Dissertation defense: Cross-Layer Techniques for Boosting Base-Station Anonymity in Wireless Sensor Networks

Dissertation Defense Announcement

Cross-Layer Techniques for Boosting Base-Station Anonymity in Wireless Sensor Networks

Sami Alsemairi

9:30 Wednesday, 9 November 2016, ITE 346

Wireless Sensor Networks (WSNs) provide an effective solution for surveillance and data gathering applications in hostile environments where human presence is infeasible, risky or very costly. Examples of these applications include military reconnaissance, guarding boarders against human trafficking, security surveillance, etc. A WSN is typically composed of a large number of sensor nodes that probe their surrounding and transmit measurements over multi-hop paths to an in-situ Base-Station (BS). The BS not only acts as a sink of all collected sensor data but also provides network management and serves as a gateway to remote commend centers. Such an important role makes the BS a target of adversary attacks that opt to achieve Denial-of-Service (DoS) and nullify the WSN utility to the application. Even if the WSN applies conventional security mechanisms such as authentication and data encryption, the adversary may apply traffic analysis techniques to locate the BS and target it with attacks. This motivates a significant need for boosting BS anonymity to conceal its location.

In this dissertation, we address the challenges of BS anonymity and develop a library of techniques to counter the threat of traffic analysis. The focus of our work is on the link and network layers. We first exploit packet combining as a means to vary the traffic density throughout the network. We call this technique combining the data payload of multiple packets (CoDa), where a node groups the payload of multiple incoming data packets into a single packet that is forwarded toward the BS. CoDa cuts on the number of transmissions that constitute evidences for implicating the BS as a destination of all traffic and thus degrades the adversary’s ability in conducting effective traffic analysis.

Next we develop a novel technique for increasing BS anonymity by establishing a sleep/active schedule among the nodes that are far away from the BS, and increasing the traffic density in selected parts of the network in order to give the impression that the BS is located in the vicinity of the sleeping nodes. We call this technique Adaptive Sampling Rate for increased Anonymity (ASRA). Moreover, we develop three novel techniques based on a hierarchical routing topology. The first, which we call Hierarchical Anonymity-aware Routing Topology (HART), forms clusters and an inter-cluster-head routing topology so that a high traffic volume can be observed in areas away from the BS. The second is a novel cross-layer technique that forms a mesh topology. We call this technique cluster mesh topology to boost BS’s anonymity (CMBA). CMBA opts to establish a routing topology such that the traffic pattern does not implicate any particular node as a sink.

The third technique creates multiple mesh-based routing topologies among the cluster-heads (CHs). By applying the closed space-filling curves such as the Moore curve, for forming a mesh, the CHs are offered a number of choices for disseminating aggregated data to the BS through inter-CH paths. Then, the BS forwards the aggregated data as well so that it appears as one of the CH. We call this technique boosting the BS anonymity through multiple mesh-based routing topologies (BAMT). We validate the effectiveness of all anonymity-boosting techniques through simulation and highlight the trade-off between anonymity and overhead.

Committee: Drs. Mohamed Younis (Chair), Charles Nicholas, Chintan Patel, Richard Forno and Waleed Youssef

Career and internship opportunities at Google, 9/29-30


Interested in learning more about Google?
Come hear it from Googlers and UMBC alumni!

On Thursday Sept. 29 and Friday Sept. 30, Google host hour tech/culture/info talk events on campus for UMBC students to learn more about Google and the internship and career opportunities it offers to students. They will have food, swags and many internship and full time opportunities for students.

Check out the details below and register for the event(s) HERE, if you’re interested in Google opportunites make sure to include a soft copy of your resume.


Who: Except the first event on Thursday 09/29, at  1pm that is designed for PhD engineering students, all Computer Science and Engineering students regardless of degrees they are pursuing, and anyone else with an interest in software development are welcome!

Why: Learn more about Google’s hiring process, culture, technology, job and/ internship opportunities, and more! – directly from a Googler!

What to do next?: Register for the event HERE! Make sure your resume and LinkedIn profiles are up to date (feel free to link both in the form above) and of course come with lots of good questions!

Here’s information on the four events:

  • What: Info Sharing: Google PhD Info Session for PhD CS/Engineering Students
    When: 9/29, Thursday, 1pm – 3pm
    Where: Commons 318 RSVP: RSVP Form

  • What: Info Sharing: Resume Tips & Tricks for Technical Opportunities
    When: 9/29, Thursday, 4pm – 5pm
    Where: Commons 331 RSVP: RSVP Form

  • What: Talk and Workshop: Google Technical Interview Prep Workshop
    When: 9/30, Friday, 1pm – 2:30pm
    Where: Commons 331 RSVP: RSVP Form

  • What: Tech Talk: Google AppEngine, Simple & Scalable Solution for Startups
    When: 9/30, Friday, 3pm – 4pm
    Where: Commons 329 RSVP: RSVP Form


Are you interested in assistive robotics research?

