Thuraisingham, Bhavani M.
Permanent URI for this collectionhttps://hdl.handle.net/10735.1/6768
Bhavani Thuraisingham is a Professor of Computer Science, Executive Director of the Cyber Security Research and Education Institute and the Louis A. Beecherl Jr. Distinguished Professor. Her research interests include:
- Data Security
- Data Mining for Counter-Terrorism
- Secure Cloud Computing
- Assured Information Sharing
- Surveillance/Biometrics
- Cyber Security
- Privacy
News
Awarded the 2019 Communications and Information Security Technical Recognition Award from the Institute of Electrical and Electronics Engineers (IEEE) Communications Society.
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Recent Submissions
Item Specification and Analysis of ABAC Policies via the Category-Based Metamodel(Assoc Computing Machinery, 2019-03-25) Fernandez, Maribel; Mackie, Ian; Thuraisingham, Bhavani; Thuraisingham, BhavaniThe Attribute-Based Access Control (ABAC) model is one of the most powerful access control models in use. It subsumes popular models, such as the Role-Based Access Control (RBAC) model, and can also enforce dynamic policies where authorisations depend on values of user, resource or environment attributes. However, in its general form, ABAC does not lend itself well to some operations, such as review queries, and ABAC policies are in general more difficult to specify and analyse than simpler RBAC policies. In this paper we propose a formal specification of ABAC in the category-based metamodel of access control, which adds structure to ABAC policies, making them easier to design and understand. We provide an axiomatic and an operational semantics for ABAC policies, and show how to use them to analyse policies and evaluate review queries.Item Multistream Classification for Cyber Threat Data with Heterogeneous Feature Space(Association for Computing Machinery, Inc, 2019-05) Li, Yifan; Tao, Hemeng; Gao, Yang; Khan, Latifur; Ayoade, Gbadebo; Thuraisingham, B.; 51656251 (Khan, L); Li, Yifan; Tao, Hemeng; Gao, Yang; Khan, Latifur; Ayoade, Gbadebo; Thuraisingham, B.Under a newly introduced setting of multistream classification, two data streams are involved, which are referred to as source and target streams. The source stream continuously generates data instances from a certain domain with labels, while the target stream does the same task without labels from another domain. Existing approaches assume that domains for both data streams are identical, which is not quite true in real world scenario, since data streams from different sources may contain distinct features. Furthermore, obtaining labels for every instance in a data stream is often expensive and time-consuming. Therefore, it has become an important topic to explore whether labeled instances from other related streams can be helpful to predict those unlabeled instances in a given stream. Note that domains of source and target streams may have distinct features spaces and data distributions. Our objective is to predict class labels of data instances in the target stream by using the classifiers trained by the source stream. We propose a framework of multistream classification by using projected data from a common latent feature space, which is embedded from both source and target domains. This framework is also crucial for enterprise system defenders to detect cross-platform attacks, such as Advanced Persistent Threats (APTs). Empirical evaluation and analysis on both real-world and synthetic datasets are performed to validate the effectiveness of our proposed algorithm, comparing to state-of-the-art techniques. Experimental results show that our approach significantly outperforms other existing approaches. © 2019 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC-BY 4.0 License.Item Towards Self-Adaptive Metric Learning on the Fly(Association For Computing Machinery, Inc, 2019-05) Gao, Yang; Li, Yi-Fan; Chandra, Swarup; Khan, Latifur; Thuraisingham, Bhavani; 51867299 (Thuraisingham, BM); Gao, Yang; Li, Yi-Fan; Chandra, Swarup; Khan, Latifur; Thuraisingham, Bhavani M.Good quality similarity metrics can significantly facilitate the performance of many large-scale, real-world applications. Existing studies have proposed various solutions to learn a Mahalanobis or bilinear metric in an online fashion by either restricting distances between similar (dissimilar) pairs to be smaller (larger) than a given lower (upper) bound or requiring similar instances to be separated from dissimilar instances with a given margin. However, these linear metrics learned by leveraging fixed bounds or margins may not perform well in real-world applications, especially when data distributions are complex. We aim to address the open challenge of “Online Adaptive Metric Learning” (OAML) for learning adaptive metric functions on-the-fly. Unlike traditional online metric learning methods, OAML is significantly more challenging since the learned metric could be non-linear and the model has to be self-adaptive as more instances are observed. In this paper, we present a new online metric learning framework that attempts to tackle the challenge by learning a ANN-based metric with adaptive model complexity from a stream of constraints. In particular, we propose a novel Adaptive-Bound Triplet Loss (ABTL) to effectively utilize the input constraints, and present a novel Adaptive Hedge Update (AHU) method for online updating the model parameters. We empirically validates the effectiveness and efficacy of our framework on various applications such as real-world image classification, facial verification, and image retrieval. © 2019 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC-BY 4.0 License.Item Towards a Privacy-Aware Quantified Self Data Management Framework(Association for Computing Machinery) Thuraisingham, Bhavani M.; Kantarcioglu, Murat; Bertino, E.; Bakdash, Jonathan Z.; Fernandez, M.; Thuraisingham, Bhavani M.; Kantarcioglu, Murat; Bakdash, Jonathan Z.Massive amounts of data are being collected, stored, and analyzed for various business and marketing purposes. While such data analysis is critical for many applications, it could also violate the privacy of individuals. This paper describes the issues involved in designing a privacy aware data management framework for collecting, storing, and analyzing the data. We also discuss behavioral aspects of data sharing as well as aspects of a formal framework based on rewriting rules that encompasses the privacy aware data management framework. ©2018 Association for Computing Machinery.Item Integrating Cyber Security and Data Science for Social Media: A Position Paper(Institute of Electrical and Electronics Engineers Inc.) Thuraisingham, Bhavani M.; Kantarcioglu, Murat; Khan, Latifur; 0000-0001-6423-4533 (Kantarcioglu, M); 51867299 (Thuraisingham, BM); 305367293 (Kantarcioglu, M); 51656251 (Khan, L); Thuraisingham, Bhavani M.; Kantarcioglu, Murat; Khan, LatifurCyber security and data science are two of the fastest growing fields in Computer Science and more recently they are being integrated for various applications. This position paper will review the developments in applying Data science for cyber security and cyber security for data science and then discuss the applications in Social Media.Item Large-Scale Realistic Network Data Generation on a Budget(Institute of Electrical and Electronics Engineers Inc.) Ricks, Brian; Tague, P.; Thuraisingham, Bhavani M.; 51867299 (Thuraisingham, BM); Ricks, Brian; Thuraisingham, Bhavani M.Many novel problems in computer networking require relevant network trace data during the research process. Unfortunately, such data can often be hard to find, which becomes a problem within itself. While generating appropriate data using in-lab network testbeds and simulators are feasible solutions, the former has limitations in terms of network scale, while the latter has limitations in the generated data. To help address these issues, we present an approach for the generation of realistic network trace data in a contained, large-scale network environment. We use network emulation to enable large-scale, in-lab networking, and a software framework we developed to support autonomous client-side protocols and services, including user-behavioral models which scale in a shared CPU environment. Our framework also enables quick experiment setup and monitoring. We show through experimentation on a low-end laptop that our approach enables network scale into the hundreds of nodes, allowing anyone with even basic hardware to generate potentially relevant, realistic network data.