Claudia Picardi

User authentication through keystroke dynamics

Unlike other access control systems based on biometric features, keystroke analysis has not led to techniques providing an acceptable level of accuracy. The reason is probably the intrinsic variability of typing dynamics, versus other---very stable---biometric characteristics, such as face or fingerprint patterns. In this paper we present an original measure for keystroke dynamics that limits the instability of this biometric feature. We have tested our approach on 154 individuals, achieving a False Alarm Rate of about 4% and an Impostor Pass Rate of less than 0.01%. This performance is reached using the same sampling text for all the individuals, allowing typing errors, without any specific tailoring of the authentication system with respect to the available set of typing samples and users, and collecting the samples over a 28.8-Kbaud remote modem connection.

Keystroke analysis of free text

Keystroke dynamics can be useful to ascertain personal identity even after an authentication phase has been passed, provided that we are able to deal with the typing rhythms of free text, chosen and entered by users without any specific constraint. In this paper we present a method to compare typing samples of free text that can be used to verify personal identity. We have tested our technique with a wide set of experiments on 205 individuals, obtaining a False Alarm Rate of less than 5&percent; and an Impostor Pass Rate of less than 0.005&percent;. Different trade-offs are, however, possible. Our approach can rely on what is typed by people because of their normal job, and a few lines of text, even collected in different working sessions, are sufficient to reach a high level of accuracy, which improves proportionally to the amount of available information: As a consequence, we argue that our method can be useful in computer security as a complementary or alternative way to user authentication and as an aid to intrusion detection.

Enhancing Web services with diagnostic capabilities

Fault management in Web services composed by individual services from multiple suppliers currently relies on a local analysis that does not span across individual services, thus limiting the effectiveness of recovery strategies. We propose to address this limitation of current standards for Web service composition by employing model-based diagnosis to enhance fault analysis. We propose to add diagnostic Web services to the set of Web services providing the overall service, acting as supervisors of their execution, by identifying anomalies and explaining them in terms of faults to be repaired. This approach poses the basis for the development of specialized recovery and compensation techniques aimed at addressing different problems, which could not be otherwise discriminated.

Keystroke analysis of different languages: a case study

Typing rhythms are one of the rawest form of data stemming from the interaction between humans and computers. When properly analyzed, they may allow to ascertain personal identity. In this paper we provide experimental evidence that the typing dynamics of free text can be used for user identification and authentication even when typing samples are written in different languages. As a consequence, we argue that keystroke analysis can be useful even when people may use different languages, in those areas where ascertaining personal identity is important or crucial, such as within Computer Security.

Interacting with social networks of intelligent things and people in the world of gastronomy

This article introduces a framework for creating rich augmented environments based on a social web of intelligent things and people. We target outdoor environments, aiming to transform a region into a smart environment that can share its cultural heritage with people, promoting itself and its special qualities. Using the applications developed in the framework, people can interact with things, listen to the stories that these things tell them, and make their own contributions. The things are intelligent in the sense that they aggregate information provided by users and behave in a socially active way. They can autonomously establish social relationships on the basis of their properties and their interaction with users. Hence when a user gets in touch with a thing, she is also introduced to its social network consisting of other things and of users; she can navigate this network to discover and explore the world around the thing itself. Thus the system supports serendipitous navigation in a network of things and people that evolves according to the behavior of users. An innovative interaction model was defined that allows users to interact with objects in a natural, playful way using smartphones without the need for a specially created infrastructure. The framework was instantiated into a suite of applications called WantEat, in which objects from the domain of tourism and gastronomy (such as cheese wheels or bottles of wine) are taken as testimonials of the cultural roots of a region. WantEat includes an application that allows the definition and registration of things, a mobile application that allows users to interact with things, and an application that supports stakeholders in getting feedback about the things that they have registered in the system. WantEat was developed and tested in a real-world context which involved a region and gastronomy-related items from it (such as products, shops, restaurants, and recipes), through an early evaluation with stakeholders and a final evaluation with hundreds of users.

