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The article offers a method for user identification in smart homes through touch-screen operation data, addressing privacy concerns associated with camera-based identification. Topics include the challenges of user-aware device usage detection in smart homes, the development of a non-intrusive user identification technique based on touch-screen button operations, and the application of supervised learning to accurately identify users.

Q&A platforms are vital sources of information but often face challenges related to their high ratios of passive to active contributors, which can impede knowledge construction and information exchange on the platforms. This study introduced a novel method for identifying trend discoverers, key users who can detect and initiate discussions on emerging question trends, through response order analysis of data from Zhihu and Stack Overflow. This study underscores the significant role of trend discoverers in influencing question popularity. Trend discoverers not only exhibit higher engagement in knowledge‐sharing activities but also participate in discussions across a broader range of topics compared to regular users. The insights derived from this research have crucial implications for improving the development and functionality of Q&A platforms. [ABSTRACT FROM AUTHOR]

Object detection and action/gesture recognition have become imperative in security and surveillance fields, finding extensive applications in everyday life. Advancement in such technologies will help in furthering cybersecurity and extended reality systems through the accurate identification of users and their interactions, which plays a pivotal role in the security management of an entity and providing an immersive experience. Essentially, it enables the identification of human–object interaction to track actions and behaviors along with user identification. Yet, it is performed by traditional camera-based methods with high difficulties and challenges since occlusion, different camera viewpoints, and background noise lead to significant appearance variation. Deep learning techniques also demand large and labeled datasets and a large amount of computational power. In this paper, a novel approach to the recognition of human–object interactions and the identification of interacting users is proposed, based on three-dimensional hand pose data from an egocentric camera view. A multistage approach that integrates object detection with interaction recognition and user identification using the data from hand joints and vertices is proposed. Our approach uses a statistical attribute-based model for feature extraction and representation. The proposed technique is tested on the HOI4D dataset using the XGBoost classifier, achieving an average F1-score of 81% for human–object interaction and an average F1-score of 80% for user identification, hence proving to be effective. This technique is mostly targeted for extended reality systems, as proper interaction recognition and users identification are the keys to keeping systems secure and personalized. Its relevance extends into cybersecurity, augmented reality, virtual reality, and human–robot interactions, offering a potent solution for security enhancement along with enhancing interactivity in such systems. [ABSTRACT FROM AUTHOR]

User identification is an essential technical support for downstream tasks such as recommendation systems, information retrieval, and collaborative filtering. Computing the similarity between user display names through classifiers is an effective solution for user identification across social networks. However, there are two problems with existing methods. Applying expert domain knowledge to extract handcrafted features of display names ignores the semantic information, resulting in poor performance of these methods. Selecting influential display names, and handcrafted features in user identification problems are also one of the difficulties. To solve these two problems, we propose a method based on the multi-feature fusion of display names using gated units. First, we extract the deep semantic features of display names through the BERT pre-trained multi-language model. Then, the gated mechanism is applied to select the handcrafted features we extracted to retain the essential features. Then, the adaptive factors are used to fuse handcrafted features and deep features to obtain user identification results across social networks. Finally, the efficiency of our model is verified on three constructed real-world multilingual display names 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. [ABSTRACT FROM AUTHOR]

User identification is an essential technical support for downstream tasks such as recommendation systems, information retrieval, and collaborative filtering. Computing the similarity between user display names through classifiers is an effective solution for user identification across social networks. However, there are two problems with existing methods. Applying expert domain knowledge to extract handcrafted features of display names ignores the semantic information, resulting in poor performance of these methods. Selecting influential display names, and handcrafted features in user identification problems are also one of the difficulties. To solve these two problems, we propose a method based on the multi-feature fusion of display names using gated units. First, we extract the deep semantic features of display names through the BERT pre-trained multi-language model. Then, the gated mechanism is applied to select the handcrafted features we extracted to retain the essential features. Then, the adaptive factors are used to fuse handcrafted features and deep features to obtain user identification results across social networks. Finally, the efficiency of our model is verified on three constructed real-world multilingual display names 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.

