Anastasios Drosou

Using activity-related behavioural features towards more effective automatic stress detection

This paper introduces activity-related behavioural features that can be automatically extracted from a computer system, with the aim to increase the effectiveness of automatic stress detection. The proposed features are based on processing of appropriate video and accelerometer recordings taken from the monitored subjects. For the purposes of the present study, an experiment was conducted that utilized a stress-induction protocol based on the stroop colour word test. Video, accelerometer and biosignal (Electrocardiogram and Galvanic Skin Response) recordings were collected from nineteen participants. Then, an explorative study was conducted by following a methodology mainly based on spatiotemporal descriptors (Motion History Images) that are extracted from video sequences. A large set of activity-related behavioural features, potentially useful for automatic stress detection, were proposed and examined. Experimental evaluation showed that several of these behavioural features significantly correlate to self-reported stress. Moreover, it was found that the use of the proposed features can significantly enhance the performance of typical automatic stress detection systems, commonly based on biosignal processing.

Spatiotemporal analysis of human activities for biometric authentication

This paper presents a novel framework for unobtrusive biometric authentication based on the spatiotemporal analysis of human activities. Initially, the subjects actions that are recorded by a stereoscopic camera, are detected utilizing motion history images. Then, two novel unobtrusive biometric traits are proposed, namely the static anthropometric profile that accurately encodes the inter-subject variability with respect to human body dimensions, while the activity related trait that is based on dynamic motion trajectories encodes the behavioral inter-subject variability for performing a specific action. Subsequently, score level fusion is performed via support vector machines. Finally, an ergonomics-based quality indicator is introduced for the evaluation of the authentication potential for a specific trial. Experimental validation on data from two different datasets, illustrates the significant biometric authentication potential of the proposed framework in realistic scenarios, whereby the user is unobtrusively observed, while the use of the static anthropometric profile is seen to significantly improve performance with respect to state-of-the-art approaches.

Multi-Objective Optimization for Multimodal Visualization

Using data visualization techniques can be of significant assistance in exploring multimedia databases. Data visualization is typically addressed as a unimodal learning task, where data are described with only one feature set, or modality. However, using multiple data modalities has been proved to increase the performance of learning methods. In this paper a novel approach for exploiting the multiple available modalities for visualization is proposed, motivated by the field of multi-objective optimization. Initially, each modality is considered separately. A graph of the dissimilarities among the data and the corresponding minimum spanning tree are formed. The suitability of a particular data placement is quantified using multiple cost functions, one for each modality. The utilized cost functions are defined in terms of graph aesthetic measures, computed for the unimodal minimum spanning trees. The cost functions are then used as the multiple objectives of a multi-objective optimization problem. Solving the problem results in a set of Pareto optimal placements, which represent different trade-offs among the various objectives. Experimental evaluation shows that the proposed method outperforms current multimodal visualization methods both in discovering more visualizations and in producing ones which are more aesthetically pleasing and easily perceivable.

A BRPCA Based Approach for Anomaly Detection in Mobile Networks

Researchers have recently uncovered numerous exploitable vulnerabilities that enable malicious individuals to mount attacks against mobile network users and services. The detection and attribution of these threats are of major importance to the mobile operators. Therefore, this paper presents a novel approach for anomaly detection in 3G/4G mobile networks based on Bayesian Robust Principal Component Analysis (BRPCA), which enables cognition in mobile networks through the ability to perceive threats and to act in order to mitigate their effects. BRPCA is used to model aggregate network data and subsequently identify abnormal network states. A major difference with previous work is that this method takes into account the spatio-temporal nature of the mobile network traffic, to reveal encoded periodic characteristics, which has the potential to reduce false positive rate. Furthermore, the BRPCA method is unsupervised and does not raise privacy issues due to the nature of the raw data. The effectiveness of the approach was evaluated against three other methods on two synthetic datasets for a large mobile network, and the results show that BRPCA provides both higher detection rate and lower computational overhead.

A Novel Graph-Based Descriptor for the Detection of Billing-Related Anomalies in Cellular Mobile Networks

Mobile devices are evolving and becoming increasingly popular over the last few years. This growth, however, has exposed mobile devices to a large number of security threats. Malware installed in smartphones can be used for a variety of malicious purposes, including stealing personal data, sending spam SMSs, and launching Denial of Service (DoS) attacks against core network components. Authentication and access-control-based techniques, employed by network operators fail to provide integral protection against malware threats. In order to solve this issue, the activity of each mobile device in the network must be taken into account, and combined with the activities of all the other devices. The communication activity in the mobile network has a source, a destination, and possibly communication weights (e.g., the number of calls between two mobile devices). This relational nature of the communication activity is naturally represented with graphs. This indicates that graphs can be utilized in order to provide better representations of the entire network activity, and lead to better detection results when compared to methods that consider the activity of each mobile device individually. Towards this end, this paper proposes a novel graph-based descriptor for the detection of anomalies in mobile networks, using billing-related information. The graph-based descriptor represents the total activity in the network. Smaller graphs are afterwards extracted from the graph-based descriptor, each one representing the activity of one mobile device (e.g., Calls or SMSs), while multiple features are calculated for each such graph. These features are subsequently used for the supervised classification on network events, and the identification of anomalous mobile devices. Experimental results and comparison of the proposed anomaly detection method to the existing work, show that the graph-based descriptor has superior performance in a variety of scenarios.

