hackUMBC ’14 Registration Opens

The second hackUMBC is coming! (And has nothing to do with security.)

hackUMBC ’14 will take place on the weekend of 27-28 September (Saturday into Sunday). The event is open to students of any skill level, from innovators and entrepreneurs to designers and hardcore coders. Its purpose is to allow students to mingle and collaborate for 24 continuous hours of community exploration to grow technology projects from scratch while expanding their connections to other students and mentors from both industry and academia. Through the generosity of various sponsors, admission is free, and includes meals, snacks, swag, prizes, and more!

Last year’s inaugural event ‘sold out’ at 100 students from across the UMBC campus community, including teams from CMSC, CMPE, EE, IS, Biology, Biotechnology, Math, Physics, and Media Studies. This year we’re expanding admission to students from both UMBC and local colleges/high schools, so register early!

More details and sign-up information is available at the event website.

Faculty, staff, and leaders from around campus and the local industry who are interested in serving as mentors or judges for hackUMBC are invited to contact Dr. Rick Forno for more information.

Photos from last year’s inaugural event held in the Skylight Room can be found here.

PhD Defense, E. Birrane on Virtual Circuit Provisioning in Challenged Sensor Internetworks: with Application to the Solar System Internet, 10am Mon 8/11

from flckr, marked for reuse

Dissertation Defense

Virtual Circuit Provisioning in Challenged Sensor Internetworks:
with Application to the Solar System Internet

Ed Birrane

10:00am-12:00pm Monday, 11 August 2014, ITE325b

In this thesis, we present a challenged sensor internetwork (CSI) networking architecture which federates heterogeneous constituent networks behind an overlay routing mechanism abstracted from individual data link layers. The CSI is unique and required to implement expanding sensor networks.

Demand for sensing networks with increasing spatial footprints is evidenced by ongoing efforts to build geo-political border monitoring networks, intelligent highway initiatives, automated undersea surveillance, and NASA effort to construct a Solar System Internet. Existing network technologies fail to address multiple physical links, frequent disruptions, and significant signal propagation delays. The construction and maintenance of virtual circuits in an internetwork abstracted from differences in the physical, data-link, and transport layers of an internetwork represents a unique research contribution with immediate utility for a wide variety of sensing network concepts.

We describe the CSI architecture as the intersection of wireless, delay-tolerant, and heterogeneous networks and describe special characteristics of this architecture than enable useful assumptions to optimize messaging. We define an internetwork routing (INR) framework that decomposes the routing function into discrete logical steps and we provide algorithms for each of these steps. An inferred Contact Graph Routing (iCGR) algorithm populates logical graphs from local nodes. A Contact Graph Routing with Extension Blocks (CGR-EB) algorithm provides a hybrid source-path algorithm for synchronizing link state along network paths. A Predictive Capacity Consumption (PCC) algorithm exploits CGR-EB data to build a congestion model. Payload Aggregation and Fragmentation (PAF) and Traffic-Shaping Contacts (TSC) algorithms condition data and place limits on the amount of internetwork traffic carried over local networks.

From simulation, iCGR performs within ~15% of a perfect-knowledge system. CGR-EB has a speedup over standard approaches by 300% in stable topologies, by 3000% in unstable topologies, and by 11000% in unstable topologies with non-monotonic cost functions. PCC delivers 97% more data in congested networks over table-based approaches and 37% more data than the INR framework without the congestion model. PAF/TSC reduces message count by 43% while increasing goodput by 63%.

Together, these algorithms build and monitor virtual circuits in the CSI architecture. Portions of this work are in consideration for deployment in NASA networks.

Committee: Drs. Alan Sherman (Co-Chair, UMBC), Mohammed Younis (Co-Chair, UMBC), Dhananjay Phatak (UMBC), Vinton Cerf (Google), Keith Scott (MITRE), Hans Kruse (OU)

MS defense: S. Viseh, Low Power On-board Processor for A Tongue Assistive Device, 12pm Tue 8/5

