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PhD thesis abstracts
March 2011
PhD thesis abstractsAndre MiedeCross-Organizational Service Security - Attack Modeling and Evaluation of Selected Countermeasures
Just as any economic system requires security in order to function and to be accepted by its participants, the security of the involved IT systems, exchanged messages, and communication channels used has to be ensured for cross-organizational service-based collaboration. Achieving and guaranteeing basic IT security goals such as confidentiality, authentication, authorization, non-repudiation, integrity, availability, and anonymity is a necessity in this context and an active topic, both in research and industry. The main tenor of current SOA security research is that conventional security measures are not effective enough in the SOA context. Furthermore, just equalizing SOA security with Web service security reduces SOA security requirements to Web service security standards and their configuration, which is an incomplete view. This thesis makes several contributions regarding the security of service-based systems: First, it is shown how a model of cross-organizational SOA concepts can be used for analyzing SOA elements regarding their impact on security. This is done by applying core IT security concepts, such as threats, vulnerabilities, etc., to the general elements of a cross-organizational SOA, such as loose coupling, composability, etc. Second, an analysis of attacks in the Internet of Services is performed by proposing an attack taxonomy for service-based systems and by modeling selected examples of service-specific attack classes. This goes beyond the current state-of-the-art regarding SOA attacks by taking into account more service-specific and business-oriented threats. The modeling of these attacks builds on a self-developed generic metamodel, that brings together the most important concepts of IT security and their relationships. It is shown, how assets, threats, vulnerabilities, risks, security goals, etc. relate to each other at the core of this metamodel and what the basic structure of countermeasures is. Third, an attack scenario of communication analysis that threatens relationship anonymity in the Internet of Services is further investigated, due to its system-inherent implications. With a particular focus on service compositions, a simulation-based evaluation of different attack models and scenarios offers insights regarding the anonymity of cross-organizational collaboration. Furthermore, the impact of using standard anonymity mechanisms on selected Quality of Service parameters is evaluated for Web services in real networks. The obtained results aim at identifying the limits of anonymity in the Internet of Services and at quantifying side-effects of using state-of-the-art countermeasures.
Beatriz SoretAnalysis of QoS parameters in fading channels based on the effective bandwidth theory
In this thesis, a variable-rate multiuser and multichannel system using adaptive modulation is addressed. Specifically, the tradeoff among information source, fading channel and delay is analyzed, based on the effective bandwidth theory. Within the effective bandwidth framework, expressions of the channel effective bandwidth function (also known as effective capacity) are obtained on the channel side. Several scenarios are addressed: uncorrelated and time-correlated flat Rayleigh channels and an OFDM system under a frequency-selective Rayleigh channels. The procedure to obtain these functions is generic and could be applied to other channel models and scenarios. The effective bandwidth theory makes feasible the analysis of the distribution tail of the delay. The percentile of the delay and the maximum information rate that can be transmitted over the channel under a target BER and a probabilistic delay constraint are evaluated. The delay suffered by certain information flow depends not only on the transmission rate but also on the distribution and self-correlation of the information process. Even in wired systems (constant rate channels) different distributions of the information process having the same average rate will cause different delays. Indeed, the better conditions for the delay are obtained when the incoming user traffic is constant. For any other source process, the delay performance degrades. Besides, the correlation of the channel process, in time or in frequency, has also a negative impact on the delay. In the last part of the thesis, multiplexing of users over multiple shared fading channel is addressed. A new element comes up in this case: the scheduling algorithm. We calculate the maximum rate that each user can transmit by fulfilling a target BER and its own delay constraint, and under a given scheduling discipline. The analysis is done first in a single channel link and later on generalized to multiple shared channels employing OFDMA as multiplexing mechanism. Now it is not only the delay constraint and the channel and source process that influence the source rate, but also the discipline that rules the system. Three representative multiplexing algorithms are analyzed: Round Robin, Best Channel and Proportional Fair. The results make possible the comparison of the algorithms in terms of throughput, delay and fairness. In summary, this thesis shows the high sensitivity of the delay to the burstiness of the traffic, to the time or frequency correlation of the channel and to the scheduling discipline. The proposed procedure is generic and can be extended to other disciplines and traffic and channel models. Nevertheless, the effective bandwidth function of the source and the channel process cannot always be explicitly evaluated. For such cases, a semi-analytical strategy is also proposed.
