Department of Computer Science and Electrical Engineering

MS Thesis Defense

Extracting cybersecurity related entities,
terms and concepts from text

Ravendar Lal

10:30am Tuesday, 28 May 2013, ITE325b, UMBC

Securing computers, data, cyber-physical systems and networks is a growing problem as society's dependence on them increases while they remain vulnerable to attacks by both criminals and rival nation states. Creating 'situationally aware' computer systems that defend against new "zero day" software vulnerabilities requires them to automatically integrate and use new security-related data from a wide variety of sources. One important source is information found in text from security bulletins, vulnerability databases, news reports, cybersecurity blogs and Internet chat rooms.

We describe an information extraction framework to extract cybersecurity-relevant entities, terms and concepts from text. We use a Conditional Random Field based model trained on manually annotated data to identify and extract the relevant terms. These are then mapped to a previously developed OWL ontology and represented as RDF linked data. We evaluated the system's performance by comparing its results on test data from the National Vulnerability Database and security bulletins from Microsoft and Adobe.

Committee: Drs. Tim Finin (Advisor), Anupam Joshi, Tim Oates

Five students in UMBC's College of Engineering and Information Technology have been selected for major scholarships to pursue studies in cybersecurity-related fields under UMBC's participation in the National Science Foundation's Scholarship for Service (SFS) Federal CyberCorps program. As SFS Scholars, students receive full tuition, fees, annual reimbursement of professional development expenses ($3,000), a nine-month stipend ($20,000 for undergraduates, $25,000 for MS/MPS students, and $30,000 for PhD students) for up to two years (three years for PhD), and assistance with federal cybersecurity internships and career placement.

The awardees for AY13-14 are:

Nathan Price, MS (CMPE)
Punlada Muangrat, BS (IS)
Shannon Mcpherson, MPS (CYBR)
Alex Cooke, BS (IS)
Denis Danilin, MS (IS)

These new awardees will join existing UMBC SFS Scholars Oliver Kubik (BS, CMSC), Mary Mathews (PhD, CMSC), Brendan Masiar (MPS, CYBR) and Brandyn Schult (MPS, CYBR).

The CyberCorps program produces highly-qualified professionals to meet the United States government's increasing need to protect American's cyber infrastructure. While in the program at UMBC, SFS CyberCorps Scholars participate in special SFS program activities, have opportunities to engage in mentored research opportunities both at UMBC and its partners from industry and government, and must complete a paid summer internship for the federal government. Upon graduation, each student must work for the government (for pay) for one year for each year of scholarship received. Drs. Alan Sherman and Richard Forno direct the program under a five-year $2.5 million NSF grant received in 2012.

NOTE:  We expect to have additional SFS slots coming available in January 2014.  Applications for SFS support beginning in the Spring 2014 semester will be accepted through 12PM on Friday, 15 November 2013. All levels of study are welcome to apply — however, students must be admitted to (or already enrolled at) UMBC first before applying for SFS.

For information about SFS (including application information and instructions) and other US government-related cybersecurity scholarships available, please visit cisa.umbc.edu.

You don’t know until you try.

This is what the leaders behind Computing Education for the 21^st Century (CE21-Maryland) have figured out. Drs. Marie desJardins, Penny Rheingans, and Susan Martin say that removing the mystery and misconceptions surrounding Computer Science is the key to getting more women and minorities to purse careers in the field.  

Historically, Computer Science has been dominated by white males. Just take a look at the numbers. In 2010-11 only 12.7% of computing degrees went to women. A mere 4.6% of Computer Science graduates were African Americans and 6.5% were Hispanics.

It’s not that women and minorities are no good at Computer Science.  It’s that they often think they’re no good. When you walk into a classroom and no one looks like you, it’s easy to feel like you don’t belong.

Dr. Marie desJardins, a CSEE professor, gives one part of the explanation for this trend. “Youth gaming culture is really dominating Computer Science majors,” she says. Boys who love video games naturally gravitate to Computer Science in college. Women aren’t typically avid gamers. Instead, they tend to gravitate towards social careers–not something they associate with the field of Computer Science.

Herein lies the problem: most of us don’t know what computer scientists actually do. “There are some iconic careers that we understand,” says CSEE Professor Penny Rheingans. “We know what doctors do. We know what lawyers do. But, nobody knows what Computer Scientists do.”

Only by experiencing the discipline firsthand will kids understand if the field is for them. Take Dr. Rheingans, who was headed down the path to becoming a lawyer when she took a Computer Science class: “And I fell in love,” she says. It was hard and sometimes frustrating, but that challenge got her hooked. “Not enough students have the opportunity to experience that.”