MS Thesis Defense

A Low Power On-board Processor
for A Tongue Assistive Device

Sina Viseh

12:00 pm Tuesday, 5 August 2014, ITE 325B

In biomedical wearable devices, patient’s convenience and accuracy are the main priorities. To fulfill the patient’s convenience requirement, the power consumption, which directly translates to the battery lifetime and size, must be kept as low as possible. Meanwhile, adopted improvements should not impact the accuracy. Therefore, focus on reducing the energy consumption within these devices has already been the subject of a significant amount of research in the past few years. In most wearable devices, all raw data is transmitted to a computer to carry out the required processing. This vast amount of communication leads to a considerable amount of power consumption and the need for a bulky battery, which hinders the device’s practicality and patient’s convenience. Tongue Drive System (TDS) is a new unobtrusive, wireless, and wearable assistive device that allows for real time tracking of the voluntary tongue motion in the oral space for communication, control, and navigation applications. The intraoral TDS clasps to the upper teeth and resists sensor misplacement. However, the iTDS has more restrictions on its dimensions, limiting the battery size and consequently requiring a considerable reduction in its power consumption to operate over an extended period of two days on a single charge. In this thesis, we propose an ultra low power local processor for the TDS that performs all signals processing on the transmitter side, following the sensors. Implementing the computational engine reduces the data volume that needs to be wirelessly transmitted to a PC or smartphone by a factor of 30x, from 12 kbps to ~400 bps. The proposed design is implemented on an ultra low power IGLOO nano FPGA and is tested on AGLN250 prototype board. According to our post place and route results, implementing the engine on the FPGA significantly drops the required data transmission, while an ASIC implementation in 65 nm CMOS results in 0.128 mW power consumption and occupies a 0.02 footprint. To explore a different architecture, we mapped our proposed TDS processor on the EEHPC many-core. The many-core has a flexible and time saving design procedure. As a result of having a local processor, the power consumption and size of the iTDS will be significantly reduced through the use of a much smaller rechargeable battery. Moreover, the system can operate longer following every recharge, improving the iTDS usability.

Committee: Dr. Tinoosh Mohsenin (chair), Tim Oates and Mohamed Younis

MS defense: C. Shah, Usability Study of the Pico Authentication Device, 2pm Mon 8/4

pico-happy 700

MS Thesis Defense

A Usability Study of the Pico Authentication Device:
User Reactions to Pico Emulated on an Android Phone

Chirag Shah

2:00pm Monday, 4 August 2014, ITE 346

We emulate the Pico authentication token on the Android Smartphone and evaluate its usability through a casual survey of users. In 2011, Stajano proposed Pico as a physical token-based authentication system to replace traditional passwords. As far as we know, Pico has never been implemented nor tested by users. We evaluate the usability of our emulation of Pico by a comparative study in which each user creates and authenticates herself to three online accounts twice: once using Pico, and once using passwords. The study measures the accuracy, efficiency, and satisfaction of users in these tasks. Pico offers many advantages over passwords, including human-memory- and physically-effortless tasks, no typing, and high security. Based on public-key cryptography, Pico’s security design ensures that no credential ever leaves the Pico token unencrypted.

In summer 2014 we conducted a survey with 23 subjects from the UMBC community. Each subject carried out scripted tasks involving authentication, separately using our Pico emulator and a traditional password system. We measured the time and accuracy with which subjects carry out these tasks, and asked each subject to complete a survey. The survey instrument included ten Likert-scale questions and free responses and a demographics questionnaire. We then analyzed these data to find that subjects reacted positively to the Pico emulator in their responses to the Likert questions. By statistical analysis of the reactions and measurements gathered in this study we observed that subjects found the system accurate, efficient and were satisfactory.

Committee: Dr. Alan Sherman (chair), Kostas Kalpakis, Charles Nicholas and Dhananjay Phatak

PhD proposal: C. Grasso, Information Extraction from Clinical Notes, 11am Mon 8/4


PhD Dissertation Proposal

Information Extraction from Clinical Notes

Clare Grasso

11:00am Monday, 4 August 2014, ITE 325b

Clinical decision support (CDS) systems aid clinical decision making by matching an individual patient’s data to a computerized knowledge base in order to present clinicians with patient-specific recommendations. The need for methods to extract the clinical information in the free-text portions of the clinical record into a form that clinical decision support systems could access and utilize has been identified as one of the top five grand challenges in clinical decision support. This research focuses on investigating scalable machine learning and semantic techniques that do not rely on an underlying grammar to extract medical concepts in the text in order to apply them in CDS on commodity hardware and software systems. Additionally, by packaging the extracted data within a semantic representation, the facts can be combined with other semantically encoded facts and reasoned over. This allows other clinically relevant facts to be inferred which are not directly mentioned in the text and presented to the clinician for decision making.

Committee: Drs. Anupam Joshi (chair), Tim Finin, Aryya Gangopadhyay, Charles Nicholas, Claudia Pearce and Eliot Siegel

CyberNEXS comes to UMBC

The UMBC Graduate Cybersecurity Program is pleased to announce that it will incorporate the CyberNEXS virtual training environment within its courses and activities during AY14-15. Assistant CYBR GPD Ben Shariati was instrumental in bringing this technical capability to the CYBR program, beginning with CYBER 620 at Shady Grove this fall.

Through CyberNEXS, students are able to connect into its cloud-based “cyber range” to conduct hands-on system administration, security, research, and systems analysis activities in a sandboxed private environment capable of presenting multiple server, desktop, and other devices in a variety of networked configurations. Student activities can be scored and/or monitored for overall success and effectiveness over time.

