Conference Paper

Can We Trust This User? Predicting Insider's Attitude via YouTube Usage Profiling

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Abstract

Addressing the insider threat is a major issue in cyber and corporate security in order to enhance trusted computing in critical infrastructures. In this paper we study the psychosocial perspective and the implications of insider threat prediction via social media, Open Source Intelligence and user generated content classification. Inductively, we propose a prediction method by evaluating the predisposition towards law enforcement and authorities, a personal psychosocial trait closely connected to the manifestation of malevolent insiders. We propose a methodology to detect users holding negative attitude towards authorities. For doing so, we facilitate a brief analysis of the medium (YouTube), machine learning techniques and a dictionary-based approach, in order to detect comments expressing negative attitude. Thus, we can draw conclusions over a user behavior and beliefs via the content the user generated within the limits a social medium. We also use an assumption free flat data representation technique in order to decide over the user's attitude and improve the scalability of our method. Furthermore, we compare the results of each method and highlight the common behavior and characteristics manifested by the users. As privacy violations may well-rise when using such methods, their use should be restricted only on exceptional cases, e.g. when appointing security officers or decision-making staff in critical infrastructures.

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... Along with technical countermeasures, research has proved that it is possible to detect personality characteristics shared among insiders themselves through social media [10] [11]. Social media users tend to transfer their offline behavior to the online world [12]. ...
... Modern approaches indicate that such characteristics can be extracted through social media. To this extend, conclusions over traits, such narcissism [10] or predisposition towards law enforcement [11], have been successfully extracted via Twitter and YouTube respectively, leading towards the ability of online monitoring of users behavior so as to detect potentially malevolent users. ...
... Insider threat mitigation forms a vital factor for an organization. Traits such as narcissism [10], predisposition towards law enforcement [11] and divided loyalty [25] can be extracted from social media profiles and detect potential insider threats, as a success or horror story respectively. The above mentioned traits have been examined and detected through social media and can facilitate the insider threat prediction in the digital world. ...
Conference Paper
Modern business environments have a constant need to increase their productivity, reduce costs and offer competitive products and services. This can be achieved via modeling their business processes. Yet, even in light of modelling's widespread success, one can argue that it lacks built-in security mechanisms able to detect and fight threats that may manifest throughout the process. Academic research has proposed a variety of different solutions which focus on different kinds of threat. In this paper we focus on insider threat, i.e. insiders participating in an organization's business process, who, depending on their motives, may cause severe harm to the organization. We examine existing security approaches to tackle down the aforementioned threat in enterprise business processes. We discuss their pros and cons and propose a monitoring approach that aims at mitigating the insider threat. This approach enhances business process monitoring tools with information evaluated from Social Media. It exams the online behavior of users and pinpoints potential insiders with critical roles in the organization's processes. We conclude with some observations on the monitoring results (i.e. psychometric evaluations from the social media analysis) concerning privacy violations and argue that deployment of such systems should be only allowed on exceptional cases, such as protecting critical infrastructures.
... We screened 177 abstracts, evaluated 50 full-text articles, and included 37 articles-a total of 22 studies (59 %) propose novel IDPAs [9,. The other 15 papers either propose new features for IDP or discusses challenges associated with IDP [33][34][35][36][37][38][39][40][41][42][43][44][45][46][47]. Figure 1 presents the flow chart of the study selection process. In 13 papers (out of these 22 papers presenting novel algorithms), the authors have implemented and evaluated the proposed algorithms. ...
... For Fig. 2 The trend of security research for the insider cyber threat. The x-axis represents 'Year' and the y-axis represents the number of publications published in any given year example, Kandias et al. [40] have conducted a content analysis of user comments on YouTube videos looking for any negative comments on law enforcement. Theoretically, these negative comments posted by employees are likely to reflect their intent to commit malicious acts. ...
... The motive refers to the reason or cause why an insider or group of insiders will perpetrate a crime. Previous studies have grouped the features associated with motives into four broad categories [14,17,25,34,[38][39][40]: ...