Lin LinMultimedia Data Mining and Retrieval for Multimedia Databases Using Associations and Correlations
This research mainly focuses on multimedia data mining and retrieval system for multimedia databases by addressing some main challenges, such as data imbalance, data quality, semantic gap, user subjectivity and searching issues. Therefore, a novel CBMIR system is proposed in this dissertation. The proposed system utilizes both association rule mining (ARM) technique and multiple correspondence analysis (MCA) technique by taking into account both pattern discovery and statistical analysis. First, media content is represented by the global and local low-level and mid-level features and stored in the multimedia database. Second, a data filtering component is proposed in the system to improve the data quality and reduce the data imbalance. To be specific, the proposed filtering step is able to vertically select features and horizontally prune instances in multimedia databases. Third, a new learning and classification method mining weighted association rules is proposed in the retrieval system. The MCA-based correlation is used to generate and select the weighted N-feature-value pair rules, where the N varies from 1 to many. Forth, a ranking method independent of classifiers is proposed in the system to sort the retrieved results and put the most interesting ones on the top of the browsing list. Finally, a user interface is implemented in CBMIR system that allows the user to choose his/her interested concept, searches media based on the target concept, ranks the retrieved segments using the proposed ranking algorithm, and then displays the top-ranked segments to the user. The system is experimented with various high-level semantics from TRECVID benchmark data sets. TRECVID sound and vision data is a large data set, includes various types of videos, and has very rich semantics. Overall, the proposed system achieves promising results in comparison with the other well-known methods. Moreover, experiments that compare each component with some other famous algorithms are conducted. The experimental results show that all proposed components improve the functionalities of the CBMIR system, and the proposed system reaches effectiveness, robustness and efficiency for a high-dimensional multimedia database.
Peter KneesText-Based Description of Music for Indexing, Retrieval, and Browsing
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In the context of this thesis, different extraction, indexing, and retrieval strategies are elaborated and evaluated. Furthermore, the potential of complementing Web-based retrieval with acoustic similarity extracted from the audio signal, as well as complementing audio-similarity-based browsing approaches with Web-based descriptors is investigated and demonstrated in prototype applications.
Vineeth N BalasubramanianConformal Predictions in Multimedia Pattern Recognition
This dissertation builds on the theory of Conformal Predictions to compute reliable confidence measures that aid decision-making in real-world multimedia problems. The theory behind the CP framework guarantees that the confidence values obtained using this transductive inference framework manifest as the actual error frequencies in the online setting, i.e. they are well-calibrated. Further, this framework can be used with any classifier, meta-classifier or regressor (such as Support Vector Machines, k-Nearest Neighbors, Adaboost, ridge regression, etc). The key contributions of this dissertation (outlined below) are validated on four problems from the domains of healthcare and assistive technologies: two classification-based applications (risk prediction in cardiac decision support and multimodal person recognition), and two regression-based applications (head pose estimation and saliency prediction in radiological images). The cost of errors in decision-making is often high in these application domains, and hence these problems are selected to validate the contributions. The key contributions of this work are summarized below: (1) Efficiency Maximization in Conformal Predictors: The CP framework has two important properties that define its utility: validity and efficiency. Validity refers to controlling the frequency of errors within a pre-specified error threshold. Also, since the framework outputs a set of possible predictions as the result, it is essential that the prediction sets are as small as possible. This property is called efficiency. Evidently, an ideal implementation of the framework would ensure that the algorithm provides high efficiency along with validity. However, this is not a straightforward task, and depends on the learning algorithm (classification or regression, as the case may be) as well as the non-conformity measure chosen in a given context. In this work, a novel framework to learn a kernel (or distance metric) that will maximize the efficiency in a given context has been proposed and validated on different risk-sensitive applications. (2) Conformal Predictions for Information Fusion: The CP framework ensures the calibration property in the estimation of confidence in pattern recognition. Most of the existing work in this context has been carried out using single classification systems or ensemble classifiers (such as boosting). However, there been a recent growth in the use of multimodal fusion algorithms and multiple classifier systems. A study of statistical approaches to combine p-values from multiple classifiers and regressors has been performed, which revealed the usefulness of quantile combination methods for calibrated confidence values in information fusion contexts. (3) Online Active Learning using Conformal Predictors: As newer data are encountered, it becomes essential to select appropriate data instances for labeling and updating the classifier to facilitate a continuously learning system. Using the p-values computed by the CP framework, a novel online active learning approach has been proposed and validated. This active learning method can also be extended to an information fusion setting, where there are multiple information sources or multiple modalities. The results obtained in this work demonstrate promise and potential in using these contributions to provide reliable measures of confidence in multimedia pattern recognition problems in real-world settings.
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