Multi-Agent Security System based on Neural Network Model of User's Behavior

It is proposed an agent approach for creation of intelligent intrusion detection system. The system allows detecting known type of attacks and anomalies in user activity and computer system behavior. The system includes different types of intelligent agents. The most important one is user agent based on neural network model of user behavior. Proposed approach is verified by experiments in real Intranet of Institute of Physics and Technologies of National Technical University of Ukraine "Kiev Polytechnic Institute”.

User-representative feature selection for keystroke dynamics

Continuous user authentication with keystroke dynamics uses characters sequences as features. Since users can type characters in any order, it is imperative to find character sequences (n-graphs) that are representative of user typing behavior. The contemporary feature selection approaches do not guarantee selecting frequently-typed features which may cause less accurate statistical user-representation. Furthermore, the selected features do not inherently reflect user typing behavior. We propose four statistical based feature selection techniques that mitigate limitations of existing approaches. The first technique selects the most frequently occurring features. The other three consider different user typing behaviors by selecting: n-graphs that are typed quickly; n-graphs that are typed with consistent time; and n-graphs that have large time variance among users. We use Gunetti’s keystroke dataset and k-means clustering algorithm for our experiments. The results show that among the proposed techniques, the most-frequent feature selection technique can effectively find user representative features. We further substantiate our results by comparing the most-frequent feature selection technique with three existing approaches (popular Italian words, common n-graphs, and least frequent ngraphs). We find that it performs better than the existing approaches after selecting a certain number of most-frequent n-graphs.

An examination of keystroke dynamics for continuous user authentication

Most current computer systems authorise the user at the start of a session and do not detect whether the current user is still the initial authorised user, a substitute user, or an intruder pretending to be a valid user. Therefore, a system that continuously checks the identity of the user throughout the session is necessary without being intrusive to end-user and/or effectively doing this. Such a system is called a continuous authentication system (CAS). Researchers have applied several approaches for CAS and most of these techniques are based on biometrics. These continuous biometric authentication systems (CBAS) are supplied by user traits and characteristics. One of the main types of biometric is keystroke dynamics which has been widely tried and accepted for providing continuous user authentication. Keystroke dynamics is appealing for many reasons. First, it is less obtrusive, since users will be typing on the computer keyboard anyway. Second, it does not require extra hardware. Finally, keystroke dynamics will be available after the authentication step at the start of the computer session. Currently, there is insufficient research in the CBAS with keystroke dynamics field. To date, most of the existing schemes ignore the continuous authentication scenarios which might affect their practicality in different real world applications. Also, the contemporary CBAS with keystroke dynamics approaches use characters sequences as features that are representative of user typing behavior but their selected features criteria do not guarantee features with strong statistical significance which may cause less accurate statistical user-representation. Furthermore, their selected features do not inherently incorporate user typing behavior. Finally, the existing CBAS that are based on keystroke dynamics are typically dependent on pre-defined user-typing models for continuous authentication. This dependency restricts the systems to authenticate only known users whose typing samples are modelled. This research addresses the previous limitations associated with the existing CBAS schemes by developing a generic model to better identify and understand the characteristics and requirements of each type of CBAS and continuous authentication scenario. Also, the research proposes four statistical-based feature selection techniques that have highest statistical significance and encompasses different user typing behaviors which represent user typing patterns effectively. Finally, the research proposes the user-independent threshold approach that is able to authenticate a user accurately without needing any predefined user typing model a-priori. Also, we enhance the technique to detect the impostor or intruder who may take over during the entire computer session.

Discovering user access pattern based on probabilistic latent factor model

There has been an increased demand for characterizing user access patterns using web mining techniques since the informative knowledge extracted from web server log files can not only offer benefits for web site structure improvement but also for better understanding of user navigational behavior. In this paper, we present a web usage mining method, which utilize web user usage and page linkage information to capture user access pattern based on Probabilistic Latent Semantic Analysis (PLSA) model. A specific probabilistic model analysis algorithm, EM algorithm, is applied to the integrated usage data to infer the latent semantic factors as well as generate user session clusters for revealing user access patterns. Experiments have been conducted on real world data set to validate the effectiveness of the proposed approach. The results have shown that the presented method is capable of characterizing the latent semantic factors and generating user profile in terms of weighted page vectors, which may reflect the common access interest exhibited by users among same session cluster.

Constructing tag ontology from folksonomy based on WordNet

With the emergence of Web 2.0, Web users can classify Web items of their interest by using tags. Tags reflect users’ understanding to the items collected in each tag. Exploring user tagging behavior provides a promising way to understand users’ information needs. However, free and relatively uncontrolled vocabulary has its drawback in terms of lack of standardization and semantic ambiguity. Moreover, the relationships among tags have not been explored even there exist rich relationships among tags which could provide valuable information for us to better understand users. In this paper, we propose a novel approach to construct tag ontology based on the widely used general ontology WordNet to capture the semantics and the structural relationships of tags. Ambiguity of tags is a challenging problem to deal with in order to construct high quality tag ontology. We propose strategies to find the semantic meanings of tags and a strategy to disambiguate the semantics of tags based on the opinion of WordNet lexicographers. In order to evaluate the usefulness of the constructed tag ontology, in this paper we apply the extracted tag ontology in a tag recommendation experiment. We believe this is the first application of tag ontology for recommendation making. The initial result shows that by using the tag ontology to re-rank the recommended tags, the accuracy of the tag recommendation can be improved.