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Cyber security is vital to the success of today’s digital economy. The major security threats are coming from within, as opposed to outside forces. Insider threat detection and prediction are important mitigation techniques. This study addresses the following research questions: 1) what are the research trends in insider threat detection and prediction nowadays? 2) What are the challenges associated with insider threat detection and prediction? 3) What are the best-to-date insider threat detection and prediction algorithms? We conduct a systematic review of 37 articles published in peer-reviewed journals, conference proceedings and edited books for the period of 1950–2015 to address the first two questions. Our survey suggests that game theoretic approach (GTA) is a popular source of insider threat data; the insiders’ online activities are the most widely used features in insider threat detection and prediction; most of the papers use single point estimates of threat likelihood; and graph algorithms are the most widely used tools for detecting and predicting insider threats. The key challenges facing the insider threat detection and prediction system include unbounded patterns, uneven time lags between activities, data nonstationarity, individuality, collusion attacks, high false alarm rates, class imbalance problem, undetected insider attacks, uncertainty, and the large number of free parameters in the model. To identify the best-to-date insider threat detection and prediction algorithms, our meta-analysis study excludes theoretical papers proposing conceptual algorithms from the 37 selected papers resulting in the selection of 13 papers. We rank the insider threat detection and prediction algorithms presented in the 13 selected papers based on the theoretical merits and the transparency of information. To determine the significance of rank sums, we perform “the Friedman two-way analysis of variance by ranks” test and “multiple comparisons between groups or conditions” tests.
... Hence, it differs from other kinds of cyber-security issues such as malware or intrusion detection due to the unpredictable nature of human beings. Previous works in this domain proposed several solutions mostly focused on detection of masquerade behaviors using machine learning (ML) approaches [5], [18], [15], [13], [17], [25], [26], [30]. Among those, we can identify the two-classes/multiclasses techniques [5], [18], [15], [13], which require labeled malicious samples for the training phase, and therefore consider a certain knowledge of the attacking scenarios for their correct recognition. ...
... Previous works in this domain proposed several solutions mostly focused on detection of masquerade behaviors using machine learning (ML) approaches [5], [18], [15], [13], [17], [25], [26], [30]. Among those, we can identify the two-classes/multiclasses techniques [5], [18], [15], [13], which require labeled malicious samples for the training phase, and therefore consider a certain knowledge of the attacking scenarios for their correct recognition. In contrast to two-classes/multi-classes techniques, there exist one-class approaches [17], [25], [26], [30], which do not require any malicious samples for training, and therefore are advantageous for anomaly detection of a wider range of masquerader behaviors [13], [25]. ...
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Masqueraders are users who take control of a machine and perform malicious activities such as data exfiltration or system misuse on behalf of legitimate users. In the literature, there are various approaches for detecting masqueraders by modeling legitimate users' behavior during their daily tasks and automatically determine whether they are doing something suspicious. Usually, these techniques model user behavior using features extracted from various sources, such as file system, network activities, system calls, etc. In this work, we propose a one-class anomaly detection approach that measures similarities between a history of a user and events recorded in a time window of the user's session which is to be classified. The idea behind our solution is the application of a graph partitioning technique on weighted oriented graphs generated from such event sequences, while considering that strongly connected nodes have to belong into the same cluster. First, a history of vertex clusters is build per each user and then this history is compared to a new input by using a similarity function, which leads either to the acceptance or rejection of a new input. This makes our approach substantially different from existing general graph-based approaches that consider graphs as a single entity. The approach can be applied for different kinds of homogeneous event sequences; however, successful application of the approach will be demonstrated on file system access events only. The linear time complexity of the approach was demonstrated in the experiments and the performance evaluation was done using two state-of-the-art datasets - WUIL and TWOS - both of them containing file system access logs of legitimate users and masquerade attackers; for WUIL dataset we achieved an average per-user AUC of 0.94, a TPR over 95%, and a FPR less than 10%, while for TWOS dataset we achieved an average per-user AUC of 0.851, a TPR over 91% and a FPR around 11%.
