Department of Computer Science and Electrical Engineering
Security of Software Defined Networks, 11:15 Fri 10/7, UMBC
The UMBC Cyber Defense Lab presents
An Introduction to the Security of Software Defined Networks
CSEE Department, UMBC
11:15am-12:30pm, Friday, 7 October, UMBC, ITE 229
We introduce the concept of Software Defined Networks (SDNs) and the security challenges facing them. SDNs are a promising new network architecture that separates the data and control planes. By providing a central point of control and visibility over the network, SDNs allows a network to handle traffic with unprecedented flexibility, while simultaneously introducing potentially vulnerable lines of communication between a centralized controller and its constituent switches. To highlight the security challenges facing SDNs, we introduce and discuss several existing attacks. Anyone interested in networks and network security will want to know about the emerging trend of SDNs.
About the Speaker. Enis Golaszewski () is a first-semester PhD student and SFS scholar at UMBC working with Dr. Sherman on the security of software defined networks.
Host: Alan T. Sherman,
The UMBC Cyber Defense Lab meets biweekly Fridays
talk: Challenges of Implementing Personalized (Precision) Medicine, 11am Fri 10/7
Department of Information Systems
Challenges of Implementing Personalized (Precision) Medicine
Dr. Eddy Karnieli, Rambam Medical Center, Israel
11:00-12:00 Friday, 7 October 2016, ITE 459, UMBC
The concept of personalized (precision) medicine (PM) emphasizes the scientific and technological innovations that enable the physician to tailor disease prediction, diagnosis and treatment to the individual patient, based on a personalized data-driven approach. The major challenge for the medical systems is to translate the molecular and genomic advances into clinical available means.
For example, type 2 diabetes (T2D) is a major health problem. T2D is a very heterogeneous disease with at least three subgroups that exhibit distinct phenotypic and biological characteristics and susceptibilities to diabetes-related complications and comorbidities and specific genetic markers. Genomic analysis has already revealed about 80 gene loci variations. However, only few clinical and biochemical factors are taken into account in identifying diabetes type and advising therapy.
Among the main challenges to implementation of personalized (precision) medicine into current medical practice are knowledge gap of professionals; lack of approved and readily available genomic, phramcogenomics and other –omics tests; the lack of decision support systems integrated with EMRs clinical data, as well as updated genetic and molecular information at the clinical point of care and ethical and regulatory challenges. Currently, expensive cost of the new molecular targeting drugs like those used in treating cancers and rare genetic diseases patients results in major economic burden on the health system. Further, the question whether PM will reduce major causes of chronic morbidity and mortality still waits for an answer. During the lecture I will also outline programs currently being implemented to overcome these challenges.
Professor Eddy Karnieli is a graduate of the Rappaport Faculty of Medicine at the Technion– Israel Institute of Technology in Haifa.He obtained clinical training in Internal Medicine and Endocrinology at the Rambam Medical Center and did his Post-Doctoral Fellowship in Diabetes, Obesity and Endocrinology at the National Institutes of Health in Bethesda, Maryland. He was a visiting scholar at the University of California at San Diego and at the National Institutes of Health. He is currently the Director of the Institute of Endocrinology, Diabetes and Metabolism at the Rambam Medical Center. …
talk: Analytic approaches to study the chronnectome, 1pm Fri 10/7, ITE 229
UMBC CSEE Seminar Series
Analytic approaches to study the chronnectome
(time-varying brain connectivity)
Dr. Vince D. Calhoun
Executive Science Officer and Director, Image Analysis and MR Research
The Mind Research Network
Distinguished Professor, Electrical and Computer Engineering
The University of New Mexico
1:00-2:00pm Friday, 7 October 2016, ITE 229 UMBC
Recent years have witnessed a rapid growth in moving functional magnetic resonance imaging (fMRI) connectivity beyond simple scan-length averages into approaches that capture time-varying properties of connectivity. In this perspective we use the term “chronnectome” to describe such metrics that allow a dynamic view of coupling. We discuss the potential of these to improve characterization and understanding of brain function, which is inherently dynamic, not-well understood, and thus poorly suited to conventional scan-averaged connectivity measurements.
Prof. Vince Calhoun (EE Ph.D.’02, UMBC) is this year’s distinguished alum in the Engineering and Information Technology category and is the first speaker in our departmental seminar series.