Door access control systems are important to protect the security and integrity of physical spaces. Accuracy and speed are important factors that govern their performance. In this paper, we investigate a novel approach to identify users by measuring patterns of their interactions with a doorknob via an embedded accelerometer and gyroscope and by applying deep-learning-based algorithms to these measurements. Our identification results obtained from 47 users show an accuracy of 90.2%. When the sex of the user is used as an input feature, the accuracy is 89.8% in the case of male individuals and 97.0% in the case of female individuals. We study how the accuracy is affected by the sample duration, finding that is its possible to identify users using a sample of 0.5 s with an accuracy of 68.5%. Our results demonstrate the feasibility of using patterns of motor activity to provide access control, thus extending with it the set of alternatives to be considered for behavioral biometrics. [ABSTRACT FROM AUTHOR]

In an era marked by escalating concerns about digital security, biometric identification methods have gained paramount importance. Despite the increasing adoption of biometric techniques, keystroke dynamics analysis remains a less explored yet promising avenue. This study highlights the untapped potential of keystroke dynamics, emphasizing its non-intrusive nature and distinctiveness. While keystroke dynamics analysis has not achieved widespread usage, ongoing research indicates its viability as a reliable biometric identifier. This research builds upon the existing foundation by proposing an innovative deep-learning methodology for keystroke dynamics-based identification. Leveraging open research datasets, our approach surpasses previously reported results, showcasing the effectiveness of deep learning in extracting intricate patterns from typing behaviors. This article contributes to the advancement of biometric identification, shedding light on the untapped potential of keystroke dynamics and demonstrating the efficacy of deep learning in enhancing the precision and reliability of identification systems. [ABSTRACT FROM AUTHOR]

Due to the distinct functionalities of various social network platforms, users often register accounts on different platforms, posing significant challenges for unified user management. However, current multi-social-network user identification algorithms heavily rely on user attributes and cannot perform user identification across multiple social networks. To address these issues, this paper proposes two identity recognition models. The first model is a cross-social-network user identification model based on bidirectional GCN. It calculates user intimacy using the Jaccard similarity coefficient and constructs an adjacency matrix to accurately represent user relationships in the social network. It then extracts cross-social-network user information to accomplish user identification tasks. The second model is the multi-network feature user identification (MNF-UI) model, which introduces the concept of network feature vectors. It effectively maps the structural features of different social networks and performs user identification based on the common features of seed nodes in the cross-network environment. Experimental results demonstrate that the bidirectional GCN model significantly outperforms baseline algorithms in cross-social-network user identification tasks. The MNF-UI (multi-network feature user identification) model can operate in situations with two or more networks with inconsistent structures, resulting in improved identification accuracy. These two user identification algorithms provide technical and theoretical support for in-depth research on social network information integration and network security maintenance. [ABSTRACT FROM AUTHOR]

Recent studies actively researching user identification utilizing physiological information to verify identity. However, there is a problem with biometric information exposed to the outside of the body being susceptible to forgery and falsification. Therefore, studies on user identification systems based on biometric signals generated as electrical signals inside the body are necessary to strengthen security with high security and continuous authentication. Among the time-frequency (TF) features applied to existing nonlinear biosignals, short-time fourier transform (STFT) cannot simultaneously improve time-frequency resolution, so there is a limit to improving user identification accuracy. Therefore, this paper introduces a customized constant q transform (CQT) for time-frequency resolution adjustment applied to multiple biosignals. Additionally, a multi-stream conformer combining a convolutional neural network (CNN) and transformer is employed to improve user identification accuracy. Test results confirmed that when using CQT features and a multi-stream conformer for electrocardiogram (ECG) and electroencephalogram (EEG) signals, the accuracy of user identification using both ECG and EEG signals was 97.6%, an improvement of 0.8% or more compared to the accuracy of ECG and EEG single biosignals and CNN-based user identification.

Vision-aided wireless communication is attracting increasing interest and finding new use cases in various wireless communication applications. These vision-aided communication frameworks leverage visual data captured, for example, by cameras installed at the infrastructure or mobile devices to construct some perception about the communication environment through the use of deep learning and advances in computer vision and visual scene understanding. Prior work has investigated various problems such as vision-aided beam, blockage, and hand-off prediction in millimeter wave (mmWave) systems and vision-aided covariance prediction in massive MIMO systems. This prior work, however, has focused on scenarios with a single object (user) in front of the camera. In this paper, we define the user identification task as a key enabler for realistic vision-aided communication systems that can operate in crowded scenarios and support multi-user applications. The objective of the user identification task is to identify the target communication user from the other candidate objects (distractors) in the visual scene. We develop machine learning models that process either one frame or a sequence of frames of visual and wireless data to efficiently identify the target user in the visual/communication environment. Using the large-scale multi-modal sense and communication dataset, DeepSense 6G, which is based on real-world measurements, we show that the developed approaches can successfully identify the target users with more than 97% accuracy in realistic settings. This paves the way for scaling the vision-aided wireless communication applications to real-world scenarios and practical deployments.

