THESIS DEFENSE Department of Computer Science and Engineering University of South Carolina Author : Adel D. Rajab Advisor : Dr. Chin-Tser Huang Date : Nov 10th 2017 Time : 2:00 pm Place : Meeting room 2265, Innovation Center Abstract The Optical Bust Switching (OBS) network has become one of the most promising switching technologies for building the next-generation Internet backbone infrastructure. However, OBS networks still face a number of security and Quality of Service (QoS) challenges, particularly from Burst Header Packet (BHP) flooding attacks. First, we proposed and developed a new security model that can be embedded into OBS core switch architecture to prevent BHP flooding attacks. The countermeasure security model allows the OBS core switch to classify the ingress nodes based on their behavior and the amount of reserved resources not being utilized. A malicious node causing a BHP flooding attack will be blocked by the developed model until the risk disappears or the malicious node redeems itself. Using our security model, we can effectively and preemptively prevent a BHP flooding attack regardless of the strength of the attacker. In the second part of this dissertation, we investigated the potential use of machine learning (ML) in countering the risk of the BHP flood attack problem. In particular, we proposed and developed a new series of rules, using the decision tree method to prevent the risk of a BHP flooding attack. The proposed classification rule models were evaluated using different metrics to measure the overall performance of this approach. The experiments showed that using rules derived from the decision trees did indeed counter BHP flooding attacks, and enabled the automatic classification of edge nodes at an early stage. In the third part of this dissertation, we performed a comparative study, evaluating a number of ML techniques in classifying edge nodes, to determine the most suitable ML method to prevent this type of attack. The experimental results from a preprocessed dataset related to BHP flooding attacks showed that rule-based classifiers, in particular decision trees (C4.5), Bagging, and RIDOR, consistently derive classifiers that are more predictive, compared to alternate ML algorithms, including AdaBoost, Logistic Regression, Naive Bayes, SVM-SMO and ANN-MultilayerPerceptron. Moreover, the harmonic mean, recall and precision results of the rule-based and tree classifiers were more competitive than those of the remaining ML algorithms. Lastly, the runtime results in ms showed that decision tree classifiers are not only more predictive, but are also more efficient than other algorithms. Thus, our findings show that decision tree identifier is the most appropriate technique for classifying ingress nodes to combat the BHP flooding attack problem.

THESIS DEFENSE Department of Computer Science and Engineering University of South Carolina Author : Tian Jing Advisor : Dr. Wenyuan Xu Date : Nov 6th 2017 Time : 12:00 pm Place : Meeting room 2265, Innovation Center. Abstract Authentication plays a key role in securing various resources including corporate facilities or electronic assets. As the most used authentication scheme, knowledge-based authentication is easy to use but its security is bounded by how much a user can remember. Biometrics-based authentication requires no memorization but ‘resetting’ a biometric password may not always be possible. Thus, we propose study several behavioral biometrics (i.e., mid-air gestures) for authentication which does not have the same privacy or availability concerns as of physiological biometrics. In this dissertation, we first propose a user-friendly authentication system KinWrite that allows users to choose arbitrary, short and easy-to-memorize passwords while providing resilience to password cracking and password theft. Specifically, we let users write their passwords (i.e., signatures in the 3D space), and verify a user’s identity with similarities between the user’s password and enrolled password templates. Dynamic time warping distance is used for similarity calculation between 3D passwords samples. In the second part of the dissertation, we design an authentication scheme that does not depend on the handwriting contents, i.e., regardless of the written words or symbols, and adapt challenge-response mechanism to avoid possible eavesdropping, man-in-the-middle attacks, and reply attacks. We design a MoCRA system that utilizes Leap Motion to capture users’ writing movements and use writing style to verify users, even if what they write during the verification is completely different from what they write during the enrollment. Specifically, MoCRA leverages co-occurrence matrices to model the handwriting styles, and use a Support Vector Machine (SVM) to accept a legitimate user and reject the rest. In the third part, we study both security and usability performance on multiple types of mid-air gestures that used as passwords, including writing signatures in the air. We objectively quantify the usability performance by metrics related to the enroll time and the complexity of the gestures, and evaluate the security performance by the authentication performance. In addition, we subjectively evaluate the gestures by survey responses from both field subjects who participated in gesture experiments and on-line subjects who watched a short video on gesture introducing. Finally, we study the consistency of gestures over samples collected in a two-month period, and evaluate their security under shoulder surfing attacks.