Even if women and minorities want to take Computer Science, they don’t always have the opportunity in high school. Throughout Maryland, inconsistent curriculum and spotty availability has made it hard for students to be introduced to the subject.  

Computer Science is not a graduation requirement, nor is it even offered at every Maryland high school. Most schools offer it as an elective like ceramics or woodshop. Since the class doesn’t count for credit, those students– who aren’t otherwise encouraged by parents, or a longstanding love of computers–don’t have much incentive to take it.

Computers are becoming an increasingly ubiquitous part of our lives. As such, Dr. desJardins thinks that Computer Science should be a requirement for high school students. “We make them take Government, Math, Science, and English. But, we don’t make them take Computer Science—but it’s the fastest growing job market of any discipline,” she says. “I think it’s morally wrong that we’re not teaching children how to master this technology.”

In March 2012, Drs. desJardins, Rheingans, and Martin formed CE21-Maryland to get a deeper understanding of the shortcomings of Computer Science education in Maryland, and to help solve this problem. The group is supported by NSF’s Computing Education for the 21^st Century (CE21) program.

Last August, CE21-Maryland held its first mini-summit to raise awareness of these issues among Computer Science high school teachers across the state. The summit successfully helped establish connections among teachers who share this passion for change. “Having a community is absolutely important to helping empower people,” says Dr. Rheingans, who has proven the importance of community first-hand as the director of UMBC’s Center for Women in Technology (CWIT).

On May 17, CE21-Maryland will hold its second Summit for Computing Education. Teachers, administrators, legislators, and industry leaders will gather at UMBC to explore these issues, network, and discuss plans for increasing the number and diversity of students studying Computer Science in our state.

The summit will include a college student panel, where current computing majors will share their journey to becoming Computer Science majors. One session will take a look at the AP CS Principles course, a proposed AP course being developed by the College Board and National Science Foundation, with pilots offered around the country.

Dr. desJardins realizes that recognizing the Computer Science curriculum problem is a lot easier than fixing it.  “It’s a chicken or the egg problem,” she says. Regulating Computer Science classes across the state can’t happen until teachers are trained to teach it. Training is not likely to happen unless enrollment increases. CE21-Maryland envisions a two-pronged approach to train teachers and make connections with legislators who can make a difference.

The women behind CE21-Maryland are working hard to change the compostion of Computer Science majors. But, why?

“First of all it’s a numbers problem. Second of all it’s a diversity problem,” explains Dr. Rheingans. By 2018, nearly 40,000 new computing-related jobs will be available in Maryland each year. But, only about 2,000 bachelor’s degrees in computing and information systems are awarded by Maryland institutions annually. Recruiting more women and minorities to the major will help satisfy the huge need for computing majors in the future.   

But, perhaps more importantt is the chance to add diversity to the next generation of technological problem-solvers. “Different perspectives leads you to stronger, more robust solutions,” she says.

Ph.D. Dissertation Defense

High Power Mid-infrared Quantum Cascade Laser Array
for Standoff Photoacoustic Chemical Detection

Xing Chen

1:00-3:00pm Friday, 17 May 17 2013, TRC CASPR Conference Room

Quantum cascade lasers (QCLs) are compact, portable, powerful semiconductor laser sources with emission wavelengths from mid-infrared (mid-IR) to terahertz (THz) regions of the electromagnetic spectrum. Mid-IR (i.e. wavelengths from 3 to 20 µm) QCLs are of great importance in a wide range of applications such as trace gas sensing, environmental monitoring, free space communication, medical diagnosis and so on. High power QCLs are particularly important to applications such as infrared counter measure (IRCM) and standoff chemical detections. In such applications, the system performances critically depend on the amount of power a QCL can produce. This dissertation includes two major studies: the first part of the dissertation includes design, fabrication and characterization of high power mid-IR QCL arrays; the second part involves standoff chemical detection using QCLs as laser sources and photoacoustic effect as sensing technologies.

In the first part of the dissertation, we design, fabricate and characterize multi-emitter QCL arrays consisting of multiple narrow laser stripes. Simulation results indicate that the proposed multi-emitter laser arrays present much better thermal performance than a broad area laser device, while having the same thermal management ability as a single narrow stripe device. We have successfully fabricated edge emitting and surface emitting QCL arrays with 5 and 16 emitters. Experimental results show that, with the same laser cavity length, a QCL array with 5 emitters produces over 3 times more power than a single emitter laser device. QCL array with 16 emitters generates about 4 W output peak power at wavelength ~7.9 µm. We have also fabricated surface emitting QCL arrays and demonstrated single mode emission.

