User Identification Method Research Articles

Research on the identification and detection technology of electricity theft in power distribution areas under a smart grid environment

Abstract With the extensive application of smart meters, power companies can access a wealth of customer electricity usage data, which has not yet been fully utilized. This paper proposes a substation area user identification and electricity theft detection method based on smart meter data, aiming to enhance the level of intelligent management of power grid enterprises and reduce operational costs. By using the grey relational analysis method, analyzing the measurement data collected by smart meters at different time points, it is possible to determine the substation area and phase to which the user belongs and to detect electricity theft behavior. Finally, through simulation, the effectiveness of the proposed method is verified.

The digital pheromone: Building digital identity of smartphone users based on time-varying multivariates

The increasing reliance on smartphones necessitates secure, accurate user identification methods. This study introduces “digital pheromones”, a novel digital identity creation method based on smartphone user behaviors. Represented by a 56-bit binary number, this identity is derived from quantization and concatenation of 14 time-varying smartphone variables. Motivated by the need for enhanced security and privacy, the study employs statistical analysis to measure user behavior dispersion, showing that wider dispersion variables facilitate faster user uniqueness at lower quantization levels. The results demonstrate 100% uniqueness, ensuring no two users share the same 56-bit identity. This research offers a robust framework for future advancements in smartphone user authentication and identification systems.

Abnormal User Identification in Substation Area Based on Restricted Ridge Regression Model

The rapid development of the smart grid affects every aspect of people’s lives at all times. Among them, the identification accuracy of power metering anomalies is a serious problem that needs to be solved urgently. To further improve the identification accuracy of power metering anomalies, a user identification method for substation area anomalies is designed based on the restricted ridge regression model. First, a clustering algorithm is designed using high-frequency voltage data of users in the substation area to conduct phase separation and terminal branch identification for each user metering point in the substation area. Then establish a substation area energy conservation model equation set, and merge the end branch users based on genetic algorithm clustering results, thereby reducing the unknown quantity dimension. Then, a line resistance-constrained condition is constructed using the topological structure, and the model equation set is solved using quadratic programming based on the constrained ridge regression method to obtain the measurement point error results of users in the substation area. The experimental results show that this recognition method’s clustering effect and recognition accuracy are high, with a recognition rate of 98.21%. It can be seen that this abnormal user identification method has application value.

A Trajectory-Oriented Locality-Sensitive Hashing Method for User Identification

A Trajectory-Oriented Locality-Sensitive Hashing Method for User Identification

FedCrow: Federated-Learning-Based Data Privacy Preservation in Crowd Sensing

In the process of completing large-scale and fine-grained sensing tasks for the new generation of crowd-sensing systems, the role of analysis, reasoning, and decision making based on artificial intelligence has become indispensable. Mobile crowd sensing, which is an open system reliant on the broad participation of mobile intelligent terminal devices in data sensing and computation, poses a significant risk of user privacy data leakage. To mitigate the data security threats that arise from malicious users in federated learning and the constraints of end devices in crowd-sensing applications, which are unsuitable for high computational overheads associated with traditional cryptographic security mechanisms, we propose FedCrow, which is a federated-learning-based approach for protecting crowd-sensing data that integrates federated learning with crowd sensing. FedCrow enables the training of artificial intelligence models on multiple user devices without the need to upload user data to a central server, thus mitigating the risk of crowd-sensing user data leakage. To address security vulnerabilities in the model data during the interaction process in federated learning, the system employs encryption methods suitable for crowd-sensing applications to ensure secure data transmission during the training process, thereby establishing a secure federated-learning framework for protecting crowd-sensing data. To combat potential malicious users in federated learning, a legitimate user identification method based on the user contribution level was designed using the gradient similarity principle. By filtering out malicious users, the system reduces the threat of attacks, thereby enhancing the system accuracy and security. Through various attack experiments, the system’s ability to defend against malicious user attacks was validated. The experimental results demonstrate the method’s effectiveness in countering common attacks in federated learning. Additionally, through comparative experiments, suitable encryption methods based on the size of the data in crowd-sensing applications were identified to effectively protect the data security during transmission.