Unobtrusive Behavioral and Activity-Related Multimodal Biometrics: The ACTIBIO Authentication Concept

Unobtrusive Authentication Using ACTIvity-Related and Soft BIOmetrics (ACTIBIO) is an EU Specific Targeted Research Project (STREP) where new types of biometrics are combined with state-of-the-art unobtrusive technologies in order to enhance security in a wide spectrum of applications. The project aims to develop a modular, robust, multimodal biometrics security authentication and monitoring system, which uses a biodynamic physiological profile, unique for each individual, and advancements of the state of the art in unobtrusive behavioral and other biometrics, such as face, gait recognition, and seat-based anthropometrics. Several shortcomings of existing biometric recognition systems are addressed within this project, which have helped in improving existing sensors, in developing new algorithms, and in designing applications, towards creating new, unobtrusive, biometric authentication procedures in security-sensitive, Ambient Intelligence environments. This paper presents the concept of the ACTIBIO project and describes its unobtrusive authentication demonstrator in a real scenario by focusing on the vision-based biometric recognition modalities.

Event-based unobtrusive authentication using multi-view image sequences

his paper presents a novel framework for dynamic activity-related user authentication utilizing dynamic and static anthropometric information. The recognition of the performed activity is based on Radon transforms that are applied on spatiotemporal motion templates. User authentication is performed exploiting the behavioural variations between different users. The upper body limb anthropometric information is extracted for each user and an attributed body-related graph structure framework is employed for the detection of static biometric features of substantial discrimination power. Finally, a quality factor based on ergonomic criteria evaluates the recognition capacity of each activity. Experimental validation illustrates that the proposed approach for integrating static anthropometric features and activity-related recognition advances significantly the authentication performance.

An enhanced Graph Analytics Platform (GAP) providing insight in Big Network Data

Abstract Being a widely adapted and acknowledged practice for the representation of inter- and intra-dependent information streams, network graphs are nowadays growing vast in both size and complexity, due to the rapid expansion of sources, types, and amounts of produced data. In this context, the efficient processing of the big amounts of information, also known as Big Data forms a major challenge for both the research community and a wide variety of industrial sectors, involving security, health and financial applications. Serving these emerging needs, the current paper presents a Graph Analytics based Platform (GAP) that implements a top-down approach for the facilitation of Data Mining processes through the incorporation of state-of-the-art techniques, like behavioural clustering, interactive visualizations, multi-objective optimization, etc. The applicability of this platform is validated on 2 istinct real-world use cases, which can be considered as characteristic examples of modern Big Data problems, due to the vast amount of information they deal with. In particular, (i) the root cause analysis of a Denial of Service attack in the network of a mobile operator and (ii) the early detection of an emerging event or a hot topic in social media communities. In order to address the large volume of the data, the proposed application starts with an aggregated overview of the whole network and allows the operator to gradually focus on smaller sets of data, using different levels of abstraction. The proposed platform offers differentiation between different user behaviors that enable the analyst to obtain insight on the network’s operation and to extract the meaningful information in an effortless manner. Dynamic hypothesis formulation techniques exploited by graph traversing and pattern mining, enable the analyst to set concrete network-related hypotheses, and validate or reject them accordingly.

MoVA: A Visual Analytics Tool Providing Insight in the Big Mobile Network Data

Mobile networks have numerous exploitable vulnerabilities that enable malicious individuals to launch Denial of Service (DoS) attacks and affect network security and performance. The efficient detection and attribution of these anomalies are of major importance to the mobile network operators, especially since there is a vast amount of information collected, which renders the problem as a Big Data problem. Previous approaches focus on either anomaly detection methods, or visualization methods separately. In addition, they utilize solely either the signaling or the Call Detail Record (CDR) activity in the network. This paper presents MoVA (Mobile network Visual Analytics), a visual analytics tool for the detection and attribution of anomalies in mobile cellular networks which combines anomaly detection and visualization, and is applied on both signaling and CDR activity in the network. In order to address the large volume of the data, the proposed application starts with an aggregated overview of the whole network and allows the operator to gradually focus on smaller sets of data, using different levels of abstraction. The proposed visualization methods are able to differentiate between different user behaviors, and enable the analyst to have an insight in the mobile network operation and easily spot the anomalous mobile devices. Hypothesis formulation and validation methods are also provided, in order to enable the analyst to formulate network security-related hypotheses, and validate or reject them based on the results of the analysis.

A multi-objective clustering approach for the detection of abnormal behaviors in mobile networks

The visualization of mobile network data can be of significant value to the network security administrator in order to detect anomalies in the normal traffic, caused by malicious attacks. Although several visualization types of the network structure and traffic already exist, the literature around visualizing behavioral aspects of users or network components, in order to distinguish the normal from the abnormal ones, is limited. In this paper, a behavior-based approach for visualizing the users of the network, with respect to specific aspects of their behavior, is proposed. The approach introduces the extraction of behavior-related descriptors from the raw network traffic data, which can be used to visualize behavioral similarities, so that users with similar behavior are depicted as points close to each other. Multiple descriptors are extracted from each user and are used as the multiple modalities in a state-of-the-art multi-objective visualization method. The outcome of the multi-objective method is a visualization of the behavioral similarities of users, according to the selection of a trade-off among the multiple descriptors. This allows the analyst to visually detect anomalies and analyze their evolution in time. Experimental evaluation of the proposed approach with several datasets in various application scenarios verify its efficiency.