Personalization in tag ontology learning for recommendation making

Due to the explosive growth of the Web, the domain of Web personalization has gained great momentum both in the research and commercial areas. One of the most popular web personalization systems is recommender systems. In recommender systems choosing user information that can be used to profile users is very crucial for user profiling. In Web 2.0, one facility that can help users organize Web resources of their interest is user tagging systems. Exploring user tagging behavior provides a promising way for understanding users’ information needs since tags are given directly by users. However, free and relatively uncontrolled vocabulary makes the user self-defined tags lack of standardization and semantic ambiguity. Also, the relationships among tags need to be explored since there are rich relationships among tags which could provide valuable information for us to better understand users. In this paper, we propose a novel approach for learning tag ontology based on the widely used lexical database WordNet for capturing the semantics and the structural relationships of tags. We present personalization strategies to disambiguate the semantics of tags by combining the opinion of WordNet lexicographers and users’ tagging behavior together. To personalize further, clustering of users is performed to generate a more accurate ontology for a particular group of users. In order to evaluate the usefulness of the tag ontology, we use the tag ontology in a pilot tag recommendation experiment for improving the recommendation performance by exploiting the semantic information in the tag ontology. The initial result shows that the personalized information has improved the accuracy of the tag recommendation.

Integrating collaborative filtering and matching-based search for product recommendation

Currently, recommender systems (RS) have been widely applied in many commercial e-commerce sites to help users deal with the information overload problem. Recommender systems provide personalized recommendations to users and thus help them in making good decisions about which product to buy from the vast number of product choices available to them. Many of the current recommender systems are developed for simple and frequently purchased products like books and videos, by using collaborative-filtering and content-based recommender system approaches. These approaches are not suitable for recommending luxurious and infrequently purchased products as they rely on a large amount of ratings data that is not usually available for such products. This research aims to explore novel approaches for recommending infrequently purchased products by exploiting user generated content such as user reviews and product click streams data. From reviews on products given by the previous users, association rules between product attributes are extracted using an association rule mining technique. Furthermore, from product click streams data, user profiles are generated using the proposed user profiling approach. Two recommendation approaches are proposed based on the knowledge extracted from these resources. The first approach is developed by formulating a new query from the initial query given by the target user, by expanding the query with the suitable association rules. In the second approach, a collaborative-filtering recommender system and search-based approaches are integrated within a hybrid system. In this hybrid system, user profiles are used to find the target user’s neighbour and the subsequent products viewed by them are then used to search for other relevant products. Experiments have been conducted on a real world dataset collected from one of the online car sale companies in Australia to evaluate the effectiveness of the proposed recommendation approaches. The experiment results show that user profiles generated from user click stream data and association rules generated from user reviews can improve recommendation accuracy. In addition, the experiment results also prove that the proposed query expansion and the hybrid collaborative filtering and search-based approaches perform better than the baseline approaches. Integrating the collaborative-filtering and search-based approaches has been challenging as this strategy has not been widely explored so far especially for recommending infrequently purchased products. Therefore, this research will provide a theoretical contribution to the recommender system field as a new technique of combining collaborative-filtering and search-based approaches will be developed. This research also contributes to a development of a new query expansion technique for infrequently purchased products recommendation. This research will also provide a practical contribution to the development of a prototype system for recommending cars.

E-Learning-Webseite als facettenreiches Werkzeug in Forschung und Lehre

Im Rahmen des blended learning kann eine E-Learning-Webseite als Begleitmaterial einer Lehrveranstaltung eingesetzt werden oder Studierende zur aktiven Teilnahme an der Erstellung der Webseiteninhalte anregen. Darüber hinaus eignet sich eine solche Webseite als Plattform zur E-Learning-Forschung. Auch empirische Studien können dort eingebettet werden. Eine weitere wissenschaftliche Anwendung bietet die Analyse des Nutzerverhaltens, mit der sich aktuelle Forschungsergebnisse zum Lernen mit Hypermedien überprüfen lassen. Wir beschreiben eine solche, vielseitig einsetzbare Webseite, die eine Verknüpfung von universitärer Lehre und Forschung ermöglicht und als Anregung für ähnliche Projekte dienen kann. Erste Erfahrungen werden dabei berichtet und ausgewählte Empfehlungen für Dozierende und Forscher abgeleitet.

