A description of a Dempster type double focusing mass spectrograph, converted to a mass spectrometer is reported. The latter part of the thesis deals with an investigation of a Shaw type solid ion source. Ten microgram samples of VaCl2 and SrCl2 yielded measurable ion currents of 1.0 x 10-11 amperes. Results with UF4 and PbI2 are also reported. / Thesis / Master of Science (MS)

Thesis (M.S. in Information Systems)--Naval Postgraduate School, March 1992. / Thesis Advisor: Bhargava, Hemant. "March 1992." Description based on title screen as viewed on March 4, 2009. Includes bibliographical references (p. 71-72). Also available in print.

Thesis (Ph.D.)--University of Delaware, 2006. / Principal faculty advisor: Busby N. O. Attoh-Okine, Dept. of Civil & Environmental Engineering. Includes bibliographical references.

Thesis (D. Mus. Arts)--University of Washington, 1998. / Vita. Includes bibliographical references (p. [60]-65).

Thèse (Ph. D.)--Université Laval, 2007. / Titre de l'écran-titre (visionné le 5 mai 2008). Bibliogr.

Thesis (Ph. D.)--West Virginia University, 2004. / Title from document title page. Document formatted into pages; contains x, 118 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 104-118).

Universiẗat, Diss., 1996--Saarbrücken.

Intrusion detection is the essential part of network security in combating against illegal network access or malicious cyberattacks. Due to the constantly evolving nature of cyber attacks, it has been a technical challenge for an intrusion detection system (IDS) to effectively recognize unknown attacks or known attacks with inadequate training data. Therefore in this dissertation work, an innovative two-stage classifier is developed for accurately and efficiently detecting both unknown attacks and known attacks with insufficient or inaccurate training information. The novel two-stage fuzzy classification scheme is based on advanced machine learning techniques specifically for handling the ambiguity of traffic connections and network data. In the first stage of the classification, a fuzzy C-means (FCM) algorithm is employed to softly compute and optimize clustering centers of the training datasets with some degree of fuzziness counting for feature inaccuracy and ambiguity in the training data. Subsequently, a distance-weighted k-NN (k-nearest neighbors) classifier, combined with the Dempster-Shafer Theory (DST), is introduced to assess the belief functions and pignistic probabilities of the incoming data associated with each of known classes to further address the data uncertainty issue in the cyberattack data. In the second stage of the proposed classification algorithm, a subsequent classification scheme is implemented based on the obtained pignistic probabilities and their entropy functions to determine if the input data are normal, one of the known attacks or an unknown attack. Secondly, to strengthen the robustness to attacks, we form the three-layer hierarchy ensemble classifier based on the FCM weighted k-NN DST classifier to have more precise inferences than those made by a single classifier. The proposed intrusion detection algorithm is evaluated through the application of the KDD’99 datasets and their variants containing known and unknown attacks. The experimental results show that the new two-stage fuzzy KNN-DST classifier outperforms other well-known classifiers in intrusion detection and is especially effective in detecting unknown attacks.

Towards having a one size fits all solution to cocaine detection at borders; this thesis proposes a systematic cocaine detection methodology that can use raw data output from a fibre optic sensor to produce a set of unique features whose decisions can be combined to lead to reliable output. This multidisciplinary research makes use of real data sourced from cocaine analyte detecting fibre optic sensor developed by one of the collaborators - City University, London. This research advocates a two-step approach: For the first step, the raw sensor data are collected and stored. Level one fusion i.e. analyses, pre-processing and feature extraction is performed at this stage. In step two, using experimentally pre-determined thresholds, each feature decides on detection of cocaine or otherwise with a corresponding posterior probability. High level sensor fusion is then performed on this output locally to combine these decisions and their probabilities at time intervals. Output from every time interval is stored in the database and used as prior data for the next time interval. The final output is a decision on detection of cocaine. The key contributions of this thesis includes investigating the use of data fusion techniques as a solution for overcoming challenges in the real time detection of cocaine using fibre optic sensor technology together with an innovative user interface design. A generalizable sensor fusion architecture is suggested and implemented using the Bayesian and Dempster-Shafer techniques. The results from implemented experiments show great promise with this architecture especially in overcoming sensor limitations. A 5-fold cross validation system using a 12 13 - 1 Neural Network was used in validating the feature selection process. This validation step yielded 89.5% and 10.5% true positive and false alarm rates with 0.8 correlation coefficient. Using the Bayesian Technique, it is possible to achieve 100% detection whilst the Dempster Shafer technique achieves a 95% detection using the same features as inputs to the DF system.

WICKRAMARATHNE, T. L. (M.S., Electrical and Computer Engineering) A Belief Theoretic Approach for Automated Collaborative Filtering (May 2008) Abstract of a thesis at the University of Miami. Thesis supervised by Professor Kamal Premaratne. No. of pages in text. (84) Automated Collaborative Filtering (ACF) is one of the most successful strategies available for recommender systems. Application of ACF in more sensitive and critical applications however has been hampered by the absence of better mechanisms to accommodate imperfections (ambiguities and uncertainties in ratings, missing ratings, etc.) that are inherent in user preference ratings and propagate such imperfections throughout the decision making process. Thus one is compelled to make various "assumptions" regarding the user preferences giving rise to predictions that lack sufficient integrity. With its Dempster-Shafer belief theoretic basis, CoFiDS, the automated Collaborative Filtering algorithm proposed in this thesis, can (a) represent a wide variety of data imperfections; (b) propagate the partial knowledge that such data imperfections generate throughout the decision-making process; and (c) conveniently incorporate contextual information from multiple sources. The "soft" predictions that CoFiDS generates provide substantial exibility to the domain expert. Depending on the associated DS theoretic belief-plausibility measures, the domain expert can either render a "hard" decision or narrow down the possible set of predictions to as smaller set as necessary. With its capability to accommodate data imperfections, CoFiDS widens the applicability of ACF, from the more popular domains, such as movie and book recommendations, to more sensitive and critical problem domains, such as medical expert support systems, homeland security and surveillance, etc. We use a benchmark movie dataset and a synthetic dataset to validate CoFiDS and compare it to several existing ACF systems.