Category: Cybersecurity

Computing Majors on Team that Takes 3rd in Lockheed CTF Competition

Two College of Computing RedTeam students are part of a five-member team that finished 3rd in last weekend’s invitation-only Lockheed Martin Advanced Technologies Laboratories (ATL) Capture the Flag cybersecurity competition.

The multi-day virtual event involved 200 students on 40 teams. It opened for answer submission Friday, January 8, at 8:00 p.m., and closed Sunday, January 10, at 8 p.m.

The 3rd Place team, GoBlue!, trailed the 2nd Place team by only 14 points. RedTeam members are Michigan Tech undergraduates Dakoda Patterson, Computer Science, and Trevor Hornsby, Cybersecurity, and three University of Michigan students from the RedTeam’s partnership with that institution.

Michigan Tech RedTeam faculty advisors are Professor Yu Cai, Applied Computing, and Assistant Professor Bo Chen, Computer Science.

“We were lucky to be one of the 40 teams invited,” said Cai. “This was no small task, as the CTF included a large number of points in Reversing and “pwning” challenges, which proved to be fairly difficult. Other challenges were Cryptography, Stegonography, Web Exploitation, and miscellaneous challenges.”

CTF competitions place hidden “flags” in various computer systems, programs, images, messages, network traffic and other computing environments. Each individual or team is tasked with finding these flags. Participants win prizes while learning how to defend against cybersecurity attacks in a competitive and safe arena.

Top Three Teams

PlacementTeam NameInstitutionTotal Points
1st PlacenullbytesGeorge Mason University3697
2nd PlaceChrisSucksGeorge Mason University3330
3rd PlaceGoBlue!Michigan Tech and University of Michigan 3316

College of Computing Convocation is December 18, 3:30 pm

Congratulations Class of 2020!

We are looking forward to celebrating the accomplishments of our graduates at a Class of 2020 virtual Convocation program on Friday, December 18, 2020, at 3:30 p.m. EST.

Join the virtual event here.

The celebration will include special well-wishes from CC faculty and staff, and many will be sporting their graduation regalia. It is our privilege to welcome Ms. Dianne Marsh, 86, ’92, as our Convocation speaker. Dianne is Director of Device and Content Security for Netflix, and a member of the new College of Computing External Advisory Board.

We may be spread across the country and world this December, but we can still celebrate with some style. We look forward to sharing our best wishes with the Class of 2020 and wishing them continued success as they embark on the next phase of their lives!

This December, 40 students are expected to graduate with College of Computing degrees, joining 92 additional Class of 2020 PhD, MS, and BS alumni.

Dianne Marsh

Dianne Marsh ’86, ’92 is Director of Device and Content Security for Netflix. Her team is responsible for securing the Netflix streaming client ecosystem and advancing the platform security of Netflix-enabled devices. Dianne has a BS (’86) and MS (’92) in Computer Science from Michigan Tech.

Visit the Class of 2020 Webpage

Congratulations Graduates. We’re proud of you.


Research Excellence Fund Awards Announced

by Vice President for Research Office

The Vice President for Research Office announces the Fall 2020 REF awards. Thanks to the individual REF reviewers and the REF review panelists, as well as the deans and department chairs, for their time spent on this important internal research award process.

Research Seed Grants:

  • Sajjad Bigham, Mechanical Engineering-Engineering Mechanics
  • Bo Chen, Computer Science
  • Daniel Dowden, Civil and Environmental Engineering
  • Ana Dyreson, Mechanical Engineering-Engineering Mechanics
  • Hassan Masoud, Mechanical Engineering-Engineering Mechanics
  • Xinyu Ye, Civil and Environmental Engineering

Bo Chen, CS, Wins REF Grant for Decentralized Cloud Storage Project

Bo Chen, Computer Science, has been awarded a Fall 2020 REF Research Seed Grant (REF-RS) for his project, “Towards Secure and Reliable Decentralized Cloud Storage.” Funding for the 12-month, $25,800 award begins on January 1, 2021.

