If you are interested in cybersecurity, consider joining the CAE community.

UMBC is designated as a Center of Academic Excellence in Information Assurance/Cyber Defense Education and Research by the NSA and DHS. The CAE Community site is a good place for information, ideas and events for students, faculty and staff who are interested in information assurance and cybersecurity.

For example, a recent news post has information on an online event on Thursday 17 September 2015 that will include two talks: one from 1-2pm on Digital Investigation and the Trojan Defense and another from 2:15-3:15 on the NSA Codebreaker Challenge.

See the post for more information on the talks and how to particiate online.

UMBC Cyber Defense Laboratory
University of Maryland, Baltimore County

Enterprise and Higher Education Security

Mark Cather
Chief Information Security Officer
University of Maryland, Baltimore County

11:15am-12:30pm Friday 11 September 2015, ITE 231

Mark Cather will speak about the priorities and current challenges in securing a higher education environment and enterprises in general. Mr. Cather has been working for UMBC’s Department of Information Technology since he received his BS in computer science from UMBC in 1997. He assumed his current role as UMBC’s Chief Information Security Officer in 2014.

For more information, contact Prof. Alan Sherman, sherman at umbc.edu .

Computer Science and Electrical Engineering
University of Maryland, Baltimore County

Ph.D. Dissertation Defense

The Modeling Equation: Solving the Physically-Based
Modeling and Animation Problem with a Unified Solution

Yu Wang

12:00pm Friday, 28 August 2015, ITE 352

Physically-based modeling research in computer graphics is based largely on derivation or close approximation from physics laws defining the material behaviors. From rigid object dynamics, to various kinds of deformable objects, such as elastic, plastic, and viscous fluid flow, to their interaction, almost every natural phenomena can find a rich history in computer graphics research. Due to the nonlinear nature of almost all real world dynamics, the mathematical definition of their behavior is rarely linear. As a result, solving for the dynamics of these phenomena involves non-linear numerical solvers, which sometimes introduces numerical instability, such as volume gain or loss, slow convergence.

The contribution of this project is a unified particle-based model that implements an extended SPH solver for modeling fluid motion, integrated with rigid body deformation using shape matching. The model handles phase changes between solid and liquid, including melting and solidification, where material rigidity is treated as a function of time and particle distance to the object surface, and solid fluid coupling, where rigid body motion causes secondary fluid flow motion. Due to the stability of the fluid-rigid interplay solver, we can introduce artistic control to the framework, such as rigging, where object motion is predefined by either artistic control, or procedurally generated dynamics path. Interaction with the fluid can be indirectly achieved by rigging the rigid particles which implicitly handles rigid-fluid coupling. We used marching cubes to extract the surfaces of the objects, and applied the PN-triangles to replace the planar silhouettes with cubic approximations. We provide discussion on evaluation metrics for physically-based modeling algorithms. In addition, GPU solutions are designed for physics solvers, isosurface extraction and smoothing.

Committee: Drs. Marc Olano (CSEE; Advisor, Chair), Penny Rheingans (CSEE), Jian Chen (CSEE), Matthias Gobbert (Math), Lynn Sparling (Physics)

Ph.D. Proposal

ACCESS: An Assistive Contactless Capacitive
Electrostatic Sensing System

Alexander Nelson

12:00pm Friday, 21 August 2015, ITE 325b

The objective of ACCESS is to develop fabric capacitor sensor arrays as a holistic, wearable, touchless sensing solution. The fabric sensors are lightweight, flexible, and can therefore be integrated into items of everyday use. Further, the capacitive sensing hardware is low-power, unobtrusive, and easily maintainable. The research includes: the construction of fabric sensor prototypes and custom sensing hardware; the development of adaptive signal processing and gesture recognition; and the creation of an assistive cyber-physical interface for mobility impairment. The research is conducted with advisement from medical professionals and private consultants, and evaluated in clinical trials by individuals with upper-extremity mobility impairment. Proposed future work includes evaluation of the assistive device for computational overhead, the inclusion of personal contextual information in gesture recognition and device actuation, and investigation of a dense spatial-resolution capacitor sensor array as a low-resolution greyscale imaging system.