... Hence, it differs from other kinds of cyber-security issues such as malware or intrusion detection due to the unpredictable nature of human beings. Previous works in this domain proposed several solutions mostly focused on detection of masquerade behaviors using machine learning (ML) approaches [5], [18], [15], [13], [17], [25], [26], [30]. Among those, we can identify the two-classes/multiclasses techniques [5], [18], [15], [13], which require labeled malicious samples for the training phase, and therefore consider a certain knowledge of the attacking scenarios for their correct recognition. ...
... Previous works in this domain proposed several solutions mostly focused on detection of masquerade behaviors using machine learning (ML) approaches [5], [18], [15], [13], [17], [25], [26], [30]. Among those, we can identify the two-classes/multiclasses techniques [5], [18], [15], [13], which require labeled malicious samples for the training phase, and therefore consider a certain knowledge of the attacking scenarios for their correct recognition. In contrast to two-classes/multi-classes techniques, there exist one-class approaches [17], [25], [26], [30], which do not require any malicious samples for training, and therefore are advantageous for anomaly detection of a wider range of masquerader behaviors [13], [25]. ...
Preprint
Full-text available
Masqueraders are users who take control of a machine and perform malicious activities such as data exfiltration or system misuse on behalf of legitimate users. In the literature, there are various approaches for detecting masqueraders by modeling legitimate users' behavior during their daily tasks and automatically determine whether they are doing something suspicious. Usually, these techniques model user behavior using features extracted from various sources, such as file system, network activities, system calls, etc. In this work, we propose a one-class anomaly detection approach that measures similarities between a history of a user and events recorded in a time window of the user's session which is to be classified. The idea behind our solution is the application of a graph partitioning technique on weighted oriented graphs generated from such event sequences, while considering that strongly connected nodes have to belong into the same cluster. First, a history of vertex clusters is build per each user and then this history is compared to a new input by using a similarity function, which leads either to the acceptance or rejection of a new input. This makes our approach substantially different from existing general graph-based approaches that consider graphs as a single entity. The approach can be applied for different kinds of homogeneous event sequences; however, successful application of the approach will be demonstrated on file system access events only. The linear time complexity of the approach was demonstrated in the experiments and the performance evaluation was done using two state-of-the-art datasets - WUIL and TWOS - both of them containing file system access logs of legitimate users and masquerade attackers; for WUIL dataset we achieved an average per-user AUC of 0.94, a TPR over 95%, and a FPR less than 10%, while for TWOS dataset we achieved an average per-user AUC of 0.851, a TPR over 91% and a FPR around 11%.
... Kandias et. al. proposed a methodology to detect users holding a negative attitude towards authorities using social activities analysis by machine learning approaches for social network applications [12]. Agent-based modeling is an approach to detect/predict user behaviour and decisions in different scenarios as analyzed by [23]. ...
... In Social Perspective, authors proposed a framework for insider threat prediction and detection using individuals social activities. Mitigation of insider threat and provision of a threat control method is covered in [12] but insider attack avoidance approaches are missing. Geo-Social access control framework proposed to predict and prevent insider threat by using social analysis of employees [2]. ...
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Cloud computing is now among the most extensively used mean for resource sharing as SaaS, PaaS, and IaaS. Computing Scenarios have been emerged into cloud computing instead of distributed computing. It has provided an efficient and flexible way for dynamic services meeting needs and challenges of the time in cost effective manners. Virtual environments provided the opportunity to migrate traditional systems to the cloud. Cloud service providers and Administrators generally have full access on Virtual Machines (VMs) whereas tenants have limited access on respective VMs. Cloud Admins as well as remote administrators also have full access rights on respective resources and may pose severe insiders threats on which tenants haven shown their concerns. Securing these resources are the key issues. In this paper, available practices for cloud security are investigated and a self-managed framework is introduced to mitigate malicious insider threats posed to these virtual environments.