A Long-and Short-term Hot User Identification Method Based on Local Outlier Density

Power systems have many users; however, there are relatively few users focused on high-quality power supplies (namely, hot users), such as external query users, electricity theft, and information fraud from billions of users. Starting from power load data, this paper postulates that abnormal electricity load users are essential components of hot users. According to the characteristics of regional power loads, this paper first builds a cross-judgment method for abnormal loads to improve the identification accuracy, including spatial and temporal abnormal load detection methods. As the spatial method, the abnormal load detection based on the local outlier factor (LOF) algorithm detects different users from similar users using several constructed dimensional features. As the temporal method, dynamic abnormal load detection based on the short-term sequential (SS) algorithm is designed to improve the LOF missing alarm rate and computing efficiency with multi-dimensional features. This also accomplished abnormal load detection from historical dimensions. A long- and short-term hot user detection method is designed based on the outlier density in response to the insufficient support of single-point abnormal loads to judge hot users. Finally, experiments on the user load data in a certain region within a year test the abnormal detection method for long- and short-term hot user detection based on local outlier density (LOD). The experimental results indicate that this method can detect abnormal loads and search hot users. The long- and short-term hot user detection method based on the LOD can meet practical needs when only using user load data and has good generalization abilities.

A Review of User Identification Methods Based on Digital Fingerprint

Methods of user identification based on digital fingerprints are considered. The main approaches for the browser fingerprints creation which is installed on the user's device and characterizes the device belonging to the user are presented. The methods used to identify a person (user) during the operation of the device are also described. Methods using both the dynamics of keystrokes and interactions with the touch screen, voice and geolocation data, as well as behavioral biometrics and behavioral profile are presented. The concept of continuous authentication is described as a development of the identification approach. A list of publicly available data sets mentioned in the studies reviewed in the review is provided, with links to download them.

Who is Alyx? A new behavioral biometric dataset for user identification in XR

Introduction: This paper addresses the need for reliable user identification in Extended Reality (XR), focusing on the scarcity of public datasets in this area.Methods: We present a new dataset collected from 71 users who played the game “Half-Life: Alyx” on an HTC Vive Pro for 45 min across two separate sessions. The dataset includes motion and eye-tracking data, along with physiological data from a subset of 31 users. Benchmark performance is established using two state-of-the-art deep learning architectures, Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU).Results: The best model achieved a mean accuracy of 95% for user identification within 2 min when trained on the first session and tested on the second.Discussion: The dataset is freely available and serves as a resource for future research in XR user identification, thereby addressing a significant gap in the field. Its release aims to facilitate advancements in user identification methods and promote reproducibility in XR research.

A Study of Existing Approaches to Transaction Analysis in the Bitcoin Network

Today, the Bitcoin network faces a number of challenges, such as flawed user identification and fraudulent transaction methods that are used by criminals to conduct illegal activities. As a result, there is a growing need to improve existing tools for tracking transactions, as well as to develop new methods for identifying money in the Bitcoin network. The paper presents a study and systematization of the subject area problems, and also considers possible approaches to neutralize it. The purpose of this work is to analyze the existing methods of transaction verification of the Bitcoin network. Within the framework of the work, a system architecture is proposed, which includes a comprehensive approach to the process of transaction analysis. Each of the stages of this process is described: information gathering, aggregation, processing and analysis. An extended set of empirical rules (heuristics) for transaction analysis, which are used in existing clustering methods, is considered. The results of this work will provide an opportunity to improve the existing Bitcoin transaction verification methods and develop a new one with the possibility of increasing the efficiency of the process of identification of illegally obtained funds and their sources.