Object detection and action/gesture recognition have become imperative in security and surveillance fields, finding extensive applications in everyday life. Advancement in such technologies will help in furthering cybersecurity and extended reality systems through the accurate identification of users and their interactions, which plays a pivotal role in the security management of an entity and providing an immersive experience. Essentially, it enables the identification of human–object interaction to track actions and behaviors along with user identification. Yet, it is performed by traditional camera-based methods with high difficulties and challenges since occlusion, different camera viewpoints, and background noise lead to significant appearance variation. Deep learning techniques also demand large and labeled datasets and a large amount of computational power. In this paper, a novel approach to the recognition of human–object interactions and the identification of interacting users is proposed, based on three-dimensional hand pose data from an egocentric camera view. A multistage approach that integrates object detection with interaction recognition and user identification using the data from hand joints and vertices is proposed. Our approach uses a statistical attribute-based model for feature extraction and representation. The proposed technique is tested on the HOI4D dataset using the XGBoost classifier, achieving an average F1-score of 81% for human–object interaction and an average F1-score of 80% for user identification, hence proving to be effective. This technique is mostly targeted for extended reality systems, as proper interaction recognition and users identification are the keys to keeping systems secure and personalized. Its relevance extends into cybersecurity, augmented reality, virtual reality, and human–robot interactions, offering a potent solution for security enhancement along with enhancing interactivity in such systems.

Radio frequency (RF) technology has been applied to enable advanced behavioral sensing in human-computer interaction. Due to its device-free sensing capability and wide availability on Internet of Things devices. Enabling finger gesture-based identification with high accuracy can be challenging due to low RF signal resolution and user heterogeneity. In this paper, we propose MeshID, a novel RF-based user identification scheme that enables identification through finger gestures with high accuracy. MeshID significantly improves the sensing sensitivity on RF signal interference, and hence is able to extract subtle individual biometrics through velocity distribution profiling (VDP) features from less-distinct finger motions such as drawing digits in the air. We design an efficient few-shot model retraining framework based on first component reverse module, achieving high model robustness and performance in a complex environment. We conduct comprehensive real- world experiments and the results show that MeshID achieves a user identification accuracy of 95.17% on average in three indoor environments. The results indicate that MeshID outperforms the state-of-the-art in identification performance with less cost. [ABSTRACT FROM AUTHOR]

This paper compares well-known convolutional neural networks (CNN) models for facial recognition. For this, it uses its database created from two registered users and an additional category of unknown persons. Eight different base models of convolutional architectures were compared by transfer of learning, and two additional proposed models called shallow CNN and shallow directed acyclic graph with CNN (DAG-CNN), which are architectures with little depth (six convolution layers). Within the tests with the database, the best results were obtained by the GoogLeNet and ResNet-101 models, managing to classify 100% of the images, even without confusing people outside the two users. However, in an additional real-time test, in which one of the users had his style changed, the models that showed the greatest robustness in this situation were the Inception and the ResNet-101, being able to maintain constant recognition. This demonstrated that the networks of greater depth manage to learn more detailed features of the users' faces, unlike those of shallower ones; their learning of features is more generalized. Declare the full term of an abbreviation/acronym when it is mentioned for the first time. [ABSTRACT FROM AUTHOR]

Analyzing the influence of people and nodes in social networks has attracted a lot of attention. Social networks gain meaning, despite the groups, associations, and people interested in a specific issue or topic, and people demonstrate their theoretical and practical tendencies in such places. Influential nodes are often identified based on the information related to the social network structure and less attention is paid to the information spread by the social network user. The present study aims to assess the structural information in the network to identify influential users in addition to using their information in the social network. To this aim, the user’s feelings were extracted. Then, an emotional or affective score was assigned to each user based on an emotional dictionary and his/her weight in the network was determined utilizing centrality criteria. Here, the Twitter network was applied. Thus, the structure of the social network was defined and its graph was drawn after collecting and processing the data. Then, the analysis capability of the network and existing data was extracted and identified based on the algorithm proposed by users and influential nodes. Based on the results, the nodes identified by the proposed algorithm are considered high-quality and the speed of information simulated is higher than other existing algorithms.