Reading nineteenth century schoolbooks online : user behavior in a digitized special collection

Special collections, because of the issues associated with conservation and use, a feature they share with archives, tend to be the most digitized areas in libraries. The Nineteenth Century Schoolbooks collection is a collection of 9000 rarely held nineteenth-century schoolbooks that were painstakingly collected over a lifetime of work by Prof. John A. Nietz, and donated to the Hillman Library at the University of Pittsburgh in 1958, which has since grown to 15,000. About 140 of these texts are completely digitized and showcased in a publicly accessible website through the University of Pittsburgh’s Library, along with a searchable bibliography of the entire collection, which expanded the awareness of this collection and its user base to beyond the academic community. The URL for the website is http://digital.library.pitt.edu/nietz/. The collection is a rich resource for researchers studying the intellectual, educational, and textbook publishing history of the United States. In this study, we examined several existing records collected by the Digital Research Library at the University of Pittsburgh in order to determine the identity and searching behaviors of the users of this collection. Some of the records examined include: 1) The results of a 3-month long user survey, 2) User access statistics including search queries for a period of one year, a year after the digitized collection became publicly available in 2001, and 3) E-mail input received by the website over 4 years from 2000-2004. The results of the study demonstrate the differences in online retrieval strategies used by academic researchers and historians, archivists, avocationists, and the general public, and the importance of facilitating the discovery of digitized special collections through the use of electronic finding aids and an interactive interface with detailed metadata.

I know what you did this summer: Users' behavior on internet

Human age is surrounded by assumed set of rules and behaviors imposed by local culture and the society they live in. This paper introduces software that counts the presence of a person on the Internet and examines the activities he/she conducts online. The paper answers questions such as how "old" are you on the Internet? How soon will a newbie be exposed to adult websites? How long will it take for a new Internet user to know about social networking sites? And how many years a user has to surf online to celebrate his/her first "birthday" of Internet presence? Paper findings from a database of 105 school and university students containing their every click of first 24 hours of Internet usage are presented. The findings provide valuable insights for Internet Marketing, ethics, Internet business and the mapping of Internet life with real life. Privacy and ethical issues related to the study have been discussed at the end. © Springer Science+Business Media B.V. 2010.

Discovering user preferences by using time entries in click-through data to improve search engine results

The search engine log files have been used to gather direct user feedback on the relevancy of the documents presented in the results page. Typically the relative position of the clicks gathered from the log files is used a proxy for the direct user feedback. In this paper we identify reasons for the incompleteness of the relative position of clicks for deciphering the user preferences. Hence, we propose the use of time spent by the user in reading through the document as indicative of user preference for a document with respect to a query. Also, we identify the issues involved in using the time measure and propose means to address them.

The Behavior Language; User's Guide

The Behavior Language is a rule-based real-time parallel robot programming language originally based on ideas from [Brooks 86], [Connell 89], and [Maes 89]. It compiles into a modified and extended version of the subsumption architecture [Brooks 86] and thus has backends for a number of processors including the Motorola 68000 and 68HCll, the Hitachi 6301, and Common Lisp. Behaviors are groups of rules which are activatable by a number of different schemes. There are no shared data structures across behaviors, but instead all communication is by explicit message passing. All rules are assumed to run in parallel and asynchronously. It includes the earlier notions of inhibition and suppression, along with a number of mechanisms for spreading of activation.

A study of information-seeking behavior and user perceptions of academic researchers

The purpose of this paper is to examine the information-seeking behavior of science and social science research scholars, including service effectiveness, satisfaction level on different type of sources and various methods adopted by the scholars for keeping up to dateData were gathered using a questionnaire survey of 200, randomly selected, PhD students of science and social science departments of four universities in Kerala, IndiaAlthough similarities exist between social science and science PhD students with regard to information-seeking behavior, there are significant differences as well. There is a significant difference between science and social science scholars on the perception of the adequacy of print journals and database collection which are very relevant to the research purposes. There is no significant difference between science and social science scholars on the perception of the adequacy of e-journals, the most used source for keeping up to date. The study proved that scholars of both the fields are dissatisfied with the effectiveness of the library in keeping them up to date with latest developments

Attention and selection behavior on ‘universal search’ result pages

The ISO norm line 9241 states some criteria for ergonomics of human system interaction. In markets with a huge variety of offers and little possibility of differentiation, providers can gain a decisive competitive advantage by user oriented interfaces. A precondition for this is that relevant information can be obtained for entrepreneurial decisions in this regard. To test how users of universal search result pages use those pages and pay attention to different elements, an eye tracking experiment with a mixed design has been developed. Twenty subjects were confronted with search engine result pages (SERPs) and were instructed to make a decision while conditions “national vs. international city” and “with vs. without miniaturized Google map” were used. Different parameters like fixation count, duration and time to first fixation were computed from the eye tracking raw data and supplemented by click rate data as well as data from questionnaires. Results of this pilot study revealed some remarkable facts like a vampire effect on miniaturized Google maps. Furthermore, Google maps did not shorten the process of decision making, Google ads were not fixated, visual attention on SERPs was influenced by position of the elements on the SERP and by the users’ familiarity with the search target. These results support the theory of Amount of Invested Mental Effort (AIME) and give providers empirical evidence to take users’ expectations into account. Furthermore, the results indicated that the task oriented goal mode of participants was a moderator for the attention spent on ads. Most important, SERPs with images attracted the viewers’ attention much longer than those without images. This unique selling proposition may lead to a distortion of competition on markets.