PURE: Passive Multi-Person Identification via Footstep for Mobile Service Networks

Recently, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">passive behavioral biometrics</i> (e.g., gesture or footstep) acquired from wireless networks or mobile services have become promising complements to conventional user identification methods (e.g., face or fingerprint) under special situations, yet existing sensing technologies require lengthy measurement traces and cannot identify multiple users at the same time. To this end, we propose PURE as a passive multi-person identification system leveraging deep learning enabled footstep separation and recognition. PURE passively identifies a user by identifying the unique “footprints” in its footstep. Different from existing gait-enabled recognition systems incurring a long sensing delay to acquire many footsteps, PURE can recognize a person by as few as only one step, substantially cutting the identification latency. To make PURE adaptive to walking pace variations, environmental dynamics, and even unseen targets, we apply an adversarial learning technique to improve its domain generalisability and identification accuracy. Finally, PURE is robust against replay attack, enabled by the richness of footstep and spatial awareness. We implement a PURE prototype using commodity hardware and evaluate it in typical indoor settings. Evaluation results demonstrate a cross-domain identification accuracy of over 90%.

Mobile user identification based on road networks and multiple distance measures

AbstractMobile user identification aims at matching different mobile devices of the same user using trajectory data, which has attracted extensive research in recent years. Most of the previous work extracted trajectory features based on regular grids, which will lead to incorrect feature representation due to lack of geographic information. Besides, most trajectory similarity models only considered one single distance measure to calculate the similarity between users, which ignore the connection between different distance measures and may lead to some false matches. In light of this, we present a novel user identification method based on road networks and multiple distance measures in this article. The proposed method segments a city map into several grids and road segments based on road networks. Then it extracts location and road information of trajectories to jointly construct user features. Multiple distance measures are fused by a discriminant model to improve the effect of user identification. Experiments on real GPS trajectory datasets show that our proposed method outperforms related similarity measure methods and is stable for mobile user identification. Meanwhile, our method can also achieve good identification results even on sparse trajectory datasets.

User identification for knowledge graph construction across multiple online social networks

User identification across multiple online social networks is beneficial for building knowledge graphs. Under privacy protection considerations, researchers have shown increasing interest in user identification based on username similarity. However, existing solutions rely on manual features extracted by domain experts and do not exploit the deep semantic features of usernames. Moreover, existing solutions are limited to monolingual user names such as English or Chinese, ignoring other multilingual usernames. This paper proposes a multilingual pre-trained model-based username similarity method for user identification across multiple online social networks. First, we use many multilingual corpora to enable the model to learn more semantic information and extract deep semantic features of usernames. Then, fine-tuning is performed on our constructed dataset of multilingual usernames across multiple online social networks. Ultimately assess the similarity of user identities across multiple online social networks. Our method facilitates user identification with limited data. Finally, the efficiency of our model is verified on three constructed real-world multilingual username datasets across multiple online social networks and compared with existing state-of-the-art methods. Experimental results show that the proposed algorithm outperforms the compared algorithms.

A Binary-Search-Based Locality-Sensitive Hashing Method for Cross-Site User Identification

In recent years, we have witnessed increasing attention paid to the problem of cross-site user identification (CSUI) with the bloom of social media. Despite noticeable progress in this field, the problem of enormous computation posed by the full-scale pairwise comparison still remains unsolved, which constrains the real application of the state-of-the-art approaches, especially when the user number reaches tens of millions. To address this issue, we propose a novel trajectory-oriented method, which aims at incorporating the locality-sensitive hashing (LSH) technology into the strategy of approximate nearest neighbors searching. Specifically, we design an LSH function based on binary search (BLSH) for processing user trajectory easily, and by searching the BLSH buckets, we cluster similar users to avoid the onerous full-scale pairwise comparison. Moreover, a hierarchy of discrete attention is devised to further improve the effectiveness, which develops our method into the mature one, hierarchically attentioned binary-search-based LSH (HA-BLSH). Extensive experiments on ground-truth datasets demonstrate that our HA-BLSH outperforms the cutting-edge approaches on efficiency and effectiveness. The further discussion indicates that our method is also quite flexible and able to accommodate more scenarios.

