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    Undergrad Summer Lab Positions: Autonomous Driving Research


    Dr. Xiaoyong (Brian) Yuan, Applied Computing and Computer Science, is seeking several hourly paid undergraduate students to work in the areas of autonomous driving.

    The project is funded by MTU Research Excellence Fund (REF) and expected to begin in summer 2021 (7/1/2021).

    Preferred Qualifications

    • Passion for research in autonomous driving and machine learning
    • Solid programming skills in C, Python, Java, or related programming languages
    • Familiar with Linux OS

    To Apply

    To apply, please send a resume and a transcript to Dr. Yuan (xyyuan@mtu.edu).

    Dr. Yuan is a member of the Data Sciences and Cybersecurity research groups of the Institute of Computing and Cybersystems (ICC).


    Conference on Applied Cryptography: Call for Participation


    The 2021 EAI International Conference on Applied Cryptography in Computer and Communications (AC3 2021) takes place May 15-16, 2021.

    Register for the virtual conference here.

    Dr. Bo Chen, Computer Science, founding general chair of the new EAI conference, says the conference has brought together researchers, developers and practitioners from around the world who will focus on, discuss, and explore the area of applied cryptography in computer and communication systems.

    Conference Topics

    Conference topics include all aspects of applied cryptography, including symmetric cryptography, public-key cryptography, cryptographic protocols, cryptographic implementations, cryptographic standards and practices, as well as using cryptography to solve real-world problems.

    Technical Program

    The AC3 2021 technical program includes four main conference tracks at which 11 papers will be presented virtually in oral presentations.

    • Track 1 – Blockchain
    • Track 2 – Authentication
    • Track 3 – Secure Computation
    • 4 – Practical Crypto Application. Aside from the high-quality technical paper presentations, the technical program also features two keynote speeches, and one technical workshop.

    Keynotes

    The two keynote speeches will be delivered by Prof. Kui Ren (ACM Fellow, IEEE Fellow), Zhejiang University, China; and IEEE Fellow Prof. Robert Deng, Singapore Management University.

    Workshop

    A workshop, the First International Workshop on Security for Internet of Things (IOTS 2021), includes four technical papers which aim to develop cryptographic techniques for ensuring the IoT security. The conference, originally planned to be held in Xiamen China, was moved it online for the health and safety of participants.


    Register to participate in the virtual conference here. Use the “Sign up for free access to the livestream” option.


    European Alliance for Innovation (EAI) is an international professional community and a nonprofit organization. The goal of EAI is to empower the global ICT research and innovation community, and to promote cooperation between European and International ICT communities.

    EAI Conferences span the globe with opportunities to meet, explore, and contribute to the world of ICT research. With 100+ annual events (including MobiQuitous, SecureComm, etc.), EAI is one of the world’s most prolific scientific communities.

    EAI Conferences are published via Springer’s LNICST and EAI’s EUDL, and they are indexed in all leading indexing services, including EI, ISI, Scopus, CrossRef, Google Scholar, dblp, MAS, EBSCO, Microsoft Academic Search, CiteSeerX, and more.


    Sidike Paheding, AC, Awarded R-D Grant by Purdue University


    Sidike Paheding (AC/ICC) is the principal investigator on a project that has received a $19,037 research and development grant from Purdue University. The two-year project is titled, “Cybersecurity Modules Aligned with Undergraduate Computer Science and Engineering Curricula.”

    The project aims to serve the national interest by improving how cybersecurity concepts are taught in undergraduate computing curricula.

    The grant is a sub-award of a $159,417 Purdue University NSF project . View that project here.


    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.

    The project will examine how the modules may be best integrated into existing curricula and the effects of the modules on student learning and interest in cybersecurity. Assessment will leverage several methods including (a) a task load index to quantify rigor, (b) surveys to gain insight into the development of students’ security mindset and perceptions of cybersecurity, and (c) analysis of learning using analytical course rubrics. Deliverables of this project will include a suite of plug-and-play cybersecurity modules for Computer Engineering and Computer Science and Engineering courses that span from introductory to advanced levels and that meet standards for content breadth and depth. The results will be disseminated through publications, presentations, press releases, and social media to ensure that project outcomes are shared widely. The NSF Improving Undergraduate STEM Education: Education and Human Resources Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.


    Chinmay Kondekar, MS in Electrical Engineering Graduate, 2021

    By Karen S. Johnson, Communications Director, College of Computing

    Graduate student Chinmay Kondekar heard about Michigan Tech during his undergraduate studies. Sometime later he read a social media post about work opportunities in the robotic and automation labs, and Michigan Tech again came to his attention.

    “At that time, I was working as a controls engineer in India,” he says. “Robotics and automation interest me, and when I saw who had written the post (a former graduate student of Sergeyev’s), I knew I had found the perfect degree program.”

