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 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 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

CSEE professor Nilanjan Banerjee was interviewed at the Microsoft Faculty Summit on UMBC research that is developing sensors that can be sewn into textiles such as clothing or bedding and used control devices though gestures. Professor Banerjee is working with colleagues Ryan Robucci, Chintan Patel and Sandy McCombe-Waller (UMB) and students to prototype the hardware sensors and software components that can be part of an Internet of Things environment.

With support from Microsoft, their experimental systems are using Microsoft’s Lab of Things platform for research on connected devices in homes and other spaces. One of the use cases driving the research is helping people with limited mobility lead more independent lives by enabling them to control the environment. Buz Chmielewski, who became a quadriplegic after a surfing accident, is helping the team test and refine the system and its usability.

The Conquer site provides resources for undergraduate students interested in research, graduate school, and research careers in computing-related fields. It also provides resources for faculty mentors, looking to engage and advise undergraduates in research and prepare them for graduate school in computing fields. The site is maintained by the Computing Research Association, an organization (of which our department is a member!) with the mission to enhance innovation by joining with industry, government and academia to strengthen research and advanced education in computing.

Specific topics of interest to undergraduate students that are covered are:

• What is computing research
• Finding research opportunities
• Undergraduate research awards
• Why go to graduate school?
• The graduate school application process

Ph.D. Dissertation proposal

Creating a Collaborative Situational-Aware IDPS

Lisa Mathews

11:00am Tuesday, 10 June 2014, ITE 346

Traditional intrusion detection and prevention systems (IDPSs) have well known limitations that decrease their utility against many kinds of attacks. Current state-of-the-art IDPSs are point based solutions that perform a simple analysis of host or network data and then flag an alert. Only known attacks whose signatures have been identified and stored in some form can be discovered by most of these systems. They cannot detect “zero day” type attacks or attacks that use “low-and-slow” vectors. Many times an attack is only revealed by post facto forensics after some damage has already been done.

To address these issues, we are developing a semantic approach to intrusion detection that uses traditional as well non-traditional sensors collaboratively. Traditional sensors include hardware or software such as network scanners, host scanners, and IDPSs like Snort. Potential non-traditional sensors include open sources or information such as online forums, blogs, and vulnerability databases which contain textual descriptions of proposed attacks or discovered exploits. After analyzing the data streams from these sensors, the information extracted is added as facts to a knowledge base using a W3C standards based ontology that our group has developed. We have also developed rules/policies that can reason over the facts to identify the situation or context in which an attack can occur. By having different sources collaborate to discover potential security threats and create additional rules/policies, the resulting situational-aware IDPS is better equipped to stop creative attacks such as those that follow a low-and-slow intrusion pattern. Leveraging information from these heterogeneous sources leads to a more robust, situational-aware IDPS that is better equipped to detect complicated attacks. This will allow for detection in soft real time. We will create a prototype of this system and test the efficiency and accuracy of its ability to detect complex malware.

Committee: Drs. Anupam Joshi (Chair), Tim Finin, John Pinkston, Charles Nicholas, Claudia Pearce, Yul Williams

Mobile Analytics: An Enabler for Urban Lifestyle Applications

Professor Archan Misra

School of Information Systems, Singapore Management University

10-11:00am 24 June 2014, ITE 459, UMBC

This talk will describe various research initiatives related to the theme of “urban mobile analytics and applications”, which utilizes smartphone sensor data from multiple individuals to extract near-real time insights about individual and collective behavior in urban public spaces. A major part of this research is being conducted under the auspices of the LiveLabs Experimentation Platform, a unique urban behavioral testbed effort that enables an ecosystem of industry partners to test advanced context-based applications on a pool of approximately 30,000 real-world users in multiple real-world public spaces in Singapore. Besides describing LiveLabs-related research in areas related to energy-efficient mobile sensing and large-scale mobile analytics (e.g., queuing analytics, group detection and adaptive indoor localization). I will describe the role of such analytics for a couple of novel industry-driven applications: (a) in-store shopper intent monitoring and (b) large-scale mobile crowd-tasking.

Archan Misra is an Associate Professor of Information Systems at Singapore Management University (SMU), and a Director of the LiveLabs research center at SMU. Over the past 14 years (as part of his previous jobs with IBM Research and Telcordia Technologies), he has worked extensively in the areas of mobile systems, wireless networking and pervasive computing, and is a co-author on papers that received the Best Paper awards in EUC 2008, ACM WOWMOM 2002 and IEEE MILCOM 2001. Archan’s broad research interests lie in the areas of pervasive computing and mobile systems, with specific current focus on applying mobile sensing and real-time analytics to understand human lifestyle-driven activities in urban spaces. He is presently an Editor of the IEEE Transactions on Mobile Computing and the Elsevier Journal of Pervasive and Mobile Computing and chaired the IEEE Computer Society’s Technical Committee on Computer Communications (TCCC) from 2005-2007. Archan holds a Ph.D. in Electrical and Computer Engineering from the University of Maryland at College Park.

Host: Prof. Nirmalya Roy,

Ph.D. Dissertation Defense
Computer Science and Electrical Engineering
University of Maryland, Baltimore County

Human Sensor Networks for Disasters

Oleg Aulov

11:00am Thursday, 29 May 2014, ITE325b, UMBC

This dissertation, presents a novel approach that utilizes quantifiable social media data as a human aware near real-time observing system coupled with geophysical predictive models for improved response to disasters and extreme events. It shows that social media data has the potential to significantly improve disaster management beyond informing the public and emphasizes the importance of different roles that social media can play in management, monitoring, modeling and mitigation of natural and human-caused disasters.

In the proposed approach, social media sources are viewed as a Human Sensor Network, and Social Media users are viewed as “human sensors” that are “deployed” in the field, and their posts are considered to be “sensor observations”. I have utilized the “human sensor observations”, i.e. data acquired from social media, as boundary value forcings to show improved geophysical model forecasts of extreme disaster events when combined with other scientific data such as satellite observations and sensor measurements. In addition, I have developed a system called ASON maps that dynamically combines model forecast outputs with specified social media observations and physical measurements to define the regions of event impacts such as flood distributions and levels, beached tarballs, power outages etc. Real time large datasets were collected, archived and are available for following recent extreme disasters events as use case scenarios.

In the case of the Deepwater Horizon oil spill disaster of 2010 that devastated the Gulf of Mexico, the research demonstrates how social media data can be used as a boundary forcing condition of the oil spill plume forecast model, and results in an order of magnitude forecast improvement. In the case of Hurricane Sandy NY/NJ landfall impact of 2012, owing to inherent uncertainties in the weather forecasts, the NOAA operational surge model only forecasts the worst-case scenario for flooding from any given hurricane. This dissertation demonstrates how the model forecasts, when combined with social media data in a single framework, can be used for near-real time forecast validation, damage assessment and disaster management. Geolocated and time-stamped photos allow near real-time assessment of the surge levels at different locations, which can validate model forecasts give timely views of the actual levels of surge, as well as provide an upper bound regional street level maps beyond which the surge did not spread. In the case of the Tohoku Earthquake and Tsunami of 2011, social media aspects of handheld devices such as Geiger counters that can potentially detect radioactive debris are discussed as well.

Committee: Dr. Milton Halem (chair), Tim Finin, Anupam Joshi, James Smith, Yelena Yesha

UMBC graduate student Omar Shehab received a travel grant to attend two co-located events, the 14th Canadian Quantum Information Summer School and the 11th Canadian Quantum Information Student Conference. Both events are organized by the Fields Institute and will be held at the University of Guelph.

Omar is a fourth year PhD student in Computer Science working with by Professor Samuel Lomonaco. His Ph.D.research involves determining the quantum computational complexity of topological problems. He is also interested in quantum games, randomness and cryptography. This summer he will be working as a Visiting Research Assistant the USC Information Sciences Institute facility in Arlington, Virginia.

 Ph.D.Dissertation Defense
Computer Science and Electrical Engineering
University of Maryland, Baltimore County

Schema Free Querying of Semantic Data

Lushan Han

10:00am Friday, 23 May 2014, ITE 325b

Developing interfaces to enable casual, non-expert users to query complex structured data has been the subject of much research over the past forty years. We refer to them as as schema-free query interfaces, since they allow users to freely query data without understanding its schema, knowing how to refer to objects, or mastering the appropriate formal query language. Schema-free query interfaces address fundamental problems in natural language processing, databases and AI to connect users’ conceptual models and machine representations.

However, schema-free query interface systems are faced with three hard problems. First, we still lack a practical interface. Natural Language Interfaces (NLIs) are easy for users but hard for machines. Current NLP techniques are still unreliable in extracting the relational structure from natural language questions. Keyword query interfaces, on the other hand, have limited expressiveness and inherit ambiguity from the natural language terms used as keywords. Second, people express or model the same meaning in many different ways, which can result in the vocabulary and structure mismatches between users’ queries and the machines’ representation. We still rely on ad hoc and labor-intensive approaches to deal with this ‘semantic heterogeneity problem’. Third, the Web has seen increasing amounts of open domain semantic data with heterogeneous or unknown schemas, which challenges traditional NLI systems that require a well-defined schema. Some modern systems gave up the approach of translating the user query into a formal query at the schema level and chose to directly search into the entity network (ABox) for the matchings of the user query. This approach, however, is computationally expensive and has an ad hoc nature.

In this thesis, we develop a novel approach to address the three hard problems. We introduce a new schema-free query interface, SFQ interface, in which users explicitly specify the relational structure of the query as a graphical “skeleton” and annotate it with freely chosen words, phrases and entity names. This circumvents the unreliable step of extracting complete relations from natural language queries.

We describe a framework for interpreting these SFQ queries over open domain semantic data that automatically translates them to formal queries. First, we learn a schema statistically from the entity network and represent as a graph, which we call the schema network. Our mapping algorithms run on the schema network rather than the entity network, enhancing scalability. We define the probability of “observing” a path on the schema network. Following it, we create two statistical association models that will be used to carry out disambiguation. Novel mapping algorithms are developed that exploit semantic similarity measures and association measures to address the structure and vocabulary mismatch problems. Our approach is fully computational and requires no special lexicons, mapping rules, domain-specific syntactic or semantic grammars, thesauri or hard-coded semantics.

We evaluate our approach on two large datasets, DBLP+ and DBpedia. We developed DBLP+ by augmenting the DBLP dataset with additional data from CiteSeerX and ArnetMiner. We created 220 SFQ queries on the DBLP+ dataset. For DBpedia, we had three human subjects (who were unfamiliar with DBpedia) translate 33 natural language questions from the 2011 QALD workshop into SFQ queries. We carried out cross-validation on the 220 DBLP+ queries and cross-domain validation on the 99 DBpedia queries in which the parameters tuned for the DBLP+ queries are applied to the DBpedia queries. The evaluation results on the two datasets show that our system has very good efficacy and efficiency.

Committee: Drs. Li Ding (Memect), Tim Finin (chair), Anupam Joshi, Paul McNamee (JHU), Yelena Yesha