I would like to invite you to attend this week's CSCE 791 seminar. These seminars highlight research being performed in our department and across the world. All CSCE 791 seminars are open to anybody who wishes to attend - not just students registered for the course. Speaker: Bryant Walker Smith, University of South Carolina (School of Law) Abstract: This discussion will explore the technologies, applications, and legal aspects of automated driving. Bio: Bryant Walker Smith is an assistant professor in the School of Law and (by courtesy) in the School of Engineering at the University of South Carolina. He is also an affiliate scholar at the Center for Internet and Society at Stanford Law School, chair of the Emerging Technology Law Committee of the Transportation Research Board of the National Academies, and a member of the New York Bar. Bryant's research focuses on risk (particularly tort law and product liability), technology (automation and connectivity), and mobility (safety and regulation). As an internationally recognized expert on the law of self-driving vehicles, Bryant taught the first-ever course on this topic and is regularly consulted by government, industry, and media. His recent article, Proximity-Driven Liability, argues that commercial sellers' growing information about, access to, and control over their products, product users, and product uses could significantly expand their point-of-sale and post-sale obligations toward people endangered by those products. Before joining the University of South Carolina, Bryant led the legal aspects of automated driving program at Stanford University, clerked for the Hon. Evan J. Wallach at the United States Court of International Trade, and worked as a fellow at the European Bank for Reconstruction and Development. He holds both an LL.M. in International Legal Studies and a J.D. (cum laude) from New York University School of Law and a B.S. in civil engineering from the University of Wisconsin. Prior to his legal career, Bryant worked as a transportation engineer.

Duncan Buell The talk will focus on two main topics:

• The security of elections in a networked world, where complicated computer hardware is used in the stressful circumstances of a major election, and the ability (or inability) of verifying that voter registration databases and election results have not been corrupted by malware or hacking.
• The post-election evidence based analysis and auditing of the results to provide increased confidence that the certified election results are in fact correct.

Bio: Dr. Buell is the NCR Professor of Computer Science and Engineering at USC and has been working for more than ten years on issues of election integrity, computer security for elections, and the post-election analysis of the data from ES&S iVotronic voting computers (used in South Carolina and several other states). He has since 2010 had programs to analyze the South Carolina election data to verify correctness of the results and adherence to the official protocol for conducting elections and returning the data to county headquarters for tallying. He has testified as an expert witness in open records cases and been involved in election integrity litigation in Arizona, Colorado, Georgia, North Carolina, Pennsylvania, and Texas.

I would like to invite you to attend this week's CSCE 791 seminar. These seminars highlight research being performed in our department and across the world. All CSCE 791 seminars are open to anybody who wishes to attend - not just students registered for the course. Speaker: Dr. Yonghong Yan, University of South Carolina Abstract: Today’s computer systems are becoming much more heterogeneous and complex from both computer architecture and memory system. High performance computing systems and large-scale enterprise clusters are often built with the combination of multiple architectures including multicore CPUs, Nvidia manycore GPUs, Intel Xeon Phi vector manycores, and domain-specific processing units, such as DSP and deep-learning tensor units. The introduction of non-volatile memory and 3D-stack DRAM known as high-bandwidth memory further complicated computer systems by significantly increasing the complexity of the memory hierarchy. For users, parallel programming for those systems has thus become much more challenging than ever. In this talk, the speaker will highlight the latest development of parallel programming models for the existing and emerging architectures for high performance computing. He will introduce the ongoing work in his research team (http://passlab.github.io) for improving productivity and portability of parallel programming for heterogeneous systems with the combination of shared and discrete memory. The speaker will conclude that this is an exciting time for performing computer system research and also share some of his unsuccessful experiences for studying his Ph.D. Bio: Dr. Yonghong Yan joined University of South Carolina as an Assistant Professor in Fall 2017 and he is a member of OpenMP Architectural Review Board and OpenMP Language Committee. Dr. Yan calls himself a nerd for parallel computing, compiler technology and high-performance computer architecture and systems. He is an NSF CAREER awardee. His research team develop intra-/inter-node programming models, compiler, runtime systems and performance tools based on OpenMP, MPI and LLVM compiler, explore conventional and advanced computer architectures including CPU, vector, GPU, MIC, FPGA, and dataflow system, and support applications ranging from classical HPC, to big data analysis and machine learning, and to computer imaging. The ongoing development can be found from https://github.com/passlab. Dr. Yan received his PhD degree in computer science from University of Houston and has a bachelor degree in mechanical engineering

