Department of Computer Science and Electrical Engineering

Computer Science and Electrical Engineering
MS Thesis Defense

Multicast Routing with Byzantine Robustness

Debdatta Mukherjee

2:30-4:30 Tuesday, july 23, 2013, ITE 346

Network problems arise when nodes behave in arbitrary ways such as sending malformed messages, sending incorrect messages or not forwarding messages at all to other nodes in the network. These faults are called Byzantine failures. In a real network, these faults can be a result of hardware failure, cyber-attacks or network congestion. Due to the serious problems these faults can cause, it becomes important to make the network robust against them, so that the network continues to operate properly or degrades in an acceptable way in the presence of such faults. In this thesis, we propose methods that include multiple node disjoint path calculations and robust flooding to find byzantine-free multicast trees. By finding such trees, we can guarantee the delivery of the messages from a source to a particular multicast group.

Committee: Professors Deepinder Sidhu (chair), Kostas Kalpakis and Sergei Nirenburg

MS Defense
Computer Science and Electrical Engineering

Linked Data for Cybersecurity Vulnerability Descriptions

Arnav Joshi

9:00-11:00 Monday, 22 July 2013, 325b ITE

The Web is typically our first source of information about new software vulnerabilities, exploits and cyber-attacks. Information is found in semi-structured vulnerability databases as well as in text from security bulletins, news reports, cybersecurity blogs and Internet chat rooms. It can be useful to cybersecurity systems if there is a way to recognize and extract relevant information and represent it as easily shared and integrated semantic data. We describe such an automatic framework that generates and publishes a RDF linked data representation of cybersecurity concepts and vulnerability descriptions extracted from the National Vulnerability Database and other text sources. Entities, relations and concepts are represented using custom ontologies for the cybersecurity domain and also mapped to objects in the DBpedia knowledge base, producing a rich resource of machine-understandable linked data. The resulting cybersecurity linked data collection can be used for many purposes, including automating early vulnerability identification, mitigation and prevention efforts.

Committee: Professors Tim Finin (chair), Anupam Joshi and Tim Oates

First-year Cybersecurity MPS student Andrew Shiffer placed third in Microsoft's "Cybersecurity 2020" student essay contest.  The contest allows Microsoft to solicit original research about cybersecurity policy challenges from university students at any stage in their educational careers.  Andrew's paper is entitled "A Cybersecurity Triumvirate: Policies, Outcomes, and Emerging Trends."

Andrew will receive $2,000 prize (which he is applying toward his studies at UMBC) and the opportunity for his work to be published by Microsoft at a later date. According to a follow-up note from Microsoft, it appears that Andrew is the only American finalist — the first and second place students both came from Canadian universities.

Well done, Andrew!

Two UMBC students were among the 18 fellows selected by Code 2040 for a unique fellowship program that places high performing Black and Latino/a software engineering students in internships with top technology companies and startups in the San Francisco Bay area and supports them with mentorship, leadership training, and network development.

Perry Ogwuche, a rising senior majoring in computer science and mathematics, will spend the summer working at Redbeacon, a startup in Foster City, CA that connects qualified home-service professionals with exclusive job requests from homeowners. Perry is a member of the Phi Mu Epsilon National Mathematics Honorary Society, and has served as a Resident Assistant, Student Government Association member and as a math tutor to student athletes.

Randi Williams, a rising sophomore majoring in computer engineering, will spend her summer as an intern at Jawbone, a San Francisco company known for its noise eliminating Bluetooth headsets and portable speakers. Randi is a Meyerhoff scholar and Center for Women In Technology (CWIT) affiliate and coach in UMBC's Math Gym.

CODE2040 is a non-profit organization that aims to close the achievement, wealth, and skills gaps for Blacks and Latinos in the United States by creating access, awareness, and opportunities in technology and engineering. Its summer fellowship program brings high performing Black and Latino undergraduate and graduate coders and software engineering students to Silicon Valley for a comprehensive summer internship program that includes a paid internship with a top startup, mentoring, a speaker series, company visits, interactive workshops, executive coaching, and more. The program began last year with five students and has expanded to 18 this summer.