Dr. Calhoun is currently Executive Science Officer at the Mind Research Network and a Distinguished Professor in the Department of Electrical and Computer Engineering at the University of New Mexico. He is the author of more than 450 full journal articles and over 550 technical reports, abstracts and conference proceedings. His work includes the development of flexible methods to analyze functional magnetic resonance imaging such as independent component analysis (ICA), data fusion of multimodal imaging and genetics data, and the identification of biomarkers for disease. Among other things, he leads an NIH P20 COBRE center grant on multimodal imaging of schizophrenia, bipolar disorder, and major depression as well as an NSF EPSCoR grant focused on brain imaging and epigenetics of adolescent development. Dr. Calhoun is a fellow of the Institute of Electrical and Electronic Engineers (IEEE), The Association for the Advancement of Science, The American Institute of Biomedical and Medical Engineers, The American College of Neuropsychopharmacology, and the International Society of Magnetic Resonance in Medicine. He is currently chair of the IEEE Machine Learning for Signal Processing (MLSP) technical committee.
About the CSEE Seminar Series: The UMBC Department of Computer Science and Electrical Engineering presents technical talks on current significant research projects of broad interest to the Department and the research community. Each talk is free and open to the public. We welcome your feedback and suggestions for future talks.
talk: Categorizing Misconceptions of Cybersecurity Reasoning, 11:15 Fri 9/9
The UMBC Cyber Defense Lab presents
Categorizing Misconceptions of Cybersecurity Reasoning
Computer Science and Electronical Engineering
University of Maryland, Baltimore County
11:15am-12:30pm Friday, 9 September 2016, ITE 237
We present preliminary analysis of student responses to cybersecurity interview prompts.
During spring 2016, we interviewed twenty-six students at three diverse colleges and universities (UMBC, Prince George’s Community College, Bowie State University) to understand how they reason about cybersecurity. Each interview lasted approximately one hour during which we asked the subject to solve four cybersecurity problems. For this purpose, we developed twelve engaging interview prompts organized in three protocols each comprising four prompts. Using a paired expert-novice methodology, we are analyzing transcriptions of the interviews produced from audio and video recordings. The twelve prompts focused on five difficult and important concepts identified from a Delphi method with thirty-six experts, which we carried out in fall 2014.
Preliminary analysis of student responses reveals common misconceptions and problematic reasoning, including conflating concepts, biased reasoning, unsound logic, and factual errors. For example, students commonly conflate authentication and authorization, as well as encryption and hashing. Examples of biased reasoning include seeing the situation only from the user’s perspective, placing inappropriately high trust in physical objects, and underestimating potential vulnerabilities from insider threats. Initially, we marked student statements as “correct’’ or “incorrect.’’ From the “incorrect’’ responses, we identified misconceptions about cybersecurity. Then, we categorized why responses were incorrect and we identified a variety of biases and problematic reasonings.
Our motivation is to produce educational assessment tools that will measure how well students understand cybersecurity concepts, for the purpose of identifying effective ways to teach cybersecurity. Results of our work will also be useful in developing curricula, learning exercises, and other educational materials and policies.
Joint work with David Delatte, Geoffrey Herman, Michael Neary, Linda Oliva, Alan Sherman, and Julia Thompson. Support for this research is provided in part by the U.S. Department of Defense under CAE-R grants H98230-15-1-0294 and H98230-15-1-0273, and by the National Science Foundation under SFS grant 1241576. We will present our results at the Frontiers in Education Conference, October 12-15, 2016, in Erie, PA.
About the Speaker:Travis Scheponik is a PhD student in computer science at UMBC, working with Dr. Alan T. Sherman. His research interests include cybersecurity education.
Host: Alan T. Sherman,
talk: Cognitive Computing & Visualization at IBM Research/RPI, 10am Thur 5/19, UMBC
Cognitive Computing and Visualization at IBM Research/RPI CISL
Dr. Hui Su, IBM Research
10:00-11:00am, Thursday, 19 May 2016, ITE 325b
Dr. Hui Su will talk about Cognitive and Immersive Systems Lab, a research initiative to develop the new frontier of immersive cognitive systems that explore and advance natural, collaborative problem-solving among groups of humans and machines. This lab is a collaboration between IBM Research and Rensselaer Polytechnic Institute. Dr. Su will talk about why the research for human computer interaction is extended to build a symbiotic relationship between human beings and smart machines, what research is going to be important to build immersive cognitive systems in order to transform the way professionals work in the future.
