A survey on cybersecurity, data privacy, and policy issues in cyber-physical system deployments in smart cities

A comprehensive overview of biometric fusion

This text is a survey on cross-validation We define all classical cross-validation procedures, and we study their properties for two different goals: estimating the risk of a given estimator, and selecting the best estimator among a given family For the risk estimation problem, we compute the bias (which can also be corrected) and the variance of cross-validation methods For estimator selection, we first provide a first-order analysis (based on expectations) Then, we explain how to take into account second-order terms (from variance computations, and by taking into account the usefulness of overpenalization) This allows, in the end, to provide some guidelines for choosing the best cross-validation method for a given learning problem

Cross-validation

In online discussion communities, users can interact and share information and opinions on a wide variety of topics. However, some users may create multiple identities, or sockpuppets, and engage in undesired behavior by deceiving others or manipulating discussions. In this work, we study sockpuppetry across nine discussion communities, and show that sockpuppets differ from ordinary users in terms of their posting behavior, linguistic traits, as well as social network structure. Sockpuppets tend to start fewer discussions, write shorter posts, use more personal pronouns such as ``I'', and have more clustered ego-networks. Further, pairs of sockpuppets controlled by the same individual are more likely to interact on the same discussion at the same time than pairs of ordinary users. Our analysis suggests a taxonomy of deceptive behavior in discussion communities. Pairs of sockpuppets can vary in their deceptiveness, i.e., whether they pretend to be different users, or their supportiveness, i.e., if they support arguments of other sockpuppets controlled by the same user. We apply these findings to a series of prediction tasks, notably, to identify whether a pair of accounts belongs to the same underlying user or not. Altogether, this work presents a data-driven view of deception in online discussion communities and paves the way towards the automatic detection of sockpuppets.

An Army of Me: Sockpuppets in Online Discussion Communities

PEA: Parallel electrocardiogram-based authentication for smart healthcare systems

A person can be identified from his physiological traits as well as from behavioral patterns. However, a person's behavior is not only confined to individual actions such as walking or typing style, speech or handwriting but also social interactions and communication. In other words, social communication is an indispensable part of our daily behavior. Therefore, a person's social connections, spatio-temporal information, style of interactions etc. Can be a good source of information to identify his social behavioral pattern. Based on this hypothesis, this paper introduces a novel kind of behavioral biometrics called Social Behavioral Biometrics (SBB) for the first time. The study includes identification of social behavioral biometric features from real and virtual domain and their prospective applications for the purpose of person authentication and verification.

A Concept of Social Behavioral Biometrics: Motivation, Current Developments, and Future Trends

In todays world, identity of human beings has expanded beyond the real world to the cyber world. Virtual identity of millions of users is present at various web-based Social Networking Sites (SNSs) such as Myspace, Facebook, and Twitter. Interactions through SNSs have become a part of our daily practices, which eventually leaves a big trail of behavioral pattern in virtual domain. In this paper, the authors examined the feasibility of person identification using such social network activities as behavioral biometrics. Experimentation includes extraction of a number of idiosyncratic features from SNSs and analysis of their performance as novel social behavioral biometric features.

Social Behavioral Biometrics: An Emerging Trend

Social interactions are integral part of human behavior. Although social interactions are likely to possess unique behavioral patterns, their significance for automated user recognition has been noted in the scientific community only recently. This paper demonstrated that it is possible to generate a set of unique features, called social behavioral (SB) features, from the social interactions of individuals’ via an online social network (OSN). Specifically, this research identified a set of SB features from the online social interactions of 241 Twitter users and proposed a framework to utilize these features for an automated user recognition. Extensive experimentation demonstrated high recognition performance as well as distinctiveness of the proposed SB features. The most striking finding was that only ten recent tweets are enough to recognize 58% of users in our database at rank-1. The rank-1 recognition rate dramatically increased to 93% when 60 tweets were used as a probe set. Experimental results also demonstrated the stability of the proposed SB feature set over time and ability to recognize both frequent and nonfrequent OSN users. This confirms that human social behavior expressed through an OSN can provide a unique insight into user behavior recognition.

User Recognition From Social Behavior in Computer-Mediated Social Context

The goal of a biometric recognition system is to make a human-like decisions on individual’s identity by recognizing their physiological and/or behavioral traits. Nevertheless, the decision-making process by either a human or a biometric recognition system can be highly complicated due to low quality of data or an uncertain environment. Human brain has an advantage over computer system due to its ability to perform a massive parallel processing of auxiliary information, such as visual cues, cognitive and social interactions, contextual, and spatio-temporal data. Similarly to a human brain, social behavioral cues can aid the reliable decision-making of an automated biometric system. In this paper, a novel person recognition approach is presented, that relies on the knowledge of individuals’ social behavior to enhance the performance of a traditional biometric system. The social behavioral information of individuals’ has been mined from an online social network and fused with traditional face and ear biometrics. Experimental results on individual’s and semi-real databases demonstrate significant performance gain in the proposed method over traditional biometric system.

Social Behavioral Information Fusion in Multimodal Biometrics

This chapter investigates how existing biometric research can be advanced by integrating it with the social behavioural information. Analytical discussions on how social behavioural biometrics can be extracted and applied in various security, and authentication applications will be presented. This chapter also provides some insights onto current and emerging research in the multimodal biometric domain, formulates open questions and investigates future directions. Answers to those questions will assist not only in establishment of the new methods in the biometric security domain but also provide insights into the future emerging topics in the big data analytics and social networking research.

Madeena Sultana

Papers

A Concept of Social Behavioral Biometrics: Motivation, Current Developments, and Future Trends

Social Behavioral Biometrics: An Emerging Trend

User Recognition From Social Behavior in Computer-Mediated Social Context

Social Behavioral Information Fusion in Multimodal Biometrics

Emerging Trends in Security System Design Using the Concept of Social Behavioural Biometrics