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Deep Learning for Password Guessing and Password Strength Evaluation, A Survey

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Deep Learning for Password Guessing and Password Strength Ev...

IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Tao Zhang Zelei Cheng Yi Qin Qiang Li Lin Shi Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis Guangxi University for Nationalities Nanning China Department of Computer and Information Technology Purdue University West Lafayette USA PLA Information Engineering University Zhengzhou China Henan Key Laboratory of Network Cryptography Technology Zhengzhou China Cloud Computing and Big Data Research Institute China Academy of Information and Communications Technology Beijing China

ISBN: (数字)9781665403924

ISBN: (纸本)9781665403931

Text passwords are the most widely used authentication methods and will also be used in the future. Text passwords can be regarded as meaningful strings, and deep learning methods have an advantage of text processing. LSTM, RNN, GAN and other deep learning models have been using in password guessing and password strength measurements. In the paper, we make a survey on state-of-the-art deep learning methods for password guessing and password strength evaluation, including password pattern extraction, candidate password generation and password strength measurement. Compared with traditional methods, neural networks based methods can achieve better results and performance.

关键词： Deep learning Training Transfer learning Security Gallium nitride Password Text processing

Object detection in specific traffic scenes using YOLOv2

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arXiv 2019年

作者： Wang, Shouyu Tang, Weitao School of Engineering Technology Purdue University West LafayetteIN47907 United States School of Computer and Information Technology Purdue University West LafayetteIN47907 United States

—Real-time object detection framework plays crucial role in autonomous driving. In this paper, we introduce the real-time object detection framework called You Only Look Once (YOLOv1) and the related improvements of YOLOv2. We further explore the capability of YOLOv2 by implementing its pre-trained model to do the object detecting tasks in some specific traffic scenes. The four artificially designed traffic scenes include single-car, single-person, frontperson-rearcar and frontcar-rearperson. YOLOv2 model performs well in detecting single-car once scope of camera distance is within 60 feet. For detecting single person, YOLOv2 handle most of situations when camera distance is within 50 feet. For detecting both person and car, YOLOv2 always detect unsuccessfully if person overlaps with car in photo. In frontcar-rearperson scenario, the "success rate of prediction" goes down sharply in 50 feet camera distance which is shorter than the same situation in frontperson-rearcar scenario. Copyright © 2019, The Authors. All rights reserved.

关键词： Object detection

Smart Cloud: Scalable cloud robotic architecture for web-powered multi-robot applications

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arXiv 2019年

作者： Penmetcha, Manoj Kannan, Shyam Sundar Min, Byung-Cheol SMART Lab Department of Computer and Information Technology Purdue University West LafayetteIN47907 United States

Robots have inherently limited onboard processing, storage, and power capabilities. Cloud computing resources have the potential to provide significant advantages for robots in many applications. However, to make use of these resources, frameworks must be developed that facilitate robot interactions with cloud services. In this paper, we propose a cloud-based architecture called Smart Cloud that intends to overcome the physical limitations of single- or multi-robot systems through massively parallel computation, provided on demand by cloud services. Smart Cloud is implemented on Amazon Web Services (AWS) and available for robots running on the Robot Operating System (ROS) and on non-ROS systems. Smart Cloud features a first-of-its-kind architecture that incorporates JavaScript-based libraries to run various robotic applications related to machine learning and other methods. This paper presents the architecture and its performance in terms of CPU power usage, latency, and security, and finally validates it for navigation and machine learning applications. Copyright © 2019, The Authors. All rights reserved.

关键词： Web services

The development of a cybersecurity competency list for the internet of things (IoT) 22

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The development of a cybersecurity competency list for the i...

22nd World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2018

作者： Poe, La'reshia Dittman, Kevin Computer and Information Technology Purdue University West Lafayette Indiana47907 United States

ISBN: (纸本)9781941763810

The purpose of this study is to identify core competencies that current and future Internet of Things (IoT) cybersecurity professionals will need to possess. This research combines historical and future predictions from authors who have published public research. The researcher combined the most common security competencies from past research and security forecasts. The proposed IoT cybersecurity competency list includes such topics as: Network Security and Telecommunications, information Systems Operations, and Strategic Cybersecurity Management. The Internet of Things (IoT) specific cybersecurity competencies include: IoT Architecture, IoT protocols, and applications of IoT. A survey on the IoT architecture reveals that a basic understanding of an IoT structural design should be understood before implementing security practices. The IoT environment introduces many unfamiliar technologies to security professionals. Finally, a thorough understanding of IoT basic devices, protocols and environments must be understood prior to implementing the appropriate security design. Copyright 2018. © by the International Institute of Informatics and Systemics.