I would like to invite you to attend this week's CSCE 791 seminar. These seminars highlight research being performed in our department and across the world. All CSCE 791 seminars are open to anybody who wishes to attend - not just students registered for the course. Speaker: Dr. Jason O'Kane, University of South Carolina Abstract: The design of an effective autonomous robot relies upon a complex web of interactions and tradeoffs between various hardware and software components. The problem of designing such a robot becomes even more challenging when the objective is to find robot designs that are minimal, in the sense of utilizing only limited sensing, actuation, or computational resources. The usual approach to navigating these tradeoffs is currently by careful analysis and human cleverness. In contrast, this talk will present some recent research that seeks to automate some parts of this process, by representing models for a robot's interaction with the world as formal, algorithmically-manipulable objects, and posing various kinds of questions on those data structures. The results include both both bad news (i.e., hardness results) and good news (practical algorithms). Bio: Jason O'Kane is Associate Professor in Computer Science and Engineering and Director of the Center for Computational Robotics at the University of South Carolina. He holds the Ph.D. (2007) and M.S. (2005) degrees from the University of Illinois at Urbana-Champaign and the B.S. (2001) degree from Taylor University, all in Computer Science. He has won a CAREER Award from NSF, a Breakthrough Star Award from the University of South Carolina, and the Outstanding Graduate in Computer Science Award from Taylor University. He was a member of the DARPA Computer Science Study Group. His research spans algorithmic robotics, planning under uncertainty, and computational geometry.

I would like to invite you to attend this week's CSCE 791 seminar. These seminars highlight research being performed in our department and across the world. All CSCE 791 seminars are open to anybody who wishes to attend - not just students registered for the course. Speaker: Dr. Jijun Tang, University of South Carolina Abstract: Hi-C technology is one of the most popular tools for measuring the spatial organization of mammalian genomes. Although an increasing number of Hi-C datasets have been generated in a variety of tissue/cell types, due to high sequencing cost, the resolution of most Hi-C datasets are coarse and cannot be used to infer enhancer-promoter interactions or link disease-related non-coding variants to their target genes. To address this challenge, we develop HiCPlus, a computational approach based on deep convolutional neural network, to infer high-resolution Hi-C interaction matrices from low-resolution Hi-C data. Through extensive testing, we demonstrate that HiCPlus can impute interaction matrices highly similar to the original ones, while using only as few as 1/16 of the total sequencing reads. We observe that Hi-C interaction matrix contains unique local features that are consistent across different cell types, and such features can be effectively captured by the deep learning framework. We further apply HiCPlus to enhance and expand the usability of Hi-C data sets in a variety of tissue and cell types. In summary, our work not only provides a framework to generate high-resolution Hi-C matrix with a fraction of the sequencing cost, but also reveals features underlying the formation of 3D chromatin interactions.

Speaker: Dr. Matthew Brashears, University of South Carolina (Department of Sociology) Abstract: Humans make mistakes but diffusion through social networks is typically modeled as though they do not. We find in an experiment that efforts to correct mistakes are effective, but generate more mutant forms of the contagion than would result from a lack of correction. This indicates that the ability of messages to cross “small-world” human social networks may be overestimated and that failed error corrections create new versions of a contagion that diffuse in competition with the original. These results are extended to a nascent general theory of replicators explaining how error correction mechanisms facilitate rapid saturation of a search space. A simulation model and preliminary results are presented that are consistent with this prediction. Bio: Matthew E. Brashears is an Associate Professor of Sociology at the University of South Carolina. His work crosses levels, integrating ideas from evolutionary theory, social networks, organizational theory, and neuroscience. His current research focuses on linking cognition to social network structure, studying the effects of error and error correction on diffusion dynamics, and using ecological models to connect individual behavior to collective dynamics. He is also engaged in an effort to model values and interactional scripts in an ecological space using cross-national data, with the goal of generating a predictive model of cultural competition and evolution. His work has appeared or is forthcoming in Nature Scientific Reports, the American Sociological Review, the American Journal of Sociology, Social Networks, Social Forces, Advances in Group Processes and Frontiers in Cognitive Psychology, among others. He has received grants from the National Science Foundation, the Defense Threat Reduction Agency, the Army Research Institute, the Army Research Office, and the Office of Naval Research. He is one of two new co-editors for the journal Social Psychology Quarterly, and currently serves as an officer in the American Sociological Association’s Social Psychology Section.

The Science, Engineering and Technology (S.E.T.) Fair is the job fair that employers attend to recruit students for technical internships, co-ops and full-time positions. For more information about the services that are provided at the Career Center, visit www.sc.edu/career.