Semantic similarity in heterogeneous ontologies

Recent extensive usage of ontologies as knowledge bases that enable rigorous representation and reasoning over heterogenous data poses certain challenges in their construction and maintenance. Many of these ontologies are incomplete, containing many dense sub-ontologies. A need arises for a measure that would help calculate the similarity between the concepts in these kinds of ontologies. In this work, we introduce a new similarity measure for ontological concepts that takes these issues into account. It is based on conceptual specificity, which measures how much a certain concept is relevant in a given context, and on conceptual distance, which introduces different edge lengths in the ontology graph. We also address the problem of computing similarity between concepts in the presence of implicit classes in ontologies. The evaluation of our approach shows an improvement over Leacock and Chodorows distance based measure. Finally, we provide two application domains which can benefit when this similarity measure is used.

Toward a social web of intelligent things

In this paper we introduce the notion of “Social Web of Intelligent Things” (SWIT hereafter) as a an evolution of both the “Web of Things” and “Smart Objects” paradigms. In a SWIT, things become entities capable of an intelligent and social behavior. On the one hand, things maintain and socialize knowledge and can interact and communicate with people; on the other, “social” networks of people and things arise as a result of this interaction. SWIT is an evolution of social media that goes beyond the desktop paradigm and is a way of bridging the gap between real-life and virtual experiences. Interactions between people and things happen naturally in real life, augmenting and enhancing peoples experiences. We claim that SWITs are a challenging area of research and application for Artificial Intelligence in the context of the Future Internet. The paper provides a characterization of SWITs, discussing the ingredients that are needed to create a SWIT; it introduces a framework for building SWITs and briefly presents an instance we developed and tested with users. In the paper we claim that many areas of Artificial Intelligence can contribute to the is framework, which in turn can stimulate research in these areas and on their integration.

Dealing with different languages and old profiles in keystroke analysis of free text

In this paper we show experimentally that typing dynamics of free text provide useful information for user identification and authentication even when a long time has passed since typing profiles of users were formed, and even when users are writing in a language different from the one used to form their profiles. Hence, we argue that keystroke analysis can be an effective aid in different areas where ascertaining user identity is important or crucial, including Computer Security and User Modeling.

Advanced fault analysis in web service composition

Currently, fault management in Web Services orchestrating multiple suppliers relies on a local analysis, that does not span across individual services, thus limiting the effectiveness of recovery strategies. We propose to address this limitation by employing Model-Based Diagnosis to enhance fault analysis. In our approach, a Diagnostic Web Service is added to the set of Web Services providing the overall service, and acts as a supervisor of their execution, by identifying anomalies and explaining them in terms of faults to be repaired.

Ontology-driven collaborative annotation in shared workspaces

In the last decade, collaboration and sharing on the Web have become mainstream. Digital, remote interaction happens on a daily basis, not only to share digital resources, but also to create, manage and discuss them, in every possible situation where collaboration is required: from work teams to groups of friends, from community committees to no-profit organizations. In this paper we address the task of collaborative management of digital resources within a team, with a special focus on the task of semantic annotation, where team members, possibly supported by automated reasoning, enrich resources with properties that help in organizing, retrieving and creating connections between contents of different types. We focus in particular on the problem of reaching an agreement on the annotation itself among the participants. The paper presents a qualitative user study aimed at observing users behavior when faced with this task. The results of the study are then analyzed in order to draw guidelines, which are then implemented in a tool for collaborative annotation. This study is carried out in the context of the Semantic Table Plus Plus (Sem T++) Project, a framework supporting collaboration over thematic workspaces, whose goal is to enhance cooperation through awareness, enhanced communication and easy sharing of digital content. Display Omitted Sem T++ is a framework for the collaborative management of shared online resources.Sem T++ supports collaborative semantic annotation, thanks to a formal semantic model.Implementation of collaborative annotation is based on results of a user study.User study analyzes three collaboration policies: consensual, authored, supervised.