprints, the 3D point cloud needs to be converted into a 2D gray-scale image. However, the contrast of the generated image is often not of good quality for direct matching. In this work, we propose an image segmentation approach using the deep learning U-Net to enhance the fingerprint contrast. The enhanced fingerprint images are then used for conventional fingerprint recognition. By applying the proposed method, the fingerprint recognition Equal Error Rate (EER) in experiment A and B improved from 41.32% and 41.97% to 13.96 and 12.49%, respectively, over the public dataset.

This article provides a comprehensive survey on the application of machine learning techniques for detecting False Data Injection Attacks (FDIA) in smart grids. It introduces a novel taxonomy categorizing detection methods based on key criteria such as AC and DC systems, performance metrics, bus size, algorithm selection, and specific subcategories of detection problems. The proposed taxonomy highlights the utility of Graph Neural Networks, autoencoders, and federated learning in addressing sub-problems like privacy preservation, generalized detection, locational detection, and attack classification. The survey underscores the importance of realistic, publicly accessible datasets and enhanced attack simulation techniques. Future research directions are suggested to further the development of robust FDIA detection methods in smart grids.

Biometric authentication is pivotal in protecting user privacy and ensuring smartphone security. Recent research indicates that the vibration response of hands can serve as a biometric for user authentication on mobile devices. However, the various grip gestures of users can introduce significant noise, disrupting hand biometrics-related signals and compromising authentication performance. This paper introduces HandID, an unobtrusive and gesture-independent user authentication method for smartphones that does not require large amounts of data from different gestures. Unlike existing methods, HandID does not require users to maintain specific gestures or interact with the touchscreen. It utilizes the built-in vibration motor to generate active vibrations and sense the user's hand through responses captured by the onboard accelerometer. HandID employs an adversarial learning model to handle gesture variations and proposes a novel hand biometric generation model to reduce the enrollment data required from users. Comprehensive experiments with 50 subjects show that HandID achieves an authentication accuracy of 92.5%, with a false acceptance rate (FAR) of 5.2% and a false rejection rate (FRR) of 5.6%. Security analyses demonstrate that HandID is resistant to zero-effort, s tatistical, a nd h ill-climbing a ttacks, a nd a u sability study indicates high user acceptance of HandID.

The prediction of the State of Health (SOH) of lithium-ion batteries is essential for applications such as electric vehicles, renewable energy storage, and portable electronic devices. Existing methods struggle with accuracy and interpretability under mixed operating modes and nonlinear degradation. To address this challenge, this article proposes a hybrid deep learning model based on parallel multi-channel bidirectional Long short-term memory (MBLSTM) and inverted transformer (iTransformer). The model extracts local temporal features through MBLSTM, and employs iTransformer, which leverages an attention mechanism, to identify global dependencies among variables. This integration enables the model to combine local dynamics with global interactions, thereby improving predictive performance. Additionally, an anomaly detection mechanism and the LIME (Local Interpretable Model-agnostic Explanations) method are introduced to analyze key influencing factors at anomalous prediction points.Comparative experiments on four real-world battery datasets with different charging/discharging strategies show that the proposed model outperforms existing methods and provides interpretable insights into anomalous predictions.

For security enhancement and privacy preservation, homomorphic encryption is deployed to facilitate computations among cipher texts. To transmit extra data over a cipher text without affecting its usage, lossless data hiding in encrypted domain (LDH-CT) has been developed by exploiting randomness introduced in homomorphic encryption such as Paillier, BGN and NTRU schemes. As data extraction without decrypting the cipher text has been accomplished with BGN, how to retrieve the hidden data after decryption remains unexplored. In this article, an LDH-CT scheme named random number replacement and partitioning (RNRP) is proposed to achieve the versatility of data embedding by designing two algorithms, namely the random number replacement algorithm to embed data to be extracted after decryption and the cipher value selection algorithm to embed data to be extracted without decryption. For the first time, confidential information to be extracted after decryption can be hidden into a cipher text by a third party without knowing its plain text. For convenience in performance evaluation, the proposed algorithms and the RNRP scheme are applied to a set of test images. Experimental results and comparisons with the state-of-the-art schemes demonstrate the better applicability of the proposed scheme, such as in saving bandwidth.