... Natural Language Processing (NLP) which uses data analysis to identify linguistic elements such as for example how grammar indicators can be used to assess phishing emails -use of imperatives, multiple verbs, intensifiers, time-related words, incorrect grammar, typos, lingo etc. [5]. Other social indicators can also be measured by NLP to identifying the sender of a stereotypical phish through, for example, foreign language identification, negative tone, demands and incorrect grammar [6]. URL mismatch is a key indicator that there is a likelihood of criminal intention in phishing email, which is often considered in terms of the destination website which may appear to be a well-known brand destination but links to a different site. ...
... Identifying phishing by picking up on emotion words e.g. urgency, intensity;also used NLP [6] Provides a fine-tuning of NLP by picking out tone e.g. ...
Chapter
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Online advertisements delivered via social media platforms function in a similar way to phishing emails. In recent years there has been a growing awareness that political advertisements are being microtargeted and tailored to specific demographics, which is analogous to many social engineering attacks. This has led to calls for total bans on this kind of focused political advertising. Additionally, there is evidence that phishing may be entering a more developed phase using software known as Phishing as a Service to collect information on phishing or social engineering, potentially facilitating microphishing campaigns. To help understand such campaigns, a set of well-defined metrics can be borrowed from the field of digital marketing, providing novel insights which inform phishing email analysis. Our work examines in what ways digital marketing is analogous to phishing and how digital marketing metric techniques can be used to complement existing phishing email analysis. We analyse phishing email datasets collected by the University of Houston in comparison with Corporate junk email and microtargeting Facebook Ad Library datasets, thus comparing these approaches and their results using Weka, URL mismatch and visual metrics analysis. Our evaluation of the results demonstrates that phishing emails can be joined up in unexpected ways which are not revealed using traditional phishing filters. However such microphishing may have the potential to gather, store and analyse social engineering information to be used against a target at a later date in a similar way to microtargeting.
... As far as data privacy is concerned, it is demonstrated that Cambridge Analytica is still alive and we can export people's behavioural characteristics without their consent just by acquiring publicly available data (Pitropakis et al., 2020;Kandias et al., 2013;Isaak & Hanna, 2018). This information, being public and anonymized, is exempt from the request for approval by an ethics committee (Eysenbach & Till, 2001). ...
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With the growth that social networks have experienced in recent years, it is entirely impossible to moderate content manually. Thanks to the different existing techniques in natural language processing, it is possible to generate predictive models that automatically classify texts into different categories. However, a weakness has been detected concerning the language used to train such models. This work aimed to develop a predictive model based on BERT, capable of detecting racist and xenophobic messages in tweets written in Spanish. A comparison was made with different Deep Learning models. A total of five predictive models were developed, two based on BERT and three using other deep learning techniques, CNN, LSTM and a model combining CNN + LSTM techniques. After exhaustively analyzing the results obtained by the different models, it was found that the one that got the best metrics was BETO, a BERT-based model trained only with texts written in Spanish. The results of our study show that the BETO model achieves a precision of 85.22% compared to the 82.00% precision of the mBERT model. The rest of the models obtained between 79.34% and 80.48% precision. On this basis, it has been possible to justify the vital importance of developing native transfer learning models for solving Natural Language Processing (NLP) problems in Spanish. Our main contribution is the achievement of promising results in the field of racism and hate speech in Spanish by applying different deep learning techniques.
... For the purpose of assessing the trustworthiness of entities such as actors or documents, Mayhew et al. [2015] proposed behavior-based access control (BBAC), which is based on a sequential combination of k-means clustering and SVM. Dealing with NLP in comments of YouTube users, Kandias et al. [2013] employed SVM, logistic regression, and Naïve Bayes classifiers in order to predict users with negative/radical political attitudes, assuming these attitudes to be precursors of insider threat. ...