The Empty-Nest Power User Management Based on Data Mining Technology.

With the aging of the social population structure, the number of empty-nesters is also increasing. Therefore, it is necessary to manage empty-nesters with data mining technology. This paper proposed an empty-nest power user identification and power consumption management method based on data mining. Firstly, an empty-nest user identification algorithm based on weighted random forest was proposed. Compared with similar algorithms, the results indicate that the performance of the algorithm is the best, and the identification accuracy of empty-nest users is 74.2%. Then a method for analyzing the electricity consumption behavior of empty-nest users based on fusion clustering index adaptive cosine K-means was proposed, which can adaptively select the optimal number of clusters. Compared with similar algorithms, the algorithm has the shortest running time, the smallest Sum of the Squared Error (SSE), and the largest mean distance between clusters (MDC), which are 3.4281 s, 31.6591 and 13.9513, respectively. Finally, an anomaly detection model with an Auto-regressive Integrated Moving Average (ARIMA) algorithm and an isolated forest algorithm was established. The case analysis shows that the recognition accuracy of abnormal electricity consumption for empty-nest users was 86%. The results indicate that the model can effectively detect the abnormal behavior of empty-nest power users and help the power department to better serve empty-nest users.

Proof of Transaction (PoTx) Based Traceability System for an Agriculture Supply Chain

Tracing the origin of the product in the agriculture supply chain offers food safety, identifying the root cause of the food hazard if any, information connectivity between the participating stakeholders and to achieve customer trust. Blockchain is a distributed ledger technology whose transactions can be recorded in a decentralised and immutable ledger. Several applications were developed in the last few years for blockchain based - traceability systems. Most of the work concentrated on information retrieval and system scalability. Still, promising research has not been seen for a blockchain traceability system with user identification methods. This work presents a blockchain system with user identification and access control mechanisms to fill the gap for each supply chain participant. In addition, the system also proposes a novel Proof of Transaction (PoTx) consensus algorithm designed to achieve scalability with reduced communication overhead and computation power. A novel PoTx algorithm is developed by choosing a random validator from a consensus group based on the transaction count to enforce the fault-tolerance of the system. The proposed system with user identification method allows to prevent and identify product adulteration while tracing the agriculture product.

ENHANCED CRYPTOGRAPHIC SECURITY OF AGGREGATED DIGITAL SIGNATURES THROUGH UTILIZATION OF A UNIFIED AUTHENTICATION FRAMEWORK

The significance of this research lies in safeguarding user information and data against unauthorized alterations and destruction. Throughout the study, key aspects were explored, including user identification and authen-tication methods, cryptographic authentication protocols, digital signature properties, and strategies to enhance the cryptographic robustness of digital signatures. These strategies involved scrutinizing authentication methods using the Saati technique and the RSA algorithm. The analysis revealed that the attributes employed for digital signature recognition are highly resistant to replication, and the verification process is notably efficient. The height-ened cryptographic resilience of the electronic signature achieved through the RSA algorithm stems from the incorporation of a public key certificate. Consequently, a larger encryption key size corresponds to heightened sys-tem reliability and electronic digital signature security. While the utiliza-tion of the RSA algorithm results in encrypted text that is approximately 10 times larger than the original, its stability is comparatively increased.

Study on Noise Reduction and Data Generation for sEMG Spectrogram Based User Recognition

