ACM will be having it's second meeting of the semester on Thursday, Novemeber 3 at 7:00PM in Amoco Hall. November 8 is election day so Dr. Duncan Buell will be speaking about voting machines and their relevance to the upcoming election. There will also be pizza for those who attend. More Details and RSVP

Speaker: Juan Caicedo Affiliation: Department of Civil and Environmental Engineering, USC Location: SWGN 2A31 Time: 2:20 - 3:10 PM

DISSERTATION DEFENSE Department of Computer Science and Engineering University of South Carolina Author : Muhammad Nazmus Sakib Advisor: DR. Chin-Tser Huang Date : Oct 26th 2016 Time : 8:30 am Place : 3A75 Swearingen ABSTRACT In this age of ubiquitous communication in which we can stay constantly connected with the rest of the world, for most of the part, we have to be grateful for one particular invention - the Internet. But as the popularity of Internet connectivity grows, it has become a very dangerous place where objects of malicious content and intent can be hidden in plain sight. In this dissertation, we investigate different ways to detect and capture these malicious contents hidden in the Internet. First, we propose an automated system that mimics high-risk browsing activities such as clicking on suspicious online ads, and as a result collects malicious executable files for further analysis and diagnosis. Using our system we crawled over the Internet and collected a considerable amount of malicious executables with very limited resources. Malvertising has been one of the major recent threats against cyber security. Malvertisers apply a variety of evasion techniques to evade detection, whereas the ad networks apply inspection techniques to reveal the malicious ads. However, both the malvertiser and the ad network are under the constraints of resource and time. In the second part of this dissertation, we propose a game theoretic approach to formulate the problem of inspecting the malware inserted by the malvertisers into the Web-based advertising system. During malware collection, we used the online multi-AV scanning service VirusTotal to scan and analyze the samples, which can only generate an aggregation of antivirus scan reports. We need a multi-scanner solution that can accurately determine the maliciousness of a given sample. In the third part of this dissertation, we introduce three theoretical models, which enable us to predict the accuracy levels of different combination of scanners and determine the optimum configuration of a multi-scanner detection system to achieve maximum accuracy. Malicious communication generated by malware also can reveal the presence of it. In the case of botnets, their command and control (C&C) communication is a good candidate for it. Among the widely used C&C protocols, HTTP is becoming the most preferred one. However, detecting HTTP-based C&C packets that constitute a minuscule portion of everyday HTTP traffic is a formidable task. In the final part of this dissertation, we present an anomaly detection based approach to detect HTTP-based C&C traffic using statistical features based on client generated HTTP request packets and DNS server generated response packets.

Abstract: For many complex physical and biological models, the computational cost of high-fidelity simulation codes precludes their direct use for Bayesian model calibration and uncertainty propagation. Furthermore, the models often have tens to thousands of inputs--comprised of parameters, initial conditions, or boundary conditions--many of which are unidentifiable in the sense that they cannot be uniquely determined using measured responses. In this presentation, we will discuss techniques to isolate influential inputs and employ surrogate models when computational budgets are limited. For input selection, we will discuss the use of global sensitivity analysis methods to isolate influential inputs and active subspace construction for linearly related parameters. We will also discuss the manner in which Bayesian calibration on active subspaces can be used to quantify uncertainties in physical parameters. These techniques will be illustrated for models arising in nuclear power plant design and HIV characterization and treatment. Biosketch: Ralph Smith received his PhD in Applied Mathematics from Montana State University in 1990. Following a three-year postdoctoral position at the Institute for Computer Applications in Science and Engineering (ICASE) at NASA Langley Research Center, he was an Assistant Professor in the Department of Mathematics at Iowa State University. He joined the North Carolina State University faculty in 1998, where he is presently a Distinguished Professor of Mathematics. He is Editor-in-Chief of the SIAM book series on Advances in Design and Control and is on the editorial boards of the SIAM/ASA Journal on Uncertainty Quantification and the Journal of Intelligent Material Systems and Structures. He is co-author of the research monograph Smart Material Structures: Modeling, Estimation and Control and author of the books Smart Material Systems: Model Development and Uncertainty Quantification: Theory, Implementation, and Applications. His research areas include mathematical modeling of smart material systems, numerical analysis and methods for physical systems, Bayesian model calibration, sensitivity analysis, control, and uncertainty quantification.