Preprint
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Insider threats are one of today's most challenging cybersecurity issues that are not well addressed by commonly employed security solutions. Despite several scientific works published in this domain, we argue that the field can benefit from the proposed structural taxonomy and novel categorization of research that contribute to the organization and disambiguation of insider threat incidents and the defense solutions used against them. The objective of our categorization is to systematize knowledge in insider threat research, while leveraging existing grounded theory method for rigorous literature review. The proposed categorization depicts the workflow among particular categories that include: 1) Incidents and datasets, 2) Analysis of attackers, 3) Simulations, and 4) Defense solutions. Special attention is paid to the definitions and taxonomies of the insider threat; we present a structural taxonomy of insider threat incidents, which is based on existing taxonomies and the 5W1H questions of the information gathering problem. Our survey will enhance researchers' efforts in the domain of insider threat, because it provides: a) a novel structural taxonomy that contributes to orthogonal classification of incidents and defining the scope of defense solutions employed against them, b) an updated overview on publicly available datasets that can be used to test new detection solutions against other works, c) references of existing case studies and frameworks modeling insiders' behaviors for the purpose of reviewing defense solutions or extending their coverage, and d) a discussion of existing trends and further research directions that can be used for reasoning in the insider threat domain.
... Likewise, social media open APIs were also used in the past way before the Cambridge Analytica scandal emerged for insider threat prediction or detection [29]. Kandias et al. [30][31][32] gathered publicly available data from Twitter and created a taxonomy to classify users based on their usage intensity, Klout score and influence. They were also able to gather YouTube comments and classify them using Machine Learning techniques, determining political affiliation and predisposition to law enforcement. ...
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The proliferation of social media platforms changed the way people interact online. However, engagement with social media comes with a price, the users’ privacy. Breaches of users’ privacy, such as the Cambridge Analytica scandal, can reveal how the users’ data can be weaponized in political campaigns, which many times trigger hate speech and anti-immigration views. Hate speech detection is a challenging task due to the different sources of hate that can have an impact on the language used, as well as the lack of relevant annotated data. To tackle this, we collected and manually annotated an immigration-related dataset of publicly available Tweets in UK, US, and Canadian English. In an empirical study, we explored anti-immigration speech detection utilizing various language features (word n-grams, character n-grams) and measured their impact on a number of trained classifiers. Our work demonstrates that using word n-grams results in higher precision, recall, and f-score as compared to character n-grams. Finally, we discuss the implications of these results for future work on hate-speech detection and social media data analysis in general.
...  Real-world system log data [21]  Real data injected with synthetic anomalies [22]  Game-theoretic approach (GTA) [23]  Social media data Simulated data drawn from stochastic models [24]  Simulated data drawn from stochastic models which are developed from real data In academic literature, mostly behavior -based modeling has been presented to detect insider threat. This model can be grouped to system behaviors and user behaviors. ...
... -YouTube is large source of public videos where opinions can be expressed freely; -It is also a source of different type of religions for spreading their views and thoughts using public videos. It is not only an entertainment source, but it is a source of learning, thoughts, and changing behavior of any person thoughts; -YouTube videos and comments contain some characteristics and the appropriate phraseology of interest [19] [12]; -YouTube often contain contents which are used to change human mind towards religions, because people are free to express their negative attitude towards any religions; -Generally, users join YouTube to participate. ...
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On YouTube, billions of videos are watched online and millions of short messages are posted each day. YouTube along with other social networking sites are used by individuals and extremist groups for spreading hatred among users. In this paper, we consider religion as the most targeted domain for spreading hate speech among people of different religions. We present a methodology for the detection of religion-based hate videos on YouTube. Messages posted on YouTube videos generally express the opinions of users’ related to that video. We provide a novel dataset for religious hate speech detection on Youtube comments. The proposed methodology applies data mining techniques on extracted comments from religious videos in order to filter religion-oriented messages and detect those videos which are used for spreading hate. The supervised learning algorithms: Support Vector Machine (SVM), Logistic Regression (LR), and k-Nearest Neighbor (k-NN) are used for baseline results.
... It presents a theoretical work that consists in defining formally an opinion-oriented model. We have experimented by using it in order to rank forum messages from the most to the least interesting [2,3]. ...
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... Kandias et al. proposed using negative attitudes towards authority as the main psychosocial feature for detecting insider threats, because these attitudes were found to be a main trait of malicious insiders [84]. To extract user attitudes, the authors proposed gathering records of employees' social media activities for analysis [78,79,80]. The authors performed experiments on datasets obtained from crawled YouTube comments. ...
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The ability to detect insider threats is important for many organisations. However, the field of insider threat detection is not well understood. In this paper, we survey existing insider threat detection mechanisms to provide a better understanding of the field.We identify and categorise insider behaviours into four classes - biometric behaviours, cyber behaviours, communication behaviours, and psychosocial behaviours. Each class is further comprised of several independent research fields of anomaly detection. Our survey reveals that there is significant scope for further research in many of those research fields, with many machine learning algorithms and features that have not been explored. We identify and summarise the unexplored areas as future directions.
... There are three basic approaches to the study of malicious behavior in OSNs: (i) focusing on link analysis (URLs, clickstreams, etc.) [9], (ii) focusing on content mining (hash tag mining, comments or status semantics analysis, image processing etc.) [10], and (iii) focusing on networks features (centrality, connectivity, degrees, community detection, shortest path, small world properties, etc.) [11]. Each of these approaches mines different categories of data crawled from online platforms in an attempt to extract valuable insights on the interrelations of the social graph. ...
... Kandias et al. proposed using negative attitudes towards authority as the main psychosocial feature for detecting insider threats, because these attitudes were found to be a main trait of malicious insiders [84]. To extract user attitudes, the authors proposed gathering records of employees' social media activities for analysis [78,79,80]. The authors performed experiments on datasets obtained from crawled YouTube comments. ...
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The adoption of smartphones, devices transforming from simple communication devices to smart and multipurpose devices, is constantly increasing. Amongst the main reasons for their vast pervasiveness are their small size, their enhanced functionality, as well as their ability to host many useful and attractive applications. Furthermore, recent studies estimate that application installation in smartphones acquired from official application repositories, such as the Apple Store, will continue to increase. In this context, the official application repositories might become attractive to attackers trying to distribute malware via these repositories. The paper examines the security inefficiencies related to application distribution via application repositories. Our contribution focuses on surveying the application management procedures enforced during application distribution in the popular smartphone platforms (i.e. Android, Black-Berry, Apple iOS, Symbian, Windows Phone), as well as on proposing a scheme for an application management system suited for secure application distribution via application repositories.
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Information systems face several security threats, some of which originate by insiders. This paper presents a novel, interdisciplinary insider threat prediction model. It combines approaches, techniques, and tools from computer science and psychology. It utilizes real time monitoring, capturing the user’s technological trait in an information system and analyzing it for misbehavior. In parallel, the model is using data from psychometric tests, so as to assess for each user the predisposition to malicious acts and the stress level, which is an enabler for the user to overcome his moral inhibitions, under the condition that the collection of such data complies with the legal framework. The model combines the above mentioned information, categorizes users, and identifies those that require additional monitoring, as they can potentially be dangerous for the information system and the organization.
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Insider threat is widely recognised as an issue of utmost importance for IS security management. In this paper, we investigate the approach followed by ISO17799, the dominant standard in IS security management, in addressing this type of threat. We unfold the criminology theory that has designated the measures against insider misuse suggested by the standard, i.e. the General Deterrence Theory, and explore the possible enhancements to the standard that could result from the study of more recent criminology theories. The paper concludes with supporting the argument for a multiparadigm and multidisciplinary approach towards IS security management and insider threat mitigation.
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Studies have shown a connection between the individual personality of the user and the way he or she behaves on line. Today many millions of people around the world are connected by being members of various Internet social networks. Ross et al. (2009) studied the connection between the personality of the individual users and their behavior on a social network. They based their study on the self-reports of users of Facebook, one of the most popular social networks, and measured five personality factors using the NEO-PI-R (Costa & McCrae, 1992) questionnaire. They found that while there was a connection between the personalities of surfers and their behavior on Facebook, it was not strong. This study is based on that of Ross et al. (2009), but in our study the self-reports of subjects, were replaced by more objective criteria, measurements of the user-information upload on Facebook. A strong connection was found between personality and Facebook behavior. Implications of the results are discussed.