关键词： Internet of things

A reconfigurable quantum local area network over deployed fiber

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arXiv 2021年

作者： Alshowkan, Muneer Williams, Brian P. Evans, Philip G. Rao, Nageswara S.V. Simmerman, Emma M. Lu, Hsuan-Hao Lingaraju, Navin B. Weiner, Andrew M. Marvinney, Claire E. Pai, Yun-Yi Lawrie, Benjamin J. Peters, Nicholas A. Lukens, Joseph M. Quantum Information Science Group Oak Ridge National Laboratory Oak RidgeTN37831 United States Autonomous and Complex Systems Group Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Applied Physics Stanford University StanfordCA94305 United States School of Electrical and Computer Engineering Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Materials Science and Technology Division Oak Ridge National Laboratory Oak RidgeTN37831 United States

Practical quantum networking architectures are crucial for scaling the connection of quantum resources. Yet quantum network testbeds have thus far underutilized the full capabilities of modern lightwave communications, such as flexible-grid bandwidth allocation. In this work, we implement flex-grid entanglement distribution in a deployed network for the first time, connecting nodes in three distinct campus buildings time-synchronized via the Global Positioning System (GPS). We quantify the quality of the distributed polarization entanglement via log-negativity, which offers a generic metric of link performance in entangled bits per second. After demonstrating successful entanglement distribution for two allocations of our eight dynamically reconfigurable channels, we demonstrate remote state preparation—the first realization on deployed fiber—showcasing one possible quantum protocol enabled by the distributed entanglement network. Our results realize an advanced paradigm for managing entanglement resources in quantum networks of ever-increasing complexity and service demands. Copyright © 2021, The Authors. All rights reserved.

关键词： Quantum entanglement

Case Study of an ACM Celebration in Indiana

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Case Study of an ACM Celebration in Indiana

Research in Equity and Sustained Participation in Engineering, Computing, and technology (RESPECT)

作者： Gloria Childress Townsend Alka Harriger Department of Computer Science DePauw University Greencastle IN 46135 USA Computer and Information Technology Department Purdue University West Lafayette IN 47907 USA

ISBN: (纸本)9781728108223

In 2004, organizers in Indiana held the first ACM (Association for Computing Machinery [2]) regional conference for women in computing. ACM-W (ACM's Council on Women in Computing [3]) refers to the conferences as Celebrations, and the Indiana Celebration - called InWIC (Indiana Celebration of Women in Computing) [8] - spawned almost 30 world-wide Celebrations in academic year 2018-2019. This paper presents data collected from attendees of InWIC 2014, InWIC 2015, and InWic 2016. Accompanying analyses of these data comprise the first published multi-year study of a single Celebration.

关键词： gender issues gender issue Indiana Women Case studies

Dynamic Bird Detection Using Image Processing and Neural Network

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Dynamic Bird Detection Using Image Processing and Neural Net...

International Conference on Robot Intelligence technology and Applications (RiTA)

作者： Jeongjin Jo Junwon Park Jinyoung Han Minsun Lee Anthony H. Smith Division of Computer Convergence Chungnam National University Daejeon Republic of Korea Department of Computer and Information Technology Purdue University W. Lafayette IN USA

Collisions of aircraft and birds cause serious flight accidents, and various studies are underway to find a solution to the problem. In recent image recognition studies, state-of-the-art deep learning technologies have been actively applied. This paper proposes image preprocessing and bird detection methods in all dynamic environments using Convolutional Neural Network (CNN) technology. Image preprocessing separates moving creatures from the dynamic background and removes the background. When image preprocessing is complete, the image of the moving object remaining in the frame is used as input data for the learning model to determine whether the bird is in the frame. We used the Inception -v3 neural network model to improve the accuracy of small object classifications.