Committee: Drs. Nilanjan Banerjee and Ryan Robucci (Chairs), Chintan Patel, Sandy McCombe-Waller (UMB Medical School)

Ph.D. Proposal Defense

Data, Energy, and Privacy Management
Techniques for Sustainable Microgrids

Zhichuan Huang

11:00am Tuesday, 11 August 2015, ITE 325b

Sustainable microgrids have gained increasing attention recently, because they can provide the power supply to places i) where the traditional power grid does not exist due to the poor economy or limited number of residences (e.g., islands); and ii) when the traditional power grid is temporally not functioning due to severe weather conditions (e.g., storms). However, in order to achieve sustainability, there are a lot of challenges to be addressed. In this thesis, we propose to investigate three key techniques in sustainable microgrids. First, we investigate the big energy data management problem and present E-Sketch, a middleware for utility companies to gather data from smart meters with much less storage and communication overhead. E-Sketch utilizes adaptive sampling to compress power consumption changes in time domain. Then frequency compression is applied to further compress the sampled data.

The second key technique is the energy management in microgrids. Because energy generation and demand in each individual home and microgrid is not matching, the key challenge of the energy management is to model the existing energy demand and propose novel energy management to reduce the overall energy usage and cost in microgrids. In this technique, we study the theoretical, technical, and economic feasibility of sustainable microgrids. To enable distributed energy management, energy consumption data of different homes needs to be shared in the microgrid. Thus an important problem is how we guarantee that the shared data can only be used for energy management but not revealing the privacy of individual homes in the microgrid. To address this problem, we leverage the unique feature of hybrid AC-DC microgrids and propose the third technique — Shepherd, a privacy protection framework to effectively protect occupants’ privacy. In Shepherd, we provide a generic model for energy consumption hiding from different types of detection techniques.

Committee: Drs. Ting Zhu (chair), Nilanjan Banerjee, Chintan Patel, and David Irwin (UMass Amherst)

Ph.D. Proposal Defense

Holistic Home Energy Management:
From Sensing to Data Analytics

David Lachut

2:00pm Tuesday, 11 August 2015, ITE 325b

As home automation tools become more prevalent, they provide great potential to assist energy conservation and promote sustainable energy use in a way that increases users’ quality of life. This paper proposes the Greenhome System: a software system for using off-the-shelf home automation components and back-end data analytics to provide intelligent home energy management capabilities primarily targeted to renewable powered homes. The system will take input from various sensors and user input to detect user activities, predict home energy consumption, and make energy consumption recommendations to users. To accomplish the project goals, the Greenhome system requires in-home hardware and software components, a mobile component for user interaction, and a server component to tie them together. These components will accomplish tasks of data collection and analysis, activity and anomaly detection, prediction, planning, and recommendation.

This project builds on prior research in several areas, combining such diverse fields as predictive analytics, data visualization and annotation, planning, and recommender systems into a holistic approach. Combining these fields will result in new adaptations and make the overall Greenhome System a novel contribution. Work has begun on the Greenhome System preliminary to this proposal, with published work on residential sensor system design and implementation, data annotation collection, and energy demand prediction. It remains to incorporate automated self-maintenance, user activity detection, and personalized recommendations into a holistic system for home energy management.

Committee: Drs. Nilanjan Banerjee (chair), Ting Zhu, Charles Nicholas, Nirmalya Roy

Computer Science and Electrical Engineering
University Of Maryland, Baltimore County

M.S. Thesis Defense

Assessing Confidence in Relation Extraction Systems

Lianjie Sun

2:00ppm Thursday, 27 July 2015, ITE 325b, UMBC

In information extraction, a central and challenging task is extraction of relations. Systems that extract relations from text tend to be very productive, so it is important to quantify confidence or certainty in what is extracted. In this thesis we introduce a framework to assess confidence in relation extraction systems. We trained our system using a logistic regression model based on manually tagged sentences from the New York Times Annotated Corpora. Empirical results based on ROC curves show that our system performs better at computing confidence than previous systems such as Reverb. We conclude with a detailed analysis of the features used in our system and explain how these features might be tailored for use in other relation extraction systems.

Committee: Drs. Tim Oates (chair), Charles Nicholas and Matt Schmill

This talk was accepted for DEFCON 2015 in Las Vegas later this month. However, for those interested here in the UMBC community, John will conduct an informal preview of his talk on Friday 7/17 at 3:00PM in ITE 366 (DREAM Lab).

"Quantum" Classification of Malware
John Seymour, UMBC

3:00pm Friday 17 July 2015, ITE 366

Quantum computation has recently become an important area for security research, with its applications to factoring large numbers and secure communication. In practice, only one company (D-Wave) has claimed to create a quantum computer which can solve relatively hard problems, and that claim has been met with much skepticism. Regardless of whether it is using quantum effects for computation or not, the D-Wave architecture cannot run the standard quantum algorithms, such as Grover’s and Shor’s. The D-Wave architecture is instead purported to be useful for machine learning and for heuristically solving NP-Complete problems.