A considerable research stream in information systems security has elaborated several propositions as to how privacy and anonymity can be protected, the most prominent of which make use of encryption and digital signing. Since privacy protection is a persistent topic in most electronically performed activities, the icreasing popularity of Internet has driven researchers to approach privacy protection in a holistic way. As a result, privacy-enhancing technologies have been put forth, aiming at protecting users against privacy and anonymity threats and vulnerabilities. Nowadays, that privacy protection has to be incorporated in most IT applications is one of the least controversial statements. This paper describes Privacy Protector, a technological means for enhancing privacy in an IT application development process. Privacy Protector comprises of a set of software services that have been built upon generic, privacy-focused user requirements. The paper also describes an API that can be used for incorporating Privacy Protector in the development framework of an IT application.
Past studies suggest that computer security countermeasures such as security policies, systems, and awareness programs would be effective in preventing computer abuse in organizations. They are based on the general deterrence theory, which posits that when an organization implements countermeasures that threaten abusers, its computer abuse problems would be deterred. However, computer abuse problems persist in many organizations despite these measures. This article proposes a new model of computer abuse that extends the traditional model with the social criminology theories. Focusing on computer abuse within organizations, the model explains the phenomenon through social lenses such as social bonds and social learning. The new model contributes to our theoretical body of knowledge on computer abuse by providing a new angle for approaching the problem. It suggests to practitioners that both technical and social solutions should be implemented to reduce the pervasive computer abuse problems.
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Participation in online social networking sites (hereafter "OSNS") has dramatically increased in recent years. Services such as the well known Facebook and Myspace but also Frienster, WAYN, Bebo, Google's Orkut and many others have millions of registered active users and are continuously growing. The most common model of such sites is based on the presentation of the participants' profiles and the visualisation of their network of relations to others. Also, OSNS connect participants' profiles to their public identities, using real names and other real-world identification signs (like pictures, videos, e-mail addresses, etc.) in order to enable interaction and communication between real-world subjects. Hence, a site like Facebook cannot purely be considered as a playground for "virtual bodies" in which identities are flexible and disconnected from "real-world bodies". Not only is the provision of accurate, current and complete registration information from the users encouraged, it is even required by Facebook's terms of use. This requirement, along with the service's mission of organizing the real social life of its members, provides important incentives for users to publish only real and valid information about themselves. This accurate information being provided, privacy threats derive from interactions on Facebook. In this paper, I argue that the main privacy risk on Facebook is the one of "de-contextualization" of the information provided by the participants. According to me, this "de-contextualization" threat is due to three major characteristics of Facebook: 1) the simplification of social relations, 2) the large information dissemination and 3) the network globalization and normalization effects of Facebook. The "de-contextualization" phenomenon not only threatens the right to data protection, meaning the right to control the informational identity a Human being projects in a certain context. More fundamentally it threatens the right to privacy as a Human right: the right of the human being to be a conscious multiple and relational self without unjustified discrimination.
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In the last decade we have seen a dramatic shift away from sociological explanations of deviant behavior toward developing theoretical perspectives on societal reactions to and definitions of deviance and crime. Labeling and conflict formulations have become major foci of sociological theorizing as well as the sounding boards for most of the controversy and discourse in the field of deviance. This shift in focus was deemed necessary to redress the previous imbalance of attention to the deviant behavior itself (Akers, 1968), and it clearly has had that effect. Unfortunately, it also has led to the neglect of theoretical developments in the etiology of deviant behavior. Neither labeling nor conflict perspectives has offered a general explanation of deviant behavior, although some conflict theorists have offered preliminary but incomplete efforts in that direction (Taylor, et al., 1973; Spitzer, 1975). There have been other efforts directed toward explaining deviant behavior, but these have been fairly narrow in scope; they have usually been limited either to a specific type of deviant behavior or to a restricted range of substantive variables. For example, a good deal of attention has been paid to the modern resurrection of deterrence theory (Gibbs, 1975; 1977; Waldo and Chiricos, 1972, Tittle, 1975; Silberman, 1976; Erickson et al., 1977; Meier and Johnson, 1977; Geerken and Gove, 1977). The scope of deterrence theory has been changed little, however, since its statement by the classical criminologists two centuries ago and is limited to the actual or perceived certainty, severity, and celerity of formally administered legal sanctions for violations of the criminal law. Another example is Travis Hirschi’ s (1969) control (social bonding) theory which is a more general explanation of deviance than deterrence theory, but which is, in turn, primarily restricted to informal social control which comes from individuals being bonded to groups and institutions.