Students who are interested in the program can get more information on it and find out how to apply for the 2014 CODE 2040 fellows program at their web site.

More than twenty years ago, Tulay Adali stepped onto UMBC’s campus as an assistant professor right after receiving her PhD. Much has changed since then.

Now a professor of Computer Science and Electrical Engineering, Dr. Adali runs a highly active Machine Learning for Signal Processing Lab (MLSP­Lab). Her recent appointment as an IEEE Signal Processing Society Distinguished Lecturer has prompted invitations to speak around the world about her research in the theory and development of algorithms for signal processing. This March, Dr. Adali was awarded the University System of Maryland Regents’ Faculty Award for Scholarship, Research, or Creative Activity.

Her secret to success?

“Planning or thinking about the future is not something I do,” said Dr. Adali in her acceptance speech at the Presidential Faculty and Staff Ceremony where she was honored in March. “I rather make sure I enjoy what I do and have fun along the way.” Her technique seems to be paying off. For proof, just take a look at the recognition received by her research in two distinct areas: the development of powerful data­driven methods, and the analysis and fusion of medical imaging data. In 2008, Dr. Adali was elected a fellow of the American Institute for Medical and Biological Engineering (AIMBE). In 2009, the Institute of Electrical and Electronics Engineering (IEEE) elected her a fellow for her work on the theory and practice of statistical signal processing.

In 2011, a paper by Dr. Adali and colleagues titled “Complex ICA using nonlinear functions” received the 2010 IEEE Signal Processing Society Best Paper Award. The work develops a complete framework, allowing for the processing of complex data in a manner similar to the real­valued case, eliminating the need to make many of the simplifying assumptions commonly employed. The results of this NSF­funded study led to the development of a complete data­driven framework that enables joint use of sample dependence and higher­order­statistics.

Dr. Adali’s work in medical image analysis and fusion has also gained notoriety. She has been working on methods for data­driven analysis of medical imaging data, and for the analysis of functional magnetic resonance imaging (fMRI) data for understanding brain function. She and her colleagues discovered that fusing more than two modalities increases the sensitivity and specificity of the analyses of fMRI, electroencephalography (EEG) and structural MRI data. In March 2011, an IEEE Spectrum article mentioned her success in obtaining very high classification accuracy in identifying mental disorders in patients. Then in April 2011, in addition to her ongoing projects funded by the NSF, NIH, and the Mind Research Network, she received a grant from Michelin Research to study irregular wear detection in tires, where the new data-driven framework is applied to a completely new problem domain.

These notable research advances made Dr. Adali stand out as a nominee for this year’s Regents’ Faculty Award for Scholarship, Research, or Creative Activity. It is the highest honor given by the Board of Regents to faculty members, given to faculty members who have gone above and beyond the call of duty. This year, Dr. Adali joins only three other USM faculty members who were recognized for their exceptional research contributions. “Dr. Adali has been steadily building her research career and I am not surprised by the award since her research is remarkable,” says Dr. Carter, CSEE Department Chair. “I see her continuing to grow her research in areas of signal processing for medical applications and becoming a key UMBC faculty member

Ph.D. Dissertation Defense

On Prediction and Estimation for Datastreams

Utilizing Sparsity and Structure

Shiming Yang

10:00am-12:00pm, 6 June 2013, ITE 325b, UMBC

With the unprecedented fast growth of data, we have better opportunities to understand our complex world, and simultaneously face pervasive challenges in efficiently inferring the meaning behind these vast amounts of data. It is particularly important to explore the intrinsic structures in data to increase our rational understanding of the latent mechanisms that generate them. In modeling, structures are features used to characterize the underlying systems, such as the rank of a system, the number of clusters, the levels of hierarchy, and the order of spatio-temporal correlations in multiple measurements.