Door access control systems are important to protect the security and integrity of physical spaces. Accuracy and speed are important factors that govern their performance. In this paper, we investigate a novel approach to identify users by measuring patterns of their interactions with a doorknob via an embedded accelerometer and gyroscope and by applying deep-learning-based algorithms to these measurements. Our identification results obtained from 47 users show an accuracy of 90.2%. When the sex of the user is used as an input feature, the accuracy is 89.8% in the case of male individuals and 97.0% in the case of female individuals. We study how the accuracy is affected by the sample duration, finding that is its possible to identify users using a sample of 0.5 s with an accuracy of 68.5%. Our results demonstrate the feasibility of using patterns of motor activity to provide access control, thus extending with it the set of alternatives to be considered for behavioral biometrics.

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.

The past years have witnessed the rapid conceptualization and development of wireless sensing based on Channel State Information (CSI) with commodity WiFi devices. Recent studies have demonstrated the vast potential ofWiFi sensing in detection, recognition, and estimation applications. However, the widespread deployment of WiFi sensing systems still faces a significant challenge: how to ensure the sensing performance when exposing a pre-trained sensing system to new domains, such as new environments, different configurations, and unseen users, without data collection and system retraining. This survey provides a comprehensive review of recent research efforts on cross-domain WiFi Sensing.We first introduce the mathematical model of CSI and explore the impact of different domains on CSI. Then we present a general workflow of cross-domain WiFi sensing systems, which consists of signal processing and cross-domain sensing. Five cross-domain sensing algorithms, including domain-invariant feature extraction, virtual sample generation, transfer learning, few-shot learning and big data solution, are summarized to show how they achieve high sensing accuracy when encountering new domains. The advantages and limitations of each algorithm are also summarized and the performance comparison is made based on different applications. Finally, we discuss the remaining challenges to further promote the practical usability of cross-domain WiFi sensing systems. [ABSTRACT FROM AUTHOR]

In an era marked by escalating concerns about digital security, biometric identification methods have gained paramount importance. Despite the increasing adoption of biometric techniques, keystroke dynamics analysis remains a less explored yet promising avenue. This study highlights the untapped potential of keystroke dynamics, emphasizing its non-intrusive nature and distinctiveness. While keystroke dynamics analysis has not achieved widespread usage, ongoing research indicates its viability as a reliable biometric identifier. This research builds upon the existing foundation by proposing an innovative deep-learning methodology for keystroke dynamics-based identification. Leveraging open research datasets, our approach surpasses previously reported results, showcasing the effectiveness of deep learning in extracting intricate patterns from typing behaviors. This article contributes to the advancement of biometric identification, shedding light on the untapped potential of keystroke dynamics and demonstrating the efficacy of deep learning in enhancing the precision and reliability of identification systems.

With the development of mobile networks, social networking plays an increasingly important role in people's daily life. User identification, which aims to match user cross-site accounts, has been becoming an important issue for user supervision and recommendation system design in social networks.At present, many different user identification methods have emerged, such as DPLink, HFUL, etc. However, compared with the continuous development of user identification methods, the open-source work of datasets is very slow, and the datasets of most of the work are not public. The shortage of datasets has greatly hindered the development of this research field. At present, the academic urgently needs a large-scale social network user linkage dataset.In this paper, we publicize a new social network user linkage dataset, XSiteTraj v1.0 [2]. This dataset has good spatio-temporal coverage, including more than 27,000 users and more than one million check-in records from all over the world crawled from Facebook, Foursquare, and Twitter. Our dataset labels the identical users from different social websites, and each check-in record includes a timestamp, point of interest (PoI), and latitude and longitude of PoI. Through our dataset, we can conduct research on user behaviour habits and apply the dataset to the experiments and evaluation of social network user identification and other algorithms.

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.