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To investigate whether the long-term preservation of the authenticity of electronic healthcare records (EHR) is possible. To propose a mechanism that enables the secure validation of an EHR for long periods, far beyond the lifespan of a digital signature and at least as long as the lifetime of a patient. The study is based on the fact that although the attributes of data authenticity, i.e. integrity and origin verifiability, can be preserved by digital signatures, the necessary period for the retention of EHRs is far beyond the lifespan of a simple digital signature. It is identified that the lifespan of signed data is restricted by the validity period of the relevant keys and the digital certificates, by the future unavailability of signature-verification data, and by suppression of trust relationships. In this paper, the notarization paradigm is exploited, and a mechanism for cumulative notarization of signed EHR is proposed. The proposed mechanism implements a successive trust transition towards new entities, modern technologies, and refreshed data, eliminating any dependency of the relying party on ceased entities, obsolete data, or weak old technologies. The mechanism also exhibits strength against various threat scenarios. A future relying party will have to trust only the fresh technology and information provided by the last notary, in order to verify the authenticity of an old signed EHR. A Cumulatively Notarized Signature is strong even in the case of the compromise of a notary in the chain.
Predicting the insider threat via social media: The YouTube case
  • M Kandias
  • V Stavrou
  • N Bosovic
  • D Gritzalis
Kandias, M., Stavrou, V., Bosovic, N., and Gritzalis, D., "Predicting the insider threat via social media: The YouTube case", 12 th Workshop on Privacy in the Electronic Society, 2013.
Privacy, accountability and trust: Challen-ges and opportunities
  • C Castelluccia
  • P Druschel
  • S Hübner
  • A Pasic
  • B Preneel
  • H Tschofenig
Castelluccia, C., Druschel, P., Hübner, S., Pasic, A., Preneel, B., and Tschofenig, H., " Privacy, accountability and trust: Challen-ges and opportunities ", Technical Report, ENISA, 2011.
Towards an interdisciplinary InfoSec education model
  • D Gritzalis
  • M Theoharidou
  • E Kalimeri
Gritzalis, D., Theoharidou, M., and Kalimeri, E., "Towards an interdisciplinary InfoSec education model", 4 th IFIP World Conference on Information Security Education, pp. 22-35, 2005.
Which side are you on? A new Panopticon vs. privacy
  • M Kandias
  • L Mitrou
  • V Stavrou
  • D Gritzalis
Proactive insider threat detection through graph learning and psychological context
  • O Brdiczka
  • J Liu
  • B Price
  • J Shen
  • A Patil
  • R Chow
  • N Ducheneaut
Brdiczka, O., Liu, J., Price, B., Shen, J., Patil, A., Chow, R., and Ducheneaut, N., "Proactive insider threat detection through graph learning and psychological context", 33 rd IEEE Symposium on Security and Privacy, IEEE, pp. 142-149, 2012.
The Insider Threat: An introduction to detecting and deterring an insider spy
  • Fbi
FBI, "The Insider Threat: An introduction to detecting and deterring an insider spy", 2012. http://www.fbi.gov/about-us/ investigate/counterintelligence/ the-insider-threat.
  • C Castelluccia
  • P Druschel
  • S Hübner
  • A Pasic
  • B Preneel
  • H Tschofenig
Castelluccia, C., Druschel, P., Hübner, S., Pasic, A., Preneel, B., and Tschofenig, H., "Privacy, accountability and trust: Challenges and opportunities", Technical Report, ENISA, 2011.