We'll show why the D-Wave and the machine learning problem for malware classification seem especially suited for each other. We also explain how to translate the classification problem for malicious executables into an optimization problem which a D-Wave machine can solve. Specifically, using a 512-qubit D-Wave Two processor, we show that a minimalist malware classifier, with cross-validation accuracy comparable to standard machine learning algorithms, can be created. However, even such a minimalist classifier incurs a surprising level of overhead.

John Seymour is a Ph.D. student at the University of Maryland, Baltimore County, where he performs research at the intersection of machine learning and information security. He's mostly interested in avoiding and helping others avoid some of the major pitfalls in machine learning, especially in dataset preparation (seriously, do people still use malware datasets from 1998?) In 2014, he completed his Master’s thesis on the subject of quantum computation applied to malware analysis. He currently works at CyberPoint International, a company which performs network and host-based machine learning, located in Baltimore, MD.

Ph.D. Dissertation Proposal

Real-time Spectral Rendering of Atmospheric Optical Phenomena

Ari Blenkhorn

2:00pm Wednesday, 10 June 2015, ITE 352

Glories, rainbows, and coronas are colorful atmospheric effects which occur when sunlight interacts with cloud droplets. Adding these effects to digital cloud environments will provide increased realism and a greater sense of immersion. Furthermore, these phenomena are the subject of active scientific research.  In both communities, high-resolution real-time rendering is desirable.

The color distribution of these phenomena is typically calculated using the Mie scattering theory, Debye series, or Airy theory. The calculations give the intensity of a single wavelength of light at a single scattering angle. They must be repeated for all desired wavelengths at all desired pixels of the final image.

I propose accelerating the calculations by using general-purpose GPU computing to transform a single-threaded, CPU-based Mie scattering application into a collection of highly-parallel GPU calculations.  I also propose to reduce the number of wavelengths required by using importance sampling, a monte-carlo selection method which concentrates the computing resources on the wavelengths belonging to the most important regions of the visible spectrum.

Planned work includes development of both numerical and perceptually-based image quality metrics, of interest to optical physicists and interactive application developers, respectively. These metrics will guide development of the GPU kernel parallel structure and the selection of a suitable estimator for importance sampling.

Committee: Drs. Marc Olano, Penny Rheingans, Curtis Menyuk, Matthias Gobbert (Mathematics), Raymond Lee (USNA)

Transforming big data into smart data:
deriving value via harnessing volume, variety
and velocity using semantics and semantic web

Professor Amit Sheth
Wright State University

11:00am Tuesday, 26 May 2015, ITE 325, UMBC

Big Data has captured a lot of interest in industry, with the emphasis on the challenges of the four Vs of Big Data: Volume, Variety, Velocity, and Veracity, and their applications to drive value for businesses. In this talk, I will describe Smart Data that is realized by extracting value from Big Data, to benefit not just large companies but each individual. If my child is an asthma patient, for all the data relevant to my child with the four V-challenges, what I care about is simply, "How is her current health, and what are the risk of having an asthma attack in her current situation (now and today), especially if that risk has changed?" As I will show, Smart Data that gives such personalized and actionable information will need to utilize multimodal data and their metadata, use domain specific knowledge, employ semantics and intelligent processing, and go beyond traditional reliance on Machine Learning and NLP. I will motivate the need for a synergistic combination of techniques similar to the close interworking of the top brain and the bottom brain in the cognitive models. I will present a couple of Smart Data applications in development at Kno.e.sis from the domains of personalized health, health informatics, social data for social good, energy, disaster response, and smart city.

Amit Sheth is an Educator, Researcher and Entrepreneur. He is the LexisNexis Ohio Eminent Scholar, an IEEE Fellow, and the executive director of Kno.e.sis – the Ohio Center of Excellence in Knowledge-enabled Computing a Wright State University. In World Wide Web (WWW), it is placed among the top ten universities in the world based on 10-year impact. Prof. Sheth is a well cited computer scientists (h-index = 87, >30,000 citations), and appears among top 1-3 authors in World Wide Web (Microsoft Academic Search). He has founded two companies, and several commercial products and deployed systems have resulted from his research. His students are exceptionally successful; ten out of 18 past PhD students have 1,000+ citations each.

Host: Yelena Yesha, yeyesha2umbc.edu