The second part of the dissertation involves using high power mid-IR QCLs to perform standoff chemical detections based on photoacoustic sensing technologies. Photoacoustic effect is a light-matter interaction effect that involves generation of acoustic waves when a medium absorbs electromagnetic energy from light. It has been known as a sensitive spectroscopic technique for chemical sensing.

Standoff photoacoustic chemical detection with distance more than 41 feet using quantum cascade laser operated at relatively low power, less than 40 mW, is demonstrated. A simplified theoretical model is developed for pulsed laser photoacoustic effect in open-air environment. The standoff photoacoustic signal can be calibrated as a function of different parameters such as laser pulse energy, gas vapor concentration and detection distance. The results yield good agreements with theoretical model. Standoff detection of solid phase explosive chemicals has also been demonstrated by the use of an ultra-sensitive microphone and acoustic reflector. More than 8 feet detection distance is obtained for standoff photoacoustic sensing of explosives.

Committee: Drs. Fow-Sen Choa (Chair), Brian Cullum, Yordan Kostov, Ryan Robucci, Chen-Chia Wang and Li Yan

Google announced that it will establish a Quantum Artificial Intelligence Lab with NASA to study the use of quantum computing for machne lerning in support of problems including web search, speech recognition, planning and scheduling, search for exoplanets, and support operations in mission control centers.

"We believe quantum computing may help solve some of the most challenging computer science problems, particularly in machine learning. Machine learning is all about building better models of the world to make more accurate predictions. If we want to cure diseases, we need better models of how they develop. If we want to create effective environmental policies, we need better models of what’s happening to our climate. And if we want to build a more useful search engine, we need to better understand spoken questions and what’s on the web so you get the best answer."

The lab will use a D-Wave Systems computer and share it with external researchers via NASA's USRA. According to D-wave Systems, "Installation has already begun at NASA's Ames Research Center in Moffett Field, California, and the system is expected to be available to researchers during Q3."

"As part of the selection process, Google, NASA and USRA created a series of benchmark and acceptance tests that the new D-Wave 512-qubit system was required to pass before the installation at NASA Ames could proceed. In all cases, the D-Wave Two system met or exceeded the required performance specifications, in some cases by a large margin."

If you are interested in finding out more about quantum computing, you might talk to two members of the CSEE faculty who work on the topic: Professors Sam Lomonaco and Joel Morris.

Ph.D. Dissertation Proposal

A Semantic Resolution Framework for
Manufacturing Capability Data Integration

10:30am Tuesday, May 14, 2013, ITE 346, UMBC

Yan Kang

Building flexible manufacturing supply chains requires interoperable and accurate manufacturing service capability (MSC) information of all supply chain participants. Today, MSC information, which is typically published either on the supplier’s web site or registered at an e-marketplace portal, has been shown to fall short of the interoperability and accuracy requirements. This issue can be addressed by annotating the MSC information using shared ontologies. However, ontology-based approaches face two main challenges: 1) lack of an effective way to transform a large amount of complex MSC information hidden in the web sites of manufacturers into a representation of shared semantics and 2) difficulties in the adoption of ontology-based approaches by the supply chain managers and users because of their unfamiliar of the syntax and semantics of formal ontology languages such as OWL and RDF and the lack of tools friendly for inexperienced users.

The objective of our research is to address the main challenges of ontology-based approaches by developing an innovative approach that can effectively extract a large volume of manufacturing capability instance data, accurately annotate these instance data with semantics and integrate these data under a formal manufacturing domain ontology. To achieve the objective, a Semantic Resolution Framework is proposed to guides every step of the manufacturing capability data integration process and to resolve semantic heterogeneity with minimal human supervision. The key innovations of this framework includes 1) three assisting systems, including a Triple Store Extractor, a Triple Store to Ontology Mapper and a Ontology-based Extensible Dynamic Form, that can efficiently and effectively perform the automatic processes of extracting, annotating and integrating manufacturing capability data.; 2) a Semantic Resolution Knowledge Base (SR-KB) that incrementally filled with, among other things, rules/patterns learned from errors. This SR-KB together with an Upper Manufacturing Domain Ontology (UMO) provide knowledge for resolving semantic differences in the integration process; 3) an evolution mechanism that enables SR-KB to continuously improve itself and gradually reduce the human involvement by learning from mistakes.

