Day: September 13, 2019

Zhen Liu Co-author of Publication in Cold Regions Science and Technology

Zhen Liu, associate professor of civil and environmental engineering and member of the ICC’s Center for Cyber-Physical Systems (CPS), is co-author of the article, “A multivariate freezing-thawing depth prediction model for spring load restriction,” which was published August 6, 2019, in the journal Cold Regions Science and Technology, which is published by Elsevier. Co-authors of the article are Ting Bio and John Bland.

Abstract: Road damages induced by heavily loaded truck traffic during the spring thaw are a major road distress in cold regions. To minimize these damages, Spring Load Restriction (SLR) is widely applied in the U.S., Canada, and other countries during the early thawing season by controlling the movement of freight-carrying trucks and heavy equipment travel until the thawing ends. Most SLR policies rely on the Freezing Depth (FD) and Thawing Depth (TD), especially the latter one. Therefore, accurate predictions of FD and TD are important to prevent both the extensive damage to the pavement due to the late placement or early removal of SLR and the economic loss of road users due to an unnecessarily long SLR period. Here, we propose a new multivariate model for predicting FD and TD in support of SLR decision-making. The model gives a curving surface of FD and TD in a 3-dimensional space, instead of 2-dimensional in traditional methods, by considering both the freezing and thawing indices in the entire freeze-thaw cycle. For model evaluations, yearly field data measured at five typical sites from 104 sites in Michigan were adopted. The evaluation results showed that the proposed model is accurate in predicting FD and TD for most sites. Compared to the previous TD predictions in the existing study, the TD predictions with the proposed model have been significantly improved. In addition, this study provides field data that have not been reported earlier in the literature and that can be used for validating other prediction models. The reported work is ready for practice for roadways in cold regions to support SLR decision-making.

https://digitalcommons.mtu.edu/michigantech-p/406

Citation: Bao, T., Liu, Z., & Bland, J. (2019). A multivariate freezing-thawing depth prediction model for spring load restriction. Cold Regions Science and Technology, 167.http://dx.doi.org/10.1016/j.coldregions.2019.102856

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Tim Havens Quoted in The Enterprise Project Blog

Timothy Havens

Timothy Havens (CC/ICC), the William and Gloria Jackson Associate Professor of Computer Systems and director of the Institute of Computing and Cybersystems (ICC), was quoted extensively in the article “How to make a career switch into AI: 8 tips,” which was published September 5, 2019, on The Enterprisers Project blog.

https://enterprisersproject.com/article/2019/9/ai-career-path-how-make-switch

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Bo Chen Receives $250K NSF Award for Mobile PDE Systems Research

Bo Chen, CS

Bo Chen, assistant professor of computer science and member of the Institute of Computing and Cybersystems Center for  Cybersecurity, is the principal investigator on a project that has received a $249,918 research and development grant from the National Science Foundation. The project is entitled, “SaTC: CORE: Small: Collaborative: Hardware-Assisted Plausibly Deniable System for Mobile Devices.” This is a potential three-year project.

Abstract: Mobile computing devices typically use encryption to protect sensitive information. However, traditional encryption systems used in mobile devices cannot defend against an active attacker who can force the mobile device owner to disclose the key used for decrypting the sensitive information. This is particularly of concern to dissident users who are targets of nation states. An example of this would be a human rights worker collecting evidence of untoward activities in a region of oppression or conflict and storing the same in an encrypted form on the mobile device, and then being coerced to disclose the decryption key by an official. Plausibly Deniable Encryption (PDE) has been proposed to defend against such adversaries who can coerce users into revealing the encrypted sensitive content. However, existing techniques suffer from several problems when used in flash-memory-based mobile devices, such as weak deniability because of the way read/write/erase operations are handled at the operating systems level and at the flash translation layer, various types of side channel attacks, and computation and power limitations of mobile devices. This project investigates a unique opportunity to develop an efficient (low-overhead) and effective (high-deniability) hardware-assisted PDE scheme on mainstream mobile devices that is robust against a multi snapshot adversary. The project includes significant curriculum development activities and outreach activities to K-12 students.

This project fundamentally advances the mobile PDE systems by leveraging existing hardware features such as flash translation layer (FTL) firmware and TrustZone to achieve a high deniability with a low overhead. Specifically, this project develops a PDE system with capabilities to: 1) defend against snapshot attacks using raw flash memory on mobile devices; and 2) eliminate side-channel attacks that compromise deniability; 3) be scalable to deploy on mainstream mobile devices; and 4) efficiently provide usable functions like fast mode switching. This project also develops novel teaching material on PDE and cybersecurity for K-12 students and the Regional Cybersecurity Education Collaboration (RCEC), a new educational partnership on cybersecurity in Michigan.

Publications related to this research:

[DSN ’18] Bing Chang, Fengwei Zhang, Bo Chen, Yingjiu Li, Wen Tao Zhu, Yangguang Tian, Zhan Wang, and Albert Ching. MobiCeal: Towards Secure and Practical Plausibly Deniable Encryption on Mobile Devices. The 48th IEEE/IFIP International Conference on Dependable Systems and Networks (DSN ’18), June 2018 (Acceptance rate: 28%)
[Cybersecurity ’18] Qionglu Zhang, Shijie Jia, Bing Chang, Bo Chen. Ensuring Data Confidentiality via Plausibly Deniable Encryption and Secure Deletion – A Survey. Cybersecurity (2018) 1: 1.
[ComSec ’18 ] Bing Chang, Yao Cheng, Bo Chen, Fengwei Zhang, Wen Tao Zhu, Yingjiu Li, and Zhan Wang. User-Friendly Deniable Storage for Mobile Devices. Elsevier Computers & Security, vol. 72, pp. 163-174, January 2018
[CCS ’17] Shijie Jia, Luning Xia, Bo Chen, and Peng Liu. DEFTL: Implementing Plausibly Deniable Encryption in Flash Translation Layer. 2017 ACM Conference on Computer and Communications Security (CCS ’17), Dallas, Texas, USA, Oct 30 – Nov 3, 2017 (Acceptance rate: 18%)
[ACSAC ’15] Bing Chang, Zhan Wang, Bo Chen, and Fengwei Zhang. MobiPluto: File System Friendly Deniable Storage for Mobile Devices. 2015 Annual Computer Security Applications Conference (ACSAC ’15), Los Angeles, California, USA, December 2015 (Acceptance rate: 24.4%)
[ISC ’14] Xingjie Yu, Bo Chen, Zhan Wang, Bing Chang, Wen Tao Zhu, and Jiwu Jing. MobiHydra: Pragmatic and Multi-Level Plausibly Deniable Encryption Storage for Mobile Devices. The 17th Information Security Conference (ISC ’14), Hong Kong, China, Oct. 2014

Link to more information about this project: https://snp.cs.mtu.edu/research/index.html#pde

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Soner Onder Receives Year One Funding for $1.2M NSF SCALE Project

Soner Onder
Dave Whalley

Soner Onder, professor of computer science, was recently awarded $246,329 for the first year of a four-year NSF grant for his project, “SHF: Medium: Collaborative Research: Statically Controlled Asynchronous Lane Execution (SCALE).” The project is in collaboration with Prof. David Whalley of Florida State University. Michigan Tech is the lead institution in the project, it is expected to total $1.2 million, with Michigan Tech receiving $600,000.

Abstract: Enabling better performing systems benefits applications that span those running on mobile devices to large data applications running on data centers. The efficiency of most applications is still primarily affected by single thread performance. Instruction-level parallelism (ILP) speeds up programs by executing instructions of the program in parallel, with ‘superscalar’ processors achieving maximum performance. At the same time, energy efficiency is a key criteria to keep in mind as such speedup happens, with these two being conflicting criteria in system design. This project develops a Statically Controlled Asynchronous Lane Execution (SCALE) approach that has the potential to meet or exceed the performance of a traditional superscalar processor while approaching the energy efficiency of a very long instruction word (VLIW) processor. As implied by its name, the SCALE approach has the ability to scale to different types and levels of parallelism. The toolset and designs developed in this project will be available as open-source and will also have an impact on both education and research. The SCALE architectural and compiler techniques will be included in undergraduate and graduate curricula.

The SCALE approach supports separate asynchronous execution lanes where dependencies between instructions in different lanes are statically identified by the compiler to provide inter-lane synchronization. Providing distinct lanes of instructions allows the compiler to generate code for different modes of execution to adapt to the type of parallelism that is available at each point within an application. These execution modes include explicit packaging of parallel instructions, parallel and pipelined execution of loop iterations, single program multiple data (SPMD) execution, and independent multi-threading.

This award reflects NSF’s statutory mission and has been deemed worthy of support through evaluation using the Foundation’s intellectual merit and broader impacts review criteria.

https://www.nsf.gov/awardsearch/showAward?AWD_ID=1901005&HistoricalAwards=false

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