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. [ABSTRACT FROM AUTHOR]

With the flourishing of location-based social networks,users’mobile behavior data has been greatly enriched,which promotes the research on user identification based on spatio-temporal data.User identification in cross-location social networks emphasizes learning the correlation between time and space sequences of different platforms,aiming at discovering the accounts registered by the same user on different platforms.In order to solve the problems of data sparsity,low quality and spatio-temporal mismatch faced by existing researches,a recognition algorithm UI-STDD combining bidirectional spatio-temporal dependence and spatio-temporal distribution is proposed.The algorithm mainly consists of three modules:the space-time sequence module is combined with the bidirectional long short-term memory network of paired attention to describe user movement patterns;the time preference module defines the user personalized mode from coarse and fine granularity;the spatial location module mines local and global information of location points to quantify spatial proximity.Based on the user trajectory pair features obtained by the above modules,a multi-layer feedforward network is used in UI-STDD to distinguish whether two accounts across the network corres-pond to the same person in real life.To verify the feasibility and effectiveness of UI-STDD,experiments are carried out on three publicly available datasets.Experimental results show that the proposed algorithm can improve the user identification rate based on spatio-temporal data,and the average F1 value is more than 10% higher than the optimal comparison method.

Geolocation is a powerful source of information through which user patterns can be extracted. User regions-of-interest, along with these patterns, can be used to recognize and imitate user behavior. In this work we develop a methodology for preprocessing location data in order to discover the most relevant places the user visits, and we propose a Probabilistic Finite Automaton structure as mobility model. We analyse both location prediction and user identification tasks. Our model is assessed with two evaluation metrics regarding its predictive accuracy and user identification accuracy, and compared against other models. [ABSTRACT FROM AUTHOR]

This paper presents an enhanced approach to user identification using smartphone and wearable sensor data. Our methodology involves segmenting input data and independently analyzing subsequences with CNNs. During testing, we apply calibrated Monte-Carlo Dropout to measure prediction uncertainty. By leveraging the weights obtained from uncertainty quantification, we integrate the results through weighted averaging, thereby improving overall identification accuracy. The main motivation behind this paper is the need to calibrate the CNN for improved weighted averaging. It has been observed that incorrect predictions often receive high confidence, while correct predictions are assigned lower confidence. To tackle this issue, we have implemented the Ensemble of Near Isotonic Regression (ENIR) as an advanced calibration technique. This ensures that certainty scores more accurately reflect the true likelihood of correctness. Furthermore, our experiment shows that calibrating CNN reduces the need for Monte Carlo samples in uncertainty quantification, thereby reducing computational costs. Our thorough evaluation and comparison of different calibration methods have shown improved accuracy in user identification across multiple datasets. Our results showed notable performance improvements when compared to the latest models available. In particular, our approach achieved better results than DB2 by 1.12% and HAR by 0.3% in accuracy. • Utilizes CNNs to capture local temporal dependencies by segmenting input data. • Applies Monte-Carlo Dropout during testing to quantify prediction uncertainty. • Implements sophisticated calibration techniques to address miscalibration issues. • Introduces Ensemble of Near Isotonic Regression as the efficient calibration method. • Leverages probabilistic decision fusion for enhanced accuracy. • Integrates results through uncertainty-based weighted averaging. • Rigorously evaluates proposed methods across multiple datasets. • Demonstrates significant improvements in user identification performance. • Optimizes Monte-Carlo sampling to enhance feasibility for real-time applications. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) signals have gained widespread use in medical applications, and the utilization of EEG signals as a biometric feature for user identification systems in brain–computer interface systems has recently garnered significant attention. This paper presents a deep learning framework that uses a deep residual neural network (ResNet) for the identification of distinct individuals based on their EEG signals. The proposed framework utilizes continuous wavelet transform to convert one-dimensional EEG signals into two-dimensional spatial images. By incorporating both the frequency and time characteristics of the EEG signals, this model effectively considers and analyses the intricate details present in the data, leading to improved performance. The ResNet model considers the interdependencies between different time instances of the EEG signals. These unique features, collectively, provide sufficient discriminative information to accurately identify individuals. The proposed method achieved an exceptional classification accuracy of 99.73% and an equal error rate of 0.0041 for 64 channels within 109 individuals. The results illustrate the superiority of the proposed method over existing approaches. • Wavelet-ResNet integration: attains 99.73% EEG-based user identification accuracy. • Robust method utilizing large dataset, no complex feature design and preprocessing. • Incorporates temporal and frequency features via continuous wavelet transform. • Leverages spatial insight through various channel configurations. • Maintains high accuracy with fewer channels, reducing costs. [ABSTRACT FROM AUTHOR]

As sharing of accounts is quite common among family members or roommates, the design of efficient recommender schemes for shared accounts has raised much attention recently. Generally speaking, after each login, it is essential for a recommender system to identify the current user behind and leverage this information to make recommendations. One naive approach is first to identify the identity of the current user and then make recommendations. However, this two-stage based approach may not achieve satisfactory performance. The key is that the recommended items favoring identifying users in the first stage may not be interesting to the users, which can deplete the user's patience quickly and cause early termination of users. To address the problem, we propose a novel recommendation scheme that makes a tradeoff between recommending discriminating items (helpful for identifying the user) and recommending interesting ones to the user (helpful for increasing the number of clicks). Under this scheme, we develop a patience model to capture the user's dynamic patience level during the recommendation process. Moreover, considering the increasing popularity of mobile devices, we also incorporate mobile sensor data (i.e., angle, accelerometer, gyroscope, etc.) into our approach to further improve the performance of the system. We implemented the above system in an App on mobile devices and carried out extensive experiments. The results demonstrate that our proposed scheme significantly outperforms the existing state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

The drastic rise in the cybercrime rate associated with the surge of users' dependence on the Internet has elevated the concern of digital forensic examiners toward the footprints of perpetrators left in a virtual environment. However, suspect identification is a big challenge in network forensics due to the anonymous nature of data transmission across the network. This study utilises the decision tree classification approach to characterise users from their behavioural web navigation pattern using the meta-data of captured network packets (Destination IP, Protocol, Port Source, and Port Destination). A total of 95,795,379 network packet headers from 96 subjects were successfully collected. Their meta-data header packets were statistically profiled to generate digital fingerprints that try to link their action on the network to their identity accurately. Hence, CHAID decision tree modelling using Destination IP, Unique protocols, and a combination of the two, including Port source and Port destination, resulted in an accuracy of 4.07%, 6.34%, and 6.36%, respectively. However, the modelling could not create a reliable decision tree for the Port source and destination. The validation study on all the combined variables had a similar accuracy of 6.36%, indicating model created had reproducibility capability. Despite the outcome, the proposed method is not yet sufficiently strong for suspect identification. Further enhancement to improve its accuracy is required. [ABSTRACT FROM AUTHOR]

In order to achieve the promise of smart spaces where the environment acts to fulfill the needs of users in an unobtrusive and personalized manner, it is necessary to provide means for a seamless and continuous identification of users to know who indeed is interacting with the system and to whom the smart services are to be provided. In this paper, we propose a new approach capable of performing activity-free identification of users based on hand and arm motion patterns obtained from an wrist-worn inertial measurement unit (IMU). Our approach is not constrained to particular types of movements, gestures, or activities, thus, allowing users to perform freely and unconstrained their daily routine while the user identification takes place. We evaluate our approach based on IMU data collected from 23 people performing their daily routines unconstrained. Our results indicate that our approach is able to perform activity-free user identification with an accuracy of 0.9485 for 23 users without requiring any direct input or specific action from users. Furthermore, our evaluation provides evidence regarding the robustness of our approach in various different configurations. [ABSTRACT FROM AUTHOR]

Identifying accounts across different online social networks that belong to the same user has attracted extensive attentions. However, existing techniques rely on given user seeds and ignore the dynamic changes of online social networks, which fails to generate high quality identification results. In order to solve this problem, we propose an incremental user identification method based on user-guider similarity index (called CURIOUS), which efficiently identifies users and well captures the changes of user features over time. Specifically, we first construct a novel user-guider similarity index (called USI) to speed up the matching between users. Second we propose a two-phase user identification strategy consisting of USI-based bidirectional user matching and seed-based user matching, which is effective even for incomplete networks. Finally, we propose incremental maintenance for both USI and the identification results, which dynamically captures the instant states of social networks. We conduct experimental studies based on three real-world social networks. The experiments demonstrate the effectiveness and the efficiency of our proposed method in comparison with traditional methods. Compared with the traditional methods, our method improves precision, recall and rank score by an average of 0.19, 0.16 and 0.09 respectively, and reduces the time cost by an average of 81%. [ABSTRACT FROM AUTHOR]