“This grant will provide significant help to advance my current research,” says Chen. “This is really exciting news for me.”

As a recipient of the REF seed grant, which is awarded by the Michigan Tech Office of the Vice President for Research, Chen will participate in review and feedback for the next round of REF proposals. View the full list of Fall 2020 REF award recipients here.

Bo Chen is a researcher with the ICC’s Cybersecurity and Computing Education research groups. The ICC–Institute of Computing and Cybersystems–brings faculty and students together to discover innovative new knowledge in the field of computing.

Abstract

A decentralized cloud storage system eliminates the need of dedicated computing infrastructures by allowing peers which have spare storage space to join the network and to provide storage service. Compared to the conventional centralized cloud storage system, it can bring significant benefits including cheaper storage cost, better fault tolerance, greater scalability, as well as more efficient data storing and retrieval, making it well fit the emerging Internet of things (IoT) applications.

While bringing immense benefits, the decentralized cloud storage system also raises significant security concerns, since the storage peers are much less reputable than the traditional data centers and may more likely misbehave.

This project thus aims to build a secure and reliable decentralized cloud storage system which can serve as the cloud infrastructure for future IoT applications. The project will actively investigate two fundamental security issues faced by the decentralized cloud storage system: 1) How can we prevent the malicious storage peers from stealing the data? 2) How can we ensure that once the data are stored into the system, they are always retrievable even if the storage peers misbehave?

To address the aforementioned issues in an untrusted p2p environment, the PI will integrate efficient integrity checking with the blockchain, as well as the broadly equipped secure hardware like Intel SGX. The PI will also broaden the educational impact of the proposed project by actively involving both graduate and undergraduate students from the MTU cybersecurity programs.


RedTeam NCL CyberLeague Rankings in Top 2%

Outstanding RedTeam results in Fall 2020 NCL cyber competition.

Of the 27 Michigan Tech RedTeam students who successfully completed the individual games in National CyberLeague games this fall, seven students ranked in the top 100, out of 6,011 participants. And in team play, two teams ranked in the top 100, out of 957 teams.

RedTeam exists to promote a security-driven mindset among the student population, and to provide a community and resource for those wishing to learn more about information security.

Faculty coaches to the RedTeam student organization are Asst. Prof. Bo Chen, Computer Science, and Prof. Yu Cai, Applied Computing.

This is the highest achievement MTU students have achieved in NCL individual games since we began participating in fall 2017.

Assistant Professor Bo Chen, Computer Science

Individual Rankings (6,011 Competitors)

  • Jacson Ott: Ranked 52
  • Trevor Hornsby: 78
  • Shane Hoppe: 80
  • Dakoda Patterson: 90
  • Matthew Chau: 92
  • Ryan Klemm: 93
  • Stu Kernstock: 98

Team Rankings (957 Teams)

  • RedTeam@mtu, Team 1: Ranked 22
    Team members: Trevor Hornsby, Stu Kernstock, Jacson Ott, Shane Hoppe, Dakoda Patterson, Matthew Chau, Ryan Klemm
  • MTU Alumni Team, Team 2: Ranked 67
    Team members: Jack Bergman, Jon Preuth, Trevor Taubitz


The National Cyber League (NCL) is a biannual cybersecurity competition. Open to U.S. high school and college students, the competition consists of a series of challenges that allow students to demonstrate their ability to identify hackers from forensic data, pentest and audit vulnerable websites, recover from ransomware attacks, and more.

Every year, over 10,000 students from more than 300 colleges and universities across the U.S. participate in the NCL competitions. Student players compete in the NCL to build their skills, leverage the NCL Scouting Reports for career and professional development, and to represent their school in the national Cyber Power Rankings.