Since 2011, CyberNEXS has served as the competitive environment for the Maryland Cyber Challenge.

CyberNEXS is provided to UMBC by Leidos, a leading cybersecurity firm supporting US government activities.

MS defense: P. Pappachan, Remedy: A Semantic and Collaborative Approach to Community Health-Care, 10am Thr 7/31

MS Thesis Defense

Remedy: A Semantic and Collaborative
Approach to Community Health-Care

Primal Pappachan

10:00am Thursday, 31 July 2014, ITE 325b

Community Health Workers (CHWs) act as liaisons between health-care providers and patients in underserved or un-served areas. However, the lack of information sharing and training support impedes the effectiveness of CHWs and their ability to correctly diagnose patients. In this thesis, we propose and describe a system for mobile and wearable computing devices called Remedy which assists CHWs in decision making and facilitates collaboration among them. Remedy can infer possible diseases and treatments by representing the diseases, their symptoms, and patient context in OWL ontologies and by reasoning over this model. The use of semantic representation of data makes it easier to share knowledge such as disease, symptom, diagnosis guidelines, and demography related information, between various personnel involved in health-care (e.g., CHWs, patients, health-care providers). We describe the Remedy system with the help of a motivating community health-care scenario and present an Android prototype for smart phones and Google Glass.

Committee: Drs. Anupam Joshi (chair), Tim Finin, Michael Grasso, Aryya Gangopadhyay

MS defense: M. Madeira, Analyzing Opinions in the Mom Community on Youtube, 2pm Wed 7/30


MS Thesis Defense

Analyzing Opinions in the Mom Community on Youtube

Morgan Madeira

2:00pm Wednesday, 30 June 2014, ITE 325b

The “Mom Community” on YouTube consists of a large group of parents that share their parenting beliefs and experiences to connect and share information with others. Although there is a lot of positive support in this community, it is often a hotspot for debate of controversial parenting topics. Many of these topics have one side that represents the belief of “crunchy” moms. Crunchy is a term used to describe parents that intentionally choose natural parenting methods and eco-friendly products to raise their children. Debate over these practices has led to “mompetition” and the idea that there is a right way to parent. This research investigates these claims such as how different crunchy topics are discussed and how the community has changed over time. Video comments and user data are collected from YouTube and used to understand parenting practices and opinions in the mom community.

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

MS defense: A. Hendre, Cloud Security Control Recommendation System, 8:30 Thr 7/31


MS Thesis Defense

Comparison of Cloud Security Standards and a
Cloud Security Control Recommendation System

Amit S. Hendre

8:30am Thursday, 31 July 2014, ITE346

Cloud services are becoming an essential part of many organizations. Cloud providers have to adhere to security and privacy policies to ensure their users’ data remains confidential and secure. On one hand, cloud providers are implementing their own security and privacy controls. On the other hand, standards bodies like Cloud Security Alliance (CSA), International Organization for Standards (ISO), National Institute for Standards and Technology (NIST), etc. are developing broad standards for cloud security. In this thesis we provide a comprehensive analysis of the cloud security standards that are being developed and how they compare with the security controls of cloud providers. Our study is mainly focused on policies about mobility of resources, identity and access management, data protection, incident response and audit and assessment. This thesis will help consumer organizations with their compliance needs by evaluating the security controls and policies of cloud providers and assisting them in identifying their enterprise cloud security policies.

Committee: Drs. Karuna Joshi, Tim Finin and Yelena Yesha

MS defense: S. Padalkar, Android Malware Detection and Classification, 10:30 Wed 7/30

MS Thesis Defense

Android Malware Detection and Classification
using Machine Learning Techniques

Satyajit Padalkar

10:30am Wednesday, 30 July 2014, ITE 325b

Android is popular mobile operating system and there exists multiple marketplaces for Android applications. Most of these market places allow applications to be signed using self-signed certificates. Due to this practice there exists little or very limited control over the kind of applications that are being distributed. Also advancement of Android root kits are increasingly making it easier to repackage existing Android application with malicious code. Conventional signature based techniques fail to detect such malware. So detection and classification of Android malware is a very difficult problem. We present a method to classify and detect such malware by performing a dynamic analysis of the system call sequences. Here we make use of machine learning techniques to build multiple models using distributions of syscalls as features. Using these models we predict whether given application is malicious or benign. Also we try to classify given application to specific known malware family. We also explore deep learning methods such as stacked denoising autoencoder algorithms (SdA) and its effectiveness. We experimentally evaluate our methods using a real dataset of 600 applications from 38 malware families and 25 popular benign applications from various areas. We find that a deep learning algorithm (SdA) is most accurate in detecting a malware with lowest false positives while AdaBoost performs better in classifying a malware family.

Committee: Drs. Anupam Joshi (chair), Tim Finin and Charles Nicholas