The MTU RedTeam ranked 13th out of 162 teams in a recent 24-hour Cybar OSINT Capture The Flag (CTF) cybersecurity competition. The team finished tied for 5th place, having completed all the challenges presented by the competition.
Students on the team were Trevor Hornsby (Software Engineering), Shane Hoppe (Computer Science), Matthew Chau (Cybersecurity), Steven Whitaker (Electrical Engineering), and Sankalp Shastry (Electrical Engineering).
Professor Yu Cai, Applied Computing, and Assistant Professor Bo Chen, Computer Science, are advisor and co-advisor of RedTeam, respectively. Both are members of the ICC’s Center for Cybersecurity.
RedTeam promotes a security-driven mindset among Michigan Tech students and provides a community and resource for those wishing to learn more about information security. The RedTeam competes in National Cyber League (NCL) competitions, a great way for students to gain competency in cybersecurity tools and boost their resumes.
RedTeam is on Slack at mturedteam.slack.com. Interested students can sign up with a Michigan Tech email. View past RedTeam presentations here.
This OSINT CTF is non-theoretical and contestants work in teams of up to four members to crowdsource the collection of OSINT to assist law enforcement in generating new leads on missing persons.
The contest runs as a Capture the Flag (CTF) format where contestants must collect various “flags” which equate to points. Since the each flag submitted is treated as potential “net new intelligence”, Trace Labs has a team of volunteers known as “Judges” who validate each submission and award points if the flag meets the category requirements. At the end of each CTF, the team with the most points on the scoreboard wins.
Researcher: Jianhui Yue, PI, Assistant Professor, Computer Science
Sponsor: National Science Foundation, SHF: Small: Collaborative Research
Amount of Support: $192, 716
Duration of Support: 3 years
Abstract: Emerging nonvolatile memory (NVM) technologies, such as PCM, STT-RAM, and memristors, provide not only byte-addressability, low-latency reads and writes comparable to DRAM, but also persistent writes and potentially large storage capacity like an SSD. These advantages make NVM likely to be next-generation fast persistent storage for massive data, referred to as in-memory storage. Yet, NVM-based storage has two challenges: (1) Memory cells have limited write endurance (i.e., the total number of program/erase cycles per cell); (2) NVM has to remain in a consistent state in the event of a system crash or power loss. The goal of this project is to develop an efficient in-memory storage framework that addresses these two challenges. This project will take a holistic approach, spanning from low-level architecture design to high-level OS management, to optimize the reliability, performance, and manageability of in-memory storage. The technical approach will involve understanding the implication and impact of the write endurance issue when cutting-edge NVM is adopted into storage systems. The improved understanding will motivate and aid the design of cost-effective methods to improve the life-time of in-memory storage and to achieve efficient and reliable consistence maintenance.
Publications:
Pai Chen, Jianhui Yue, Xiaofei Liao, Hai Jin. “Optimizing DRAM Cache by a Trade-off between Hit Rate and Hit Latency,” IEEE Transactions on Emerging Topics in Computing, 2018. doi:10.1109/TETC.2018.2800721
Chenlei Tang, Jiguang Wan, Yifeng Zhu, Zhiyuan Liu, Peng Xu, Fei Wu and Changsheng Xie. “RAFS: A RAID-Aware File System to Reduce Parity Update Overhead for SSD RAID,” Design Automation Test In Europe Conference (DATE) 2019, 2019.
Pai Chen, Jianhui Yue, Xiaofei Liao, Hai Jin. “Trade-off between Hit Rate and Hit Latency for Optimizing DRAM Cache,” IEEE Transactions on Emerging Topics in Computing, 2018.