    Kondekar’s final design project was to create an interconnected system that is flexible, reconfigurable, and controlled from a central control interface to emulate a production process.

    “We decided on machining as the process because it is tricky to program and one of the more challenging applications for an industrial robot,” he says.

    The system has a number of industrial applications. “Most of the robotic work cells in the industry have similar control and communication layout,” Kondekar confirms.

    “The data generated from the project has helped me to create lab manuals on interconnected systems,” Kondekar adds. “The system has potential applications in data acquisition and analytics, cybersecurity, and future projects requiring interconnected systems.”

    The system is a result of combining multiple components that are controlled from a central interface by a method called systems integration. Similar manufacturing system layouts can be commonly found in the automotive, pharma, and food industry.

    The system is used to machine different patterns on a block of foam using various robotic attachments. Correct dimensions are assured using machine vision, and by transporting the workpiece between different stations.

    What sparked Kondekar’s interest in creating the system was the challenge presented by the hardware and software interfacing required, which is accomplished through hands-on work and software programming, which he enjoys immensely.

    “I enjoy solving problems and coming up with a solution to make things work,” he shares. “When starting the project, I had a lot of unknown variables but I knew how to approach them and, eventually, I came up with solutions and made the system work. It’s highly rewarding to watch the finished system come together, and then to see it work automatically after pressing just three buttons.”

    Kondekar had some background knowledge going into the project, gained during his employment as a controls engineer. In that position, he worked on boiler, turbine, pharma, and automotive automation verticals, making “the PLC part of the project easy.”

    His background in electrical engineering also made the controls and wiring easy. “But I had to learn robotics and electro-pneumatics from scratch, as I had never worked on either of them,” he says.

    Kondekar’s project would not have been possible without generous support from Mark Gauthier and his team at Donald Engineering. “Mark has helped the department acquire the best industry-grade hardware, and his expertise in pneumatics helped the project concept become reality,” Kondekar says.

    Kondekar has worked as a teaching assistant, an instructor for high school students and engineering undergrads, and a student researcher for Professor Aleks Sergeyev, Applied Computing.

    “Aleks has been a wonderful mentor and a great advisor,” Kondekar says. “I love his vision and his approach towards automation and robotics. I will definitely miss working with him, and I look forward to opportunities to work with him again.”

    “Chinmay is a very knowledgeable student with a great work ethic,” says Sergeyev. “Through his study and research, he acquired all the needed skills to become a very successful contributor to the industry. I certainly enjoyed working with him”

    For the next few years, Kondekar sees himself working in the automation and controls industry for systems integrator companies. He’ll soon start a controls engineer position with Patti Engineering, Auburn Hills, Mich. Research work has been interesting for him, and he says he would consider a PhD opportunity in the future.

    Professor David Labyak (MMET) helped Kondekar with the machining aspect of his project. “He is one of the best teachers I have ever had,” he says. “I would look forward to working with him in the future, as well.”

    During his high school teaching experiences—for a local mechatronics program—he worked with Professor John Irwin (MMET), whom he also identifies as a mentor. “I like his approach towards mechanical and mechatronics education, and would like to work with him in the future,” Kondekar says of Irwin.

    Kondekar graduates this spring with his master of science in electrical engineering. He completed a bachelor’s in engineering in electrical engineering at University of Pune, India, in 2017. In 2019, he completed a Michigan Tech certificate in FANUC handling tool operations and material handling.

    He says he has enjoyed his learning and life experiences at Michigan Tech. Plus, he loves the outdoors. “I am an outdoors guy and I love the UP, especially the summers. It’s full of good people and great beer!”



    Husky Innovate Students Win Top Prizes in New Venture Online Competition

    by Husky Innovate

    For the 11th year running, Central Michigan University and Michigan Tech collaborated to offer Tech students a chance to compete at CMU’s New Venture Competition. 2021 marked the second year the pitch competition was held online as the New Venture Online Competition (NVOC).

    Despite the challenges of a pandemic and a virtual platform, our students persevered, honed their pitches and won top prizes. This year’s NVOC winners were also winners at the 2021 Bob Mark Business Model Pitch Competition held at Tech in January. All of their hard work and effort paid off!

    Congratulations to this year’s MTU winners:

    • In the 2020-track 10-minute pitch category, Team Focus with Ranit Karmakar won the Best Overall Venture Award for $25,000. Watch Karmakar’s pitch.
    • In the two-minute pitch category, Team The Fitting Room with Jordan Craven won third place for $1,000. Watch Craven’s pitch.
    • Team Recirculate with Hunter Malinowski won an honorable mention award for $750. Watch Malinowski’s pitch.

    Read more in the NVOC 2021 Booklet.


    Jidong Xiao, Boise State University, to Present Talk May 12


    Jidong Xiao, an assistant professor in the computer science department at Boise State University, will present a talk on Wednesday, May 12, at 3:00 p.m.

    Dr. Xiao’s research focuses on computer security, especially computer system security and cloud security.

    In his talk, “Identifying New Threats in Cloud Environments,” Dr. Xiao will present two research projects focusing on a concept called virtual machine extrospection and a new type of rootkit, which allows attackers to perform active or passive attacks in a nested virtualization environment.

    Join the virtual talk here.

    Talk Title

    Identifying New Threats in Cloud Environments

    Talk Abstract

    Cloud computing has become prevalent over the past decade. While individuals and organizations rely on cloud computing more and more, various security problems in cloud platforms are discovered. In this talk, I will present two research projects. In the first project, I will present a concept called virtual machine extrospection, in which attackers or cloud customers collect sensitive information about the physical machine from within a virtual machine. In the second project, I will present a new type of rootkit, which allows attackers to perform active or passive attacks in a nested virtualization environment, and then I will describe our detection approach. At the end of the talk, I will briefly discuss my future research projects and plans.

    Biography

    Dr. Jidong Xiao is an assistant professor in the computer science department at Boise State University. His research focuses on computer security, especially computer system security and cloud security. He received his PhD degree in computer science from the College of William and Mary. Prior to joining Boise State University, he spent approximately 5 years in industry working at Intel and Symantec.

    Dr. Xiao’s research was recognized in different venues, including publications that won the best paper award at the USENIX Large Installation System Administration Conference (LISA) 2015, won the distinguished poster award at the Network and Distributed System Security Symposium (NDSS) 2016, and won the best paper award nomination at the International Conference on Dependable Systems and Networks (DSN) 2020. Dr. Xiao has been awarded several grants by the NSF, NSA, and the Army Research Office (ARO).


    Nathir Rawashdeh Publishes Paper at SPIE Conference

    Nathir Rawashdeh (AC) led the publication of a paper at the recent online SPIE Defense + Commercial Sensing / Autonomous Systems 2021 Conference.

    The paper, entitled “Drivable path detection using CNN sensor fusion for autonomous driving in the snow,” targets the problem of drivable path detection in poor weather conditions including on snow-covered roads. The authors used artificial intelligence to perform camera, radar and LiDAR sensor fusion to detect a drivable path for a passenger car on snow-covered streets. A companion video is available. 

    Co-authors include Jeremy Bos (ECE).


    Jinging Yao, NJ Inst. of Technology, to Present Talk May 11


    Jingjing Yao, a PhD candidate at New Jersey Institute of Technology, will present a talk on Tuesday, May 11, at 3:00 p.m,.

    In her talk, “Intelligent and Secure Fog-Aided Internet of Drones,” Yao discusses the utilization of energy harvesting technology to charge drone batteries and investigate wireless power control to adjust the drone wireless transmission power to reduce drone energy consumption.

    Yao’s research interests include Internet of Things (IoT), Internet of Drones (IoD), Deep Reinforcement Learning, Federated Learning, Cybersecurity, Mobile Edge Computing/Caching, and Energy Harvesting.

    Join the virtual talk here.

    Talk Title

    Intelligent and Secure Fog-Aided Internet of Drones

    Talk Abstract

    Internet of drones (IoD), which deploys several drones in the air to collect ground information and send them to the IoD gateway for further processing, can be applied in traffic surveillance and disaster rescue. Fog-aided IoD provisions future events prediction and image classification by machine learning technologies, where massive training data are collected by drones and analyzed in the fog node. However, the performance of IoD is greatly affected by drones’ battery capacities. Also, aggregating all data in the fog node may incur huge network traffic and drone data privacy leakage. The speaker will share her vision and research to address these two challenges.

    In this talk, the speaker utilizes energy harvesting technology to charge drone batteries and investigate wireless power control to adjust the drone wireless transmission power to reduce drone energy consumption. The joint optimization of power control and energy harvesting scheduling is investigated in time-varying IoD networks to minimize the long-term average system energy cost constrained by the drone battery capacities and quality of service (QoS) requirements. A modified actor-critic deep reinforcement learning algorithm is designed to address the joint optimization problem in time-varying IoD networks.

    To prevent the privacy leakage of IoD, the speaker utilizes federated learning (FL) by performing local training in drones and sharing all training model parameters in the fog node without uploading drone raw data. However, drone privacy can still be divulged to ground eavesdroppers by wiretapping and analyzing uploaded parameters during the FL training process. The power control problem is hence investigated to maximize the FL system security rate constrained by drone battery capacities and the FL training time requirement. An algorithm with low computational complexity is then designed to tackle the security rate maximization problem and its performance is demonstrated by extensive simulations.

    Biography

    Jingjing Yao is a Ph.D. candidate in Computer Engineering with the Department of Electrical and Computer Engineering at the New Jersey Institute of Technology (NJIT). She will receive her Ph.D. degree from NJIT in May 2021.

    She received the M.E. degree in Information and Communication Engineering from the University of Science and Technology of China (USTC), and the B.E. degree in Information and Communication Engineering from the Dalian University of Technology (DUT).

    She has published 13 first-author journal articles and 7 first-author conference papers. Her research interests include Internet of Things (IoT), Internet of Drones (IoD), Deep Reinforcement Learning, Federated Learning, Cybersecurity, Mobile Edge Computing/Caching, and Energy Harvesting.


    Tara Salman, Washington Univ., to Present Talk April 27

    Tara Salman, a final-year PhD candidate at Washington University in St. Louis, will present a talk on Tuesday, April 27, 2021, at 3:00 p.m.

    In her talk, “A Collaborative Knowledge-Based Security Solution using Blockchains,” she will present her work on redesigning the blockchains and building a collaborative, distributed, intelligent, and hostile solution that can be used for security purposes.

    Talk Title

    A Collaborative Knowledge-Based Security Solution using Blockchains

    Talk Abstract

    Artificial intelligence and machine learning have recently gained wide adaptation in building intelligent yet simple and proactive security solutions such as intrusion identification, malware detection, and threat intelligence. With the increased risk and severity of cyber-attacks and the distributed nature of modern threats and vulnerabilities, it becomes critical to pose a distributed intelligent solution that evaluates the systems’ and networks’ security collaboratively. Blockchain, as a decade-old successful distributed ledger technology, has the potential to build such collaborative solutions. However, to be used for such solutions, the technology needs to be extended so that it can intelligently process the stored information and achieve a collective decision about security risks or threats that might target a system.

    In this talk, I will present our work on redesigning the blockchains and build a collaborative, distributed, intelligent, and hostile solution that can be used for security purposes. In particular, we will discuss our work on (1) extending blockchains for general collaborative decision-making applications, where knowledge should be made out of decisions, risks, or any information stored on the blockchain; (2) applying the proposed extensions to security applications such as malware detection and threat intelligence.

    Biography

    Tara Salman is a final year Ph.D. candidate at Washington University in St. Louis, where she is advised by Raj Jain. She previously received her MS and BSc degrees from Qatar University in 2015 and 2012, respectively. Her research aims to integrate state-of-the-art technologies to provide scalable, collaborative, and intelligent cybersecurity solutions.

    Her recent work focuses on the intersection of artificial intelligence, blockchains, and security applications. The work spans several fields, including blockchain technology, security, machine learning, and deep learning applications, cloud computing, and the Internet of Things. She has been selected for the EECS Rising Star in UC Berkeley 2020. Her research has been published in more than twenty internationally recognized conferences and journals and supported by national and international funds.


    Xinyu Lei, Michigan State, to Present Talk April 29

    Xinyu Lei, a Ph.D. candidate in the Department of Computer Science and Engineering at Michigan State University, will present a talk on Thursday, April 29, 2021, at 3:00 p.m.

    In his talk, “Secure and Efficient Queries Processing in Cloud Computing,” Lei will discuss his work developing new techniques to support secure and efficient queries processing in cloud storage.

    Title

    Secure and Efficient Queries Processing in Cloud Computing

    Abstract

    With the advent of cloud computing, data owners are motivated to outsource their databases to the commercial public cloud for storage. The public cloud with database-as-a-service (DBaaS) model has many benefits (including lower cost, better performance, and higher flexibility). However, hosting the datasets on the commercial public cloud deprives the data owners’ direct control over their databases, which brings in security concerns. For example, the corrupted cloud employees may spy the data owner’s commercial valuable databases and sell them for money. To protect data privacy, the sensitive database must be encrypted before outsourcing to the cloud. However, it becomes hard to perform efficient queries (e.g., keyword query) processing over the encrypted database.

    In this talk, I focus on developing new techniques to support secure and efficient queries processing in cloud storage. An index-aid approach is proposed to address the problem. In my approach, the data items are formally encrypted, and a secure index is generated for efficient queries processing. The cloud can perform queries directly over the secure index rather than the encrypted data items. The secure index is constructed based on a new data structure named random Bloom filter. Then, multiple random Bloom filters are organized into a binary tree structure to support fast query processing. The proposed approach can achieve data privacy, index privacy, search token privacy, and fast query processing simultaneously.

    Biography

    Xinyu Lei is a Ph.D. candidate in the Department of Computer science and Engineering at Michigan State University. He once worked as a research assistant in Texas A&M University at Qatar and Ford Motor Company. He has research interests in cybersecurity problems in different computer systems (including IoT, blockchain, cloud computing). He has published 20+ papers with 600+ citations. His work has been published on top-tier conferences and journals such as ACM MobiSys, ACM CodaSpy, IEEE ICDE, IEEE ICDCS, etc.