In this thesis, we present our research contributions on utilizing structures and sparsity in observed data to improve estimation and prediction of trajectories of system states for two systems: the highway traffic system and the human physiology systems. Both systems exhibit features that are seen in many other applications.

For the traffic problem, it is useful to know the near–term traffic conditions after the occurrence of some events which have noticeable impact on the road traffic. Often used macroscopic models, which view road traffic as fluid flowing in pipes, suffer from various inaccuracies, which could be mitigated by incorporating past observations to correct predictions. However, we often have limited observation and computing resources (e.g., probe vehicles, smartphones, bandwidth, sensors) to gather and process past observations. We describe a novel low-overhead strategy to adaptively select observation sites in real-time by using the density of the mesh of the numerical solution of the underlying mathematical model to capture the variability of that solution. We show that our proposed strategy improves the numerical accuracy of near–term traffic forecasting with limited observation resources as compared with with uniform deployment of the observation resources. In addition to deploying limited observation resources, one is often concerned with detecting special traffic events. To this end, we propose a novel method to decompose traffic observations into normal background and sparse events. Our method couples multiple traffic datastreams so that they share a certain sparse spatio–temporal structure.

We also study the utility of sparseness and structure in physiological datastreams. Missing values hinder the use of many machine learning methods. We show how to incorporate ideas from compressive sensing into handling the missing values problem in continuous intracranial pressure (ICP) datastreams from patients with traumatic brain injury. We experimentally evaluate the proposed method in experiments where randomly selected ICP values are marked as missing. We find our method gives estimated missing values that are in better agreement with the true values as compared with k–nearest neighbor and expectation maximization data imputation methods.

Moreover, predicting the near–term intracranial pressure for traumatic brain injury patients is of great importance to clinicians. Traditional regression methods, need an explicit parametric form of the model to fit. However, due to our limited knowledge of the complex brain physiology, it is difficult to specify an accurate parametric model. To overcome this difficulty, our model uses Gaussian processes to quantify our prior beliefs on the smoothness of the regression model, and performs regression in an infinite dimensional space. We show that the proposed Gaussian process regression model shows predicts ICP changes in clinically useful timeframes and may support future development of minimally-invasive ICP monitoring systems, earlier intervention strategies, and better patient outcomes.

Committee: Drs. K. Kalpakis (Chair), Alain Biem (IBM TJ Watson), Chein-I Chang, Colin MacKenzie, Dhananjay Phatak, Yaacov Yesha

MS Defense

Nimbus: Scalable, Distributed, In-Memory Data Storage

Adam Shook

1:30pm Thursday, 6 June 2013, 325b ITE, UMBC

The Apache Hadoop project provides a framework for reliable, scalable, distributed computing. The storage layer of Hadoop, called the Hadoop Distributed File System (HDFS), is an append-only distributed file system designed for commodity hardware. The append-only nature of the file system limits the ability for applications to have random reads and writes of data. This was addressed by Apache HBase and Apache Accumulo, which both allow for quick random access to a highly scalable key/value store.

However, these projects still require data to be read from the local disk of the server, and therefore cannot handle the type of I/O throughput that many applications require. This limits the potential for "hot" data sets that cannot be stored in memory of one machine, but do not need the scalability of HBase, i.e. the ones that can be sharded and stored in memory on dozens of machines. These data sets are often referenced by many applications and can be dozens of gigabytes in size.

Nimbus is a project designed for Hadoop to expose distributed in-memory data structures, backed by the reliability of HDFS. By executing a series of I/O benchmarks against HBase, Nimbus's architecture and implementation are validated by demonstrating the performance advantage over HBase, allowing for high-throughput data fetch operations. The overall architecture and design of each component are discussed to validate Nimbus's design goals, as well as a description of relevant use cases and future work for the project.