Dr. Hui Su is the Director of Cognitive and Immersive Systems Lab, a collaboration between IBM Research and Rensselaer Polytechnic Institute. He has been a technical leader and an executive at IBM Research. Most recently, he was the Director of IBM Cambridge Research Lab in Cambridge, MA and was responsible for a broad scope of global missions in IBM Research, including Cognitive User Experience, Center for Innovation in Visual Analytics and Center for Social Business. As a technical leader and a researcher for 19 years at IBM Research, Dr. Su has been an expert in multiple areas ranging from Human Computer Interaction, Cloud Computing, Visual Analytics, and Neural Network Algorithms for Image Recognition etc. As an executive, he has been leading research labs and research teams in the US and China. He is passionate about game-changing ideas and fundamental research, passionate in speeding up the impact generation process for technical innovations, discovering and developing new linkages between innovative research work and business needs.
Social media predictive analytics bring unique opportunities to study people and their behaviors in real time, at an unprecedented scale: who they are, what they like and what they think and feel. Such large-scale real-time social media predictive analytics provide a novel set of conditions for the construction of predictive models. This talk focuses on various approaches to handling this dynamic data for predicting latent user demographics, from constrained-resource batch classification, to incremental bootstrapping, and then iterative learning via interactive rationale (feature) crowdsourcing. In addition, we present the relationships between a variety of perceived user properties e.g., income, education etc. and opinions, emotions and interests in a social network.
Svitlana Volkova received her PhD in Computer Science from Johns Hopkins University. She was affiliated with the Center for Language and Speech Processing and the Human Language Technology Center of Excellence. Her PhD research focused on building predictive models for sociolinguistic content analysis in social media. She built online models for streaming social media analytics, fine-grained emotion detection and multilingual sentiment analysis, and effective annotation techniques via crowdsourcing incorporated into the active learning framework. She interned at Microsoft Research in 2011, 2012 and 2014 at the Natural Language Processing and Machine Learning and Perception teams. She was awarded the Google Anita Borg Memorial Scholarship in 2010 and the Fulbright Scholarship in 2008.
talk: Topic Modeling for Analyzing Document Collection, 11am Mon 3/16
CHMPR Lecture Series
Topic Modeling for Analyzing Document Collection
Department of Computer Science, University of Miami
11:00am Monday, 16 May 2016, ITE 325b, UMBC
Topic modeling (in particular, Latent Dirichlet Analysis) is a technique for analyzing a large collection of documents. In topic modeling we view each document as a frequency vector over a vocabulary and each topic as a static distribution over the vocabulary. Given a desired number, K, of document classes, a topic modeling algorithm attempts to estimate concurrently K static distributions and for each document how much each K class contributes. Mathematically, this is the problem of approximating the matrix generated by stacking the frequency vectors into the product of two non-negative matrices, where both the column dimension of the first matrix and the row dimension of the second matrix are equal to K. Topic modeling is gaining popularity recently, for analyzing large collections of documents.
In this talk I will present some examples of applying topic modeling: (1) a small sentiment analysis of a small collection of short patient surveys, (2) exploratory content analysis of a large collection of letters, (3) document classification based upon topics and other linguistic features, and (4) exploratory analysis of a large collection of literally works. I will speak not only the exact topic modeling steps but also all the preprocessing steps for preparing the documents for topic modeling.
Mitsunori Ogihara is a Professor of Computer Science at the University of Miami, Coral Gables, Florida. There he directs the Data Mining Group in the Center for Computational Science, a university-wide organization for providing resources and consultation for large-scale computation. He has published three books and approximately 190 papers in conferences and journals. He is on the editorial board for Theory of Computing Systems and International Journal of Foundations of Computer Science. Ogihara received a Ph.D. in Information Sciences from Tokyo Institute of Technology in 1993 and was a tenure-track/tenured faculty member in the Department of Computer Science at the University of Rochester from 1994 to 2007.
talk: Human mental models and robots: Grasping and tele-presence, 11am 5/9
Human mental models and robots:
Grasping and tele-presence
Dr. Cindy Grimm, Oregon State University
11:00-12:00 Monday 9 May 2016, ITE 325b
In this talk I will cover two separate research efforts in robotics, both of which use human mental models to improve robotic functionality. Robots struggle to pick up and manipulate physical objects, yet humans do this with ease – but can’t tell you how they do it. In this research we focus on how to capture human data in such a way as to gain insight into how people structure the grasping task. Specifically, we look at the role of perceptual cues in evaluating grasps and mental classification models of grasps (i.e., all these grasps are the “same”). In the second half of the talk I will switch to discussing how human mental models of privacy, trust, and presence come in to play in remote tele-presence applications (“Skype-on-a-movable-stick”).