Are you interested in assistive robotics research?

Kavita Krishnaswamy is a Ph.D. candidate in the UMBC Computer Science program and has Spinal Muscular Atrophy (SMA), a neuromuscular disorder that affects the control of muscle movement.

Her goal is to develop robotics aids to increase independence for people with physical disabilities like herself. As part of her research she is conducting a survey on attitudes toward robotic aids and how they may improve the quality of life for those with physical disabilities, their family members, and their caregivers.

If you have a physical disability, are a caregiver for a person with a physical disability, or are a friend or family member of a person with a physical disability, you can help Kavita with her research by particiating in the survey. Participation is voluntary and anonymous. The participant must be 18 years or older. You can access the survey here.

This study has been reviewed and approved by the UMBC Institutional Review Board (IRB). A representative of that Board, from the Office.for Research Protections and Compliance, is available to discuss the review process or Kavita’s rights as a research participant. Contact information of the Office is (410) 455-2737 or

Consider pursuing an advanced degree in computing

Screen Shot 2016-08-30 at 1.04.19 AM

The Computing Research Association has published five short videos to explain the benefits of pursuing a PhD in a computing discipline. The videos showcase young researchers with PhDs who are now working in industry as they talk about what compelled them to pursue a doctorate and how they are using their advanced training in their work. The videos illustrate how a PhD is useful in industry as well as in academia.


Click to watch all five videos or select one below.
  • Video 1: Adrienne Porter Felt (PhD Berkeley) talks about her work on security at Google.
  • Video 2: Hoda Eldardiry (PhD Purdue) talks about her work on predictive analytics, using machine learning and data mining at Palo Alto Research Center (PARC)
  • Video 3: Susanna Ricco (PhD Duke) and Mac Mason (PhD Duke) at Google talk about their work in robotics and vision.
  • Video 4: Richard Socher (PhD Stanford) talks about his work in artificial intelligence at Salesforce.
  • Video 5: Tiffany Chen (PhD Stanford) talks about her work in bioinformatics at Cytobank.

Omar Shehab PhD defense: Solving Mathematical Problems in Quantum Regime, 7/7


Ph.D. Dissertation Defense
Computer Science and Electrical Engineering

Solving Mathematical Problems in Quantum Regime

Omar Shehab

2:00pm Thursday, 7 July 2016, ITE 325b

In this dissertation, I investigate a number of algorithmic approaches in quantum computational regime to solve mathematical problems. My problems of interest are the graph isomorphism and graph automorphism problems, and the complexity of memory recall of Hopfield network. I show that the hidden subgroup algorithm, quantum Fourier sampling, always fails, to construct the automorphism group for the class of the cycle graphs. I have discussed what we may infer for a few non-trivial classes of graphs from this result. This raises the question, which I have discussed in this dissertation, whether the hidden subgroup algorithm is the best approach for these kinds of problems. I have given a correctness proof of the Hen-Young quantum adiabatic algorithm for graph isomorphism for cycle graphs. To the best of my knowledge, this result is the first of its kind. I also report a proof-of-concept implementation of a quantum annealing algorithm for the graph isomorphism problem on a commercial quantum annealing device. This is also, to the best of my knowledge, the first of its kind. I have also discussed the worst-case for the algorithm. Finally, I have shown that quantum annealing helps us achieve exponential capacity for Hopfield networks.

Committee: Drs. Samuel J Lomonaco Jr. (Chair), Milton Halem, Yanhua Shih, William Gasarch and John Dorband

Travel grants for students to attend 2016 Grace Hopper Conference

Google will fund travel grants to the 2016 Grace Hopper Celebration of Women in Computing Conference (GHC) which takes place in Houston, Oct 19-21, 2016. The GHC is the world’s largest gathering of women technologists and offers many valuable resources to students and academics alike, from a Student Opportunity Lab to tracks specifically designed to educate and inspire faculty. Its career fair, one of the largest in the U.S., earns a 97% satisfaction rate from our student survey respondents.

University students and industry professionals in the US and Canada who are excelling in computing and passionate about supporting women in tech can apply for a travel grant to attend the 2016 Grace Hopper conference. Sponsorship includes: conference registration, round trip flight to Houston, TX, arranged hotel accommodations from October 18-22, $75 USD reimbursement for miscellaneous travel costs and a fun social event with your fellow travel grant recipients on one of the evenings of the conference.

Apply by Sunday, July 10 using this online form. The Grace Hopper Travel Grant recipients will be announced by July 27th.