Google representatives will be hosting a presentation on opportunities at Google for interested students on Wednesday, October 5th at 5:00PM in Amoco Hall on the 1st floor of Swearingen.

RSVP: http://goo.gl/UTOrFZ Share your Experience: http://goo.gl/33Rn1D

Virtually every technology company has a patent portfolio. Whether they’re in the news for suing another company for patent infringement, being sued themselves (often by “patent trolls”), or selling their patent portfolio for billions of dollars, patents and technology are inextricably linked. Why is this the case, and what is a patent, anyway? If patents are so important to these companies, what should I know about patents prior to getting a job in computers? Does my CS or EE degree qualify me to get a job in the patent field? Come find out the answers to these questions, and ask your own. Stephen Shaw, who received a CS degree ‘03 and also has a law degree ‘97 from USC will present a colloquium Tuesday September 13 @5:45-6:45 in SWGN 2A31. Stephen currently lives in San Francisco and is working for a large firm there. This will be sponsored by the Software Engineering SIG of our student ACM chapter.

TecHealth: A South Carolina SmartState Center at USC Arnold School of Public Health Invites you to TECH TUESDAY TALKS Date: Tuesday, September 13 Time: Noon Place: Discovery 1, 915 Greene Street, Room 140* Speaker: Dirk den Ouden, Ph.D. Associate Professor, Dirk B. den Ouden is the Director of the Neurolinguistics Laboratory in the Communication Sciences and Disorders Department in the Arnold School of Public Health. In collaboration with Drs. Jijun Tang and Jeremiah Shepherd from the Dept. of Computer Engineering, the lab has developed a game-based computer application that allows speakers with aphasia to train speech output autonomously, making use of rhythmic cueing, instant feedback, and the benefits of overlearning to neural plasticity. Tech Tuesday Talks is a free, monthly seminar series bringing together researchers from across the USC campus who share interest in technology-assisted health promotion and disease prevention interventions and research. The series presents a forum to learn about one another’s work, spark collaborations, as well as to introduce students to the ongoing research conducted on the USC campus which incorporates technology in health promotion. All interested faculty, staff, students and the general public are invited to attend. Tech Tuesday Talks are presented on the second Tuesday of the month in Discovery I Building, Room 140 at noon. To learn more, visit http://techealth.sc.edu/tech-tuesday-talks or contact magradey@mailbox.sc.edu.

Speaker: Zhu Wang Affiliation: Mathematics, USC Location: SWGN 2A14 Time: 2:20 - 3:10 PM Abstract: In many scientific and engineering applications of complex fluid flows, computational efficiency is of paramount importance. However, because of the requisite of repeated numerical simulations in applications such as control, optimization, data assimilation, and uncertainty quantification, using the original system becomes prohibitive. Therefore, model reduction techniques have been frequently used by engineers and researchers. Among them, proper orthogonal decomposition is one of the most commonly used methods to generate reduced-order models for turbulent flows dominated by coherent structures. To achieve a balance between the low computational cost required by a reduced-order model and the complexity of the target turbulent flows, appropriate closure modeling strategies need to be employed. In this talk, we present reduced-order modeling strategies synthesizing ideas originating from proper orthogonal decomposition and large eddy simulation, develop rigorous error estimates and design efficient algorithms for the new reduced-order models. Bio: Dr. Wang is an assistant professor in the Department of Mathematics at University of South Carolina. He earned his PhD in Mathematics from Virginia Tech in 2012, and he was an industrial postdoc of the IMA at University of Minnesota, Twin Cities in 2012-2014. Dr. Wang research interests are scientific computing, numerical analysis, reduced-order modeling, climate modeling, large eddy simulation, and numerical solutions to PDEs.