Browsing by Author "Thuraisingham, Bhavani M."
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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.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 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.