PhD defense: Z. Wang, Learning Representations and Modeling Temporal Signals

Computer Science PhD Dissertation Defense

Learning Representations and Modeling Temporal Signals:
Symbolic Approximation, Deep Learning, Optimization and Beyond

Zhiguang Wang

1:00pm Tuesday, 31 May 2016, ITE 325, UMBC

Most real-world data has a temporal component, whether it is measurements of natural or man-made phenomena. Specifically, complex, high-dimensional and noisy temporal data are often difficult to model because the intrinsic temporal/topographic structures are highly non-linear, which makes the learning and optimization procedure more complicated. This talk will cover three correlated but self-contained topics to address the problem of representation learning in time series, deep learning optimization, and unsupervised feature learning.

First, I will show how to incorporate ideas from symbolic approximation with simple NLP techniques to represent and model temporal signals. To improve the symbolic approximation to model signals as words, we build a time-delay embedding vector (AKA skip gram) to extract the dependencies at different time scales, which yields state-of-the-art classification performance with a bag-of-patterns and vector space model. A non-parametric pooling/weighting scheme is proposed to extend the methods to multivariate signals

Second, I will show how to encode signals as images to learn and analyze them with deep learning methods. The Gramian Angular Field (GAF) and Markov Transition Field (MTF), as two novel approaches to encode both multi-scale spatial correlation and first order Markov dynamics of the temporal signals as images, are proposed. These visual representations are proved to work well in both visualizations by humans and pattern recognition using deep learning approaches. This work yields state-of-the-art algorithms for temporal data classification and imputation.

Finally, deep learning in image recognition (e.g. pictures or GAF/MTF images) involves high-dimensional non-convex optimization. Such optimization is generally intractable. However, I show how to use a set of exponential form based error estimators (NRAE/NAAE) and learning approaches (Adaptive Training) to attack the non-convex optimization problems in training deep neural networks. Both in theory and practice, they are able to achieve optimality on accuracy and robustness against outliers/noise. They provide another perspective to address the non-convex optimization problem (especially saddle points) in deep learning.

Committee: Tim Oates (Chair), Matt Schmill (Miner & Kasch), Hamed Pirsiavash, Yun Peng, Kostas Kalpakis

Microsoft Student Partners program

Microsoft Student Partners (MSPs) are student technology leaders, empowered to build Microsoft communities on their campus and share their deep knowledge and passion for technology with their fellow classmates.  See here for more information. Apply by 15 July 2016.

UMBC students demonstrate smartphone applications, 12:30-2:30 Tue 5/10


7919_New Faculty 2009 Nilanjan Banerjee Computer Science and Computer Engineering

Student groups drawn from two UMBC classes will demonstrate twelve mobile applications they developed as projects from 12:30 to 2:30 on Tuesday, 10 May 2016 in the UC Ballroom. Pizza will be provided.

The projects are a result of an innovative collaboration between a computer science class lead by Professor Nilanjan Banerjee (CMSC 678 Mobile Computing) and a visual arts class lead by Professor Viviana Chacon (ART 434 Advanced Interface Design).

The two faculty were awarded a grant from the fall 2015 round of the Hrabowski Fund for Innovation competition to develop and evaluate the collaboration between the two courses. The classes held regular joint sessions and each project group comprised students from both Engineering and Visual Arts.

In ART 434 Prof. Cordova concentrated on the visual experience of the interface in mobile and desktop applications, while in CMSC 628 Prof.  Banerjee provided the tools necessary to design and implement mobile applications.  Specific mobile development topics such as user interface design and implementation, accessing and displaying sensor and location data, and mobile visual design were co-­‐taught by both instructors.  Teams comprising Engineering and Visual Arts students designed and built mobile applications for local clients in Baltimore and Washington DC area.

poster describing the event has brief descriptions of the twelve class projects.

NSF CyberCorps: Scholarship For Service, May 15 deadline

UMBC undergraduate and graduate students interested in cybersecurity can apply for an Federal CyberCorps: Scholarship For Service scholarship by 15 May 2016. This application deadline will be the last one under the current NSF grant, which ends August 2017.

The Federal CyberCorps: Scholarship For Service program is designed to increase and strengthen the cadre of federal information assurance professionals that protect the government’s critical information infrastructure. This program provides scholarships that may fully fund the typical costs incurred by full-time students while attending a participating institution, including tuition and education and related fees. Participants also receive stipends of $22,500 for undergraduate students and $34,000 for graduate students.

Applicants must be be full-time UMBC students within two years of graduation with a BS or MS degree; a student within three years of graduation with both the BS/MS degree; a student participating in a combined BS/MS degree program; or a research-based doctoral student within three years of graduation in an academic program focused on cybersecurity or information assurance. Recipients must also be US citizens or permanent residents; meet criteria for Federal employment; and be able to obtain a security clearance, if required.

For more information and instructions on how to apply see the UMBC CISA site (use old application form, and be sure to include the cover sheet).

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