Committee: Drs. Tim Finin (chair), Anupam Joshi and Konstantinos Kalpakis

Ph.D. Dissertation Defense

Mid-Infrared Photonic Integration

Dingkai Guo

10:00am Tuesday, 4 June 2013, TRC CASPR conference room

The mid-Infrared (Mid-IR) wavelength range is important for applications including medical and security imaging, environmental trace gas sensing and free space communications. However, photonic integrated circuits (PICs) in the mid-IR range are completely under-developed which significantly slows the reduction of mid-IR system size, weight, and coupling losses and limits the development of highly functional mid-IR photonic modules with lower cost. In this dissertation, a solution to mid-IR photonic integration was demonstrated using a compact widely tunable mid-IR transmitter and a mid-IR amplifying photo-detector, which can be integrated with the mid-IR source.

This integrated widely tunable mid-IR source is fabricated by incorporating super structure grating (SSG) to the mid-IR quantum cascade laser (QCL) waveguide. The emission wavelength of the fabricated SSG-DBR QCL can be well controlled by varying the injection currents to the two grating sections. The wavelength can be tuned from 4.58μm to 4.77μm (90cm-1) with a supermode spacing of 30nm. This SSG-DBR QCL can be a compact replacement for the external cavity QCL used in current mid-IR sensors.

Mid-IR amplification and detection can be achieved using the same material as the mid-IR source. This QCL amplifier has an adjustable bandwidth and tunable gain peak, so it can function as a tunable mid-IR filter. By biasing the QCL just below its threshold, we demonstrated more than 11dB optical gain and over 28dB electrical gain at specified wavelengths. In the electrical gain measurement process, the resonant amplifier also functioned as a detector. This indicates that intersubband-based gain materials are ideal candidates for mid-IR photonic integrations.

Beside the optimized fabrication processes, new characterization technique based on the electrical derivative of the QCL I-V curves is used to quickly acquire the QCL threshold and leakage current, and explore the device carrier transport. The leakage currents present in different QCL waveguide structures are also studied and compared using this technique.

Finally, we report that the telecom wavelengths induced optical quenching effects on mid-IR QCLs when the QCLs are operated well above their threshold. The quenching effect is a result of intersubband bandbending and it depends on the coupled near-IR intensity, wavelength, and the QCL voltage bias. The quenching effects not only can be used for mid-IR QCL optical switching and modulation but also reveal that the mid-IR QCLs can function as “converters” to convert the telecom optical signal into the mid-IR optical signal at the near-IR fiber end.

A coherent mid-IR transceiver with both transmitting and receiving functions can be realized based on each integrated component introduced in this dissertation. This compact transceiver includes an integrated widely tunable mid-IR source, a mid-IR filter, amplifier, and detector based on the same material system.

Committee: Drs. Fow-Sen Choa (Chair), Anthony Johnson, Terrance Worchesky (Physics) , Li Yan, Gymama Slaughter

MS Thesis Defense

A Multilayer Framework to Catch Data Exfiltration

Puneet Sharma

10:30am Wednesday, 5 June 2013, 325b ITE, UMBC

Data exfilteration is the unauthorized leakage of confidential data from a particular system. It is a specific form of intrusion that is particularly hard to catch due to the most common cause: an insider entity who is responsible for the leak. That entity could be a person employed in the organization or a malicious hardware component bought from an unreliable third party. Catching such intrusions, therefore, can be extremely difficult. We describe a framework comprising multiple parameters that are constantly monitored in a system. These parameters can cover the entire stack of the computer architecture, from the hardware up to the application layer. Malicious behavior is detected by different modules monitoring these parameters and an aggregated attack alert is produced if multiple modules detect malicious activity within a short period of time. A more distributed and comprehensive monitoring framework should ensure that designing an attack becomes extremely difficult since an attack must go through multiple detectors present in the system without raising any alarms.

Committee: Drs. Anupam Joshi (chair), Tim Finin, Chintan Patel