Committee: Yun Peng (chair), Charles Nicholas, Tim Finin, Yaacov Yesha, Boonserm Kulvatunyou (NIST)

Ph.D. Dissertation Proposal

Convexification/Deconvexification for Training Neural

Networks and Recurrent Deep Learning Machines

Yichuan Gui

9:30am Thursday, 16 May 2013, ITE 325b, UMBC

The development of artificial neural networks (ANNs) has been impeded by the local minimum problem for decades. One principle goal of this proposal focuses on devel- oping a methodology to alleviate the local minimum problem in training ANNs. A new training criterion called the normalized risk-averting error (NRAE) criterion is proposed to avoid nonglobal local minima in training multilayer perceptrons (MLPs) and deep learning machines (DLMs). Training methods based on the NRAE crite- rion are developed to achieve global or near-global minima with satisfactory learning errors and generalization capabilities.

Many advantages of DLMs have been analyzed in recent research works of ANNs, and effective architectures and training methods have been explored from those works. However, feedback structures are commonly ignored in previous research of DLMs. The next objective of this proposal is to develop recurrent deep learning machines (RDLMs) through adding feedback structures to deep architectures in DLMs. De- signing and testing works are expected to illustrate the efficiency and effectiveness of RDLMs with feedback structures comparing to feedforward DLMs.

Preliminary works presented in this proposal demonstrate the effectiveness of NRAE-based training methods in avoid nonglobal local minima for training MLPs. Methods based on the NRAE criterion will be tested in training DLMs, and the de- veloping and testing of RDLMs will be performed in subsequent works. Moreover, an approach that combining both the NRAE criterion and RDLMs will also be explored to minimize the training error and maximize the generalization capability. Contribu- tions of this proposed research are expected as (1) provide an effective way to avoid local minimum problem in training MLPs and DLMs with satisfactory performance; (2) develop a new type of RDLMs with feedback connections for training large-scale dataset efficiently; (3) apply the NRAE criterion to train RDLMs for minimizing training errors and maximizing generalization capabilities. Those contributions are expected to significantly boost research interests in ANNs' fields and stimulate new practical applications in the future.

Committee: James Lo (mentor), Yun Peng (mentor), Tim Finin, Tim Oates, Charles Nicholas

MS Defense

Social Media Analytics: Digital Footprints

Sandhya Krishnan

9:00am Monday, 13 May 2013, ITE325b

In this work we describe an approach to distinguish real and impostor/ compromised accounts on social media. Compromising a user's social media account is not only a breach of security, but can also lead to dissemination of misinformation at a fast pace on social media. There have been several such high profile attacks recently, including on Twitter feeds of AP, CBS, and Delta Airlines. A fake account for the Prime Minister's Office in India was used to spread malicious rumors last year. Our approach builds a profile or footprint of users using both the content of their tweets and the structure of their network. We analyze the real time content of users (Tweets, Facebook posts, etc.) and compare them with information about the user from reliable sources on the Web (e.g., newspapers, news channels, etc.) in order to compute a similarity metric between content from the two sources. We also compute a metric based on the social network analysis of the users: who connects to them, who they are connected with, and how central they are in their network. We have shown how such an approach can easily detect fake accounts for not just well known people such as President Obama, but also for lesser known people and organizations. We also show promising initial results on how this approach can be used to detect an account which has been hacked.

Committee: Anupam Joshi (chair), Tim Finin, Tim Oates, Ponnurangam Kumaraguru (IIIT Delhi)

On May 7, 2013, Dr. Richard Forno, Assistant Director of UMBC's Center for Cybersecurity and Director of UMBC's Graduate Cybersecurity Program, conducted an invited talk on the proposed Cybersecurity Information Sharing and Protection Act (CISPA) and moderated a discussion about general cybersecurity issues to UMBC's Delta Sigma Theta Sorority.  The evening event was the second in a series of invited guest speakers as part of the Sorority's May Week festivities.

CISPA is a proposed law that would allow and encourage the sharing of Internet traffic information between the U.S. government and technology and manufacturing companies in order to help US government agencies investigate cyber threats and ensure the security of networks against cyberattacks.

Delta Sigma Theta Sorority, Inc. is a private, non-profit organization whose purpose is to provide assistance and support through established programs in local communities throughout the world. A sisterhood of more than 200,000 predominately Black college educated women, the Sorority currently has over 900 chapters located in the United States, England, Japan (Tokyo and Okinawa), Germany, the Virgin Islands, Bermuda, the Bahamas and the Republic of Korea.