Dr. Cindy Grimm is currently an associate professor at Oregon State University (since 2013) in the School of Mechanical, Industrial, and Manufacturing Engineering (application area robotics). Prior to that she was tenured faculty at Washington University in St. Louis in Computer Science (12 years). Her research areas range from 3D sketching to biological modeling to human-robot interaction. She approaches these problems with a combination of mathematical models and empirically-verified human-centered design (HCD). Mathematical models provide a sound, quantitative, rigorous, elegant basis for representing shape and function, and are a core part of the “language” of computation. Including a human in the loop is a key component of the application areas she works in; HCD provides the mechanism for addressing the fundamental problem of how to make mathematical computation “useful” for humans. She has worked with collaborators in fields ranging from psychology, mechanical and biological engineering, statistics, to art.
talk: Statistical Testing of Hash Bit Sequences, 11:15am Fri May 6, UMBC
The UMBC Cyber Defense Lab presents
Statistical Testing of Hash Bit Sequences
11:15am-12:30pm Friday, 6 May 2016, ITE 237
We tested bit sequences generated from the MD5 hash function using multinomial distribution and close-point spatial statistical tests for randomness. We found that bit sequences generated from truncated-round MD5 hash fail these tests for high- and low-density input choices.
In 2000, the National Institute of Standards and Technology concluded a competition to select the Advanced Encryption Standard. One of the requirements for candidates was randomness of output bits. The techniques used to evaluate symmetric block cipher randomness have not been extensively applied to hash functions.
In this study, we adapt a subset of the techniques used to analyze the randomness of AES candidate algorithms to study the randomness of the well-known MD5 hash function. Our approach uses high-density, lo- density, and chained-input methods to generate MD5 hashes. We concatenate these hash outputs and subjected them to multinomial distribution and close-point spatial tests. We iterated this approach over reduced-round versions of MD5. Our presentation includes specifications for the input methods, details on the statistical tests, and analysis of the statistical results.
Through statistical testing of concatenated MD5 hashes, we derive results that demonstrate a link between the performance of the concatenated hash bit sequences in our statistical testing and the number of hash rounds applied to the high-density and low-density input methods. Randomness is a desirable property for cryptographic hash functions. We present a new approach that facilitates the analysis and interpretation of hash functions for statistical randomness.
About the Speaker. Enis Golaszewski is a prospective PhD student in CS at UMBC, working with Dr. Alan T. Sherman. His research interests include the security of software-defined networks. He graduated from UMBC in CS in December 2015 and was a student in the fall 2015 INSuRE class. Email: <>
Host: Alan T. Sherman,
tutorial: Design, Analysis and Security of Automotive Networks, 2pm 4/29
Design, Analysis and Security of Automotive Networks
University of Maryland, Baltimore County
2:00-3:30pm Friday, 29 April 2016, ITE 325b
As more electronic and wireless technologies permeate modern vehicles, understanding the design of an embedded automotive network becomes necessary to protect drivers from external agents with a malicious intent to disrupt onboard electronics. By analyzing the different types of automotive networks and current security issues that the industry faces, we will learn how intruders are able to access an automotive network, read data that streams from the connected nodes and inject potentially malicious messages. This presentation will cover the electrical design of automotive networks, the communication protocols between electronic control units, methods for analyzing network messages and a detailed overview of previous automotive attacks and current security issues.
Sekar Kulandaivel is a Meyerhoff Scholar and Computer Engineering undergraduate student at UMBC. He currently works on designing an intrusion detection system for automotive networks with Dr. Nilanjan Banerjee of the UMBC Eclipse Cluster. Sekar has had previous internships at MIT Lincoln Laboratory, Northrop Grumman Corporation and Johns Hopkins University. He will attend Carnegie Mellon University in Fall 2016 to pursue a PhD in Electrical and Computer Engineering with a focus in electric vehicle security.