Day: May 20, 2020

Michigan Tech Ranks 22nd in “Cyber Power” Top 100

NCL Logo

Twenty-one Michigan Tech students on three teams finished the spring 2020 semester with impressive success at a recent National Cybersecurity League (NCL) competition. All three teams ranked in the top 100, out of 925 teams nationwide. Assistant Professor Bo Chen, Computer Science, is the faculty advisor to the teams.

Michigan Tech’s overall “Cyber Power Ranking” is 22nd nationally and 6th in the central region, as of Spring 2020. Schools are ranked based on their top team performance, their top student’s individual performance, and the aggregate individual performance of their students.

Team 1 ranked 16th in a field of 925 teams; with Alex Larkin (CS), Jack Bergman (CS), Jon Preuth (CS), Trevor Hornsby (Software), Shane Hoppe, Dakoda Patterson (CS), and Matthew Chau (Cyber).

Team 2 ranked 45th among 925 teams; with Sophia Kraus (EE), Sam Breuer (EE), Ian Hughes (Cyber/CS), Austin Doorlag (CS), Sankalp Shastry, Hunter Indermuehle (EE), and Samantha Christie (CS).

Team 3 ranked 78th of 925; with John Claassen (CS), Stu Kernstock (Cyber), Jacson Ott (Cyber), Bradley Gipson (CNSA), Ethan Frenza (CNSA), Tim Lucero (Cyber), and Anders Jacobsen (EE).

Shane Hoppe was ranked 95th among 5,357 participants in the NCL individual game.

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.

Cyber Skyline logo

Havens, Yazdanparast Publish Article in IEEE Transactions on Big Data

An article by Audrey Yazdanparast (2019, PhD, Electrical Engineering) and Dr. Timothy Havens, “Linear Time Community Detection by a Novel Modularity Gain Acceleration in Label Propagation,” has been accepted for publication in the journal, IEEE Transactions on Big Data.

The paper presents an efficient approach for detecting self-similar communities in weighted graphs, with applications in social network analysis, online commodity recommendation systems, user clustering, biology, communications network analysis, etc.

Paper Abstract: Community detection is an important problem in complex network analysis. Among numerous approaches for community detection, label propagation (LP) has attracted a lot of attention. LP selects the optimum community (i.e., label) of a network vertex by optimizing an objective function (e.g., Newman’s modularity) subject to the available labels in the vicinity of the vertex. In this paper, a novel analysis of Newman’s modularity gain with respect to label transitions in graphs is presented. Here, we propose a new form of Newman’s modularity gain calculation that quantifies available label transitions for any LP based community detection.

The proposed approach is called Modularity Gain Acceleration (MGA) and is simplified and divided into two components, the local and global sum-weights. The Local Sum-Weight (LSW) is the component with lower complexity and is calculated for each candidate label transition. The General Sum-Weight (GSW) is more computationally complex, and is calculated only once per each label. GSW is updated by leveraging a simple process for each node-label transition, instead of for all available labels. The MGA approach leads to significant efficiency improvements by reducing time consumption up to 85% relative to the original algorithms with the exact same quality in terms of modularity value which is highly valuable in analyses of big data sets.

Timothy Havens is director of Michigan Tech’s Institute of Computing and Cybersystems (ICC), the associate dean for research for the College of Computing , and the William and Gloria Jackson Associate Professor of Computer Systems.

View the article abstract here.