Powered by Cyber Skyline, NCL provides a platform on which students can prepare and test themselves against practical cybersecurity challenges that they will likely face in the workforce, such as identifying hackers from forensic data, pentesting and audit vulnerable websites, recovering from ransomware attacks, and more.

The Cyber Power Rankings were created by Cyber Skyline in partnership with the National Cyber League (NCL). The rankings represent the ability of student competitors to perform real-world cybersecurity tasks on the Cyber Skyline platform.


Innovative, Active, Effective. Introducing Sidike Paheding, Applied Computing

Be Innovative. Be Active. Be Effective. This is College of Computing Assistant Professor Sidike Paheding’s teaching philosophy.

New to the Department of Applied Computing this fall, Paheding’s teaching interests include digital image processing and machine learning. This academic year he is teaching SAT3812 Cyber Security I.

A member of the Institute of Computing and Cybersystems’s Center for Data Sciences, Paheding’s research seeks to develop novel AI-driven technologies. His primary interests are image/video processing, machine learning, deep learning, computer vision, and remote sensing.

Paheding comes to Michigan Tech from Purdue University Northwest, where he was a visiting assistant professor in the ECE department Prior to that, he was a postdoctoral research associate and assistant research professor in the Remote Sensing Lab at Saint Louis University from 2017 to 2019.

Paheding is an associate editor of the journals, Signal Image and Video Processing (Springer) and Photogrammetric Engineering and Remote Sensing (ASPRS), and topic editor for Remote Sensing. He completed his Ph.D. in electrical engineering at University of Dayton, Ohio.

Computing is a part of my life.

Sidike Paheding, Assistant Professor, College of Computing

Active Research

Title: Cybersecurity Modules Aligned with Undergraduate Computer Science and Engineering Curricula
Sponsor: NSF
PI at Michigan Tech
Duration: July 2020 – June 2022
Total Award: $159,417.00

Research Abstract

This project aims to serve the national interest by improving how cybersecurity concepts are taught in undergraduate computing curricula. The need to design and maintain cyber-secure computing systems is increasingly important. As a result, the future technology workforce must be trained to have a security mindset, so that they consider cybersecurity during rather than after system design.

This project aims to achieve this goal by building plug-and-play, hands-on cybersecurity modules for core courses in Computer Engineering, and Computer Science and Engineering. The modules will align with the curricula recommended by the Association for Computing Machinery and will be designed for easy adoption into computing programs nationwide. Modules will be designed for integration into both introductory and advanced courses, thus helping students develop in-depth understanding of cybersecurity as they progress through their computing curriculum. It is expected that the project will encourage more students to pursue careers or higher degrees in the field of cybersecurity.

Recent Publications

Sidike, P., Sagan, V., Maimaitijiang, M., Maimaitiyiming, M., Shakoor, N., Burken, J., … & Fritschi, F. B. (2019). dPEN: deep Progressively Expanded Neural Network for mapping heterogeneous agricultural landscape using WorldView-3 satellite imagery. Remote Sensing of Environment, 221, 756-772. [Impact Factor: 9.085]

Sidike, P., Asari, V. K., & Sagan, V. (2018). Progressively Expanded Neural Network (PEN Net) for hyperspectral image classification: A new neural network paradigm for remote sensing image analysis. ISPRS journal of photogrammetry and remote sensing, 146, 161-181. [Impact Factor: 7.319]

Sidike, P., Asari, V. K., & Alam, M. S. (2015). Multiclass object detection with single query in hyperspectral imagery using class-associative spectral fringe-adjusted joint transform correlation. IEEE Transactions on Geoscience and Remote Sensing, 54(2), 1196-1208. [Impact Factor: 5.855]

Maimaitijiang, M., Sagan, V., Sidike, P., Hartling, S., Esposito, F., & Fritschi, F. B. (2020). Soybean yield prediction from UAV using multimodal data fusion and deep learning. Remote Sensing of Environment, 237, 111599. [Impact Factor: 9.085]