With the spread of the modern media industry, harmful genre contents are indiscriminately disseminated to teenagers. The password identification method used to block sensational and violent genre content has become a problem that teenagers can easily steal. Therefore, a user identification method with less risk of theft and hacking is required. The surface EMG (sEMG) signal, which is an electrical signal generated inside the body and has individual features, is being studied as a next-generation user identification method. sEMG involves measuring an individual’s unique muscular strength activated over time as digital signals, thus giving it the advantage of generating different signal patterns. However, it is difficult to constantly and repeatedly acquire each motion signal and the number of repetitions for each motion is insufficient, thus there is a limit to improving user identification accuracy. In this paper, we propose a user identification system that solves the problem of insufficient data by applying the matching pursuit that enables signal generation to the sEMG signal from which the resting signal has been removed and improves classification accuracy by extracting STFT-based time–frequency features. As a result of the experiment, the user identification accuracy of the sEMG spectrogram with the resting state signal removed was 85.4%. In addition, when the training data were increased through data generation, the accuracy was improved, showing a user identification accuracy of 96.1%. Improved user recognition accuracy was confirmed when the training data of the sEMG signal from which the resting signal was removed were increased and multidimensional features including time–frequency were used.

Stack LSTM-Based User Identification Using Smart Shoes with Accelerometer Data.

In this study, we propose a long short-term memory (LSTM)-based user identification method using accelerometer data from smart shoes. In general, for the user identification with human walking data, we require a pre-processing stage in order to divide human walking data into individual steps. Next, user identification can be made with divided step data. In these approaches, when there exist partial data that cannot complete a single step, it is difficult to apply those data to the classification. Considering these facts, in this study, we present a stack LSTM-based user identification method for smart-shoes data. Rather than using a complicated analysis method, we designed an LSTM network for user identification with accelerometer data of smart shoes. In order to learn partial data, the LSTM network was trained using walking data with random sizes and random locations. Then, the identification can be made without any additional analysis such as step division. In the experiments, user walking data with 10 m were used. The experimental results show that the average recognition rate was about 93.41%, 97.19%, and 98.26% by using walking data of 2.6, 3.9, and 5.2 s, respectively. With the experimental results, we show that the proposed method can classify users effectively.

Developing of keystroke dynamics analyzing systems using a presentation based on state contexts

This aim of the article is to discusses the main static and dynamic user identification methods by keystroke dynamics. As part of the research, a generalized way of representing the process of typing on the keyboard based on the sequential change of the keyboard state was proposed. The definition of the keyboard state context, which is the basis for the dynamic identification procedure, is formulated. The proposed approach will make it possible to apply a variety of static identification methods, significantly expanding the set of methods used for dynamic user identification by keystroke dynamics.

Never Query Alone: A distributed strategy to protect Internet users from DNS fingerprinting attacks

The Domain Name System (DNS) plays an essential role in everyday Internet activities. However, unauthorized access to DNS-generated traffic also poses some serious privacy concerns. For instance, DNS traffic traces can be processed by third parties to identify an Internet user by means of behavioral analysis (i.e., a technique that employs machine learning classifiers to link multiple pieces of traffic belonging to the same person). In general, the more sessions an attacker can link, the more he or she will learn about the interests of an individual, and the more likely that the identity of this user will be revealed. The development of such methods of user identification has been the focus of several pieces of research, and currently, there are several strategies to obtain behavioral fingerprints from DNS traces. However, only a few works have proposed countermeasures to protect users against this privacy threat on the Internet. Furthermore, new technologies such as DNS-over-TLS, DNS-over-HTTPS, or DNS over QUIC can potentially render available countermeasures ineffective. This paper proposes Never Query Alone (NQA), a strategy that allows a set of nodes to modify their DNS query patterns to mitigate the risk of being tracked by DNS resolvers. In NQA, users forward their DNS queries through their neighbors in such a way that the identification accuracy achieved by the attackers is proportionally reduced as the number of participant nodes is increased. A second strategy, called NQA-SA, is also proposed. NQA-SA decreases the accuracy of the attackers to nearly 1%, independently of the number of participant nodes. Both proposed countermeasures reduce the accuracy of the classifiers at the cost of increasing the delay of the DNS query resolution process. Thus, a trade-off between privacy and delay arises, which is theoretically studied in this work by means of queueing analysis. Experimental results with real networks demonstrate that the proposed countermeasures can significantly degrade the accuracy of commonly used machine learning classifiers, thus increasing the privacy protection of individuals on the Internet.