关键词： Birds Image color analysis Atmospheric modeling Colored noise Predictive models Data models Vegetation

Shared-Control Teleoperation Paradigms on a Soft-Growing Robot Manipulator

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arXiv 2021年

作者： Stroppa, Fabio Selvaggio, Mario Agharese, Nathaniel Luo, Ming Blumenschein, Laura H. Hawkes, Elliot W. Okamura, Allison M. Computer Engineering Department Kadir Has University Istanbul34083 Turkey Electrical Engineering and Information Technology Department University of Naples Federico II Naples80125 Italy Mechanical Engineering Department Stanford University StanfordCA94305 United States School of Mechanical and Materials Engineering Washington State University PullmanWA99163 United States School of Mechanical Engineering Purdue University West LafayetteIN47907 United States Mechanical Engineering Department UC Santa Barbara Santa BarbaraCA93105 United States

Semi-autonomous telerobotic systems allow both humans and robots to exploit their strengths while enabling personalized execution of a remote task. For soft robots with kinematic structures dissimilar to those of human operators, it is unknown how the allocation of control between the human and the robot changes the performance. This work presents a set of interaction paradigms between a human and a remote soft-growing robot manipulator, with demonstrations in both real and simulated scenarios. The soft robot can grow and retract by eversion and inversion of its tubular body, a property we exploit in the interaction paradigms. We implemented and tested six different human-robot interaction paradigms, with full teleoperation at one extreme and gradually adding autonomy to various aspects of the task execution. All paradigms are demonstrated by two experts and two naive operators. Results show that humans and the soft robot manipulator can effectively split their control along different degrees of freedom while acting simultaneously to accomplish a task. In the simple pick-and-place task studied in this work, performance improves as the control is gradually given to the robot’s autonomy, especially when the robot can correct certain human errors. However, human engagement is maximized when the control over a task is at least partially shared. Finally, when the human operator is assisted by haptic guidance, which is computed based on soft robot tip position errors, we observed that the improvement in performance is dependent on the expertise of the human operator. Copyright © 2021, The Authors. All rights reserved.

关键词： Remote control

Quantum emitters in aluminum nitride induced by heavy ion irradiation

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APL Quantum

APL Quantum 2024年第3期1卷

作者： Alexander Senichev Zachariah O. Martin Yongqiang Wang Owen M. Matthiessen Alexei Lagutchev Han Htoon Alexandra Boltasseva Vladimir M. Shalaev Elmore Family School of Electrical and Computer Engineering Birck Nanotechnology Center and Purdue Quantum Science and Engineering Institute Purdue University 610 Purdue Mall West Lafayette Indiana 47907 USA Quantum Science Center Department of Energy A National Quantum Information Science Research Center of the U.S. Oak Ridge National Laboratory 1 Bethel Valley Road Oak Ridge Tennessee 37830 USA Materials Science and Technology Division Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Materials Physics and Applications Division Los Alamos National Laboratory Los Alamos New Mexico 87545 USA

The integration of solid-state single-photon sources with foundry-compatible photonic platforms is crucial for practical and scalable quantum photonic applications. This study explores aluminum nitride (AlN) as a material with properties highly suitable for integrated on-chip photonics and the ability to host defect-center related single-photon emitters. We have conducted a comprehensive analysis of the creation of single-photon emitters in AlN, utilizing heavy ion irradiation and thermal annealing techniques. Subsequently, we have performed a detailed analysis of their photophysical properties. Guided by theoretical predictions, we assessed the potential of Zirconium (Zr) ions to create optically addressable spin defects and employed Krypton (Kr) ions as an alternative to target lattice defects without inducing chemical doping effects. With a 532 nm excitation wavelength, we found that single-photon emitters induced by ion irradiation were primarily associated with vacancy-type defects in the AlN lattice for both Zr and Kr ions. The density of these emitters increased with ion fluence, and there was an optimal value that resulted in a high density of emitters with low AlN background fluorescence. Under a shorter excitation wavelength of 405 nm, Zr-irradiated AlN exhibited isolated point-like emitters with fluorescence in the spectral range theoretically predicted for spin-defects. However, similar defects emitting in the same spectral range were also observed in AlN irradiated with Kr ions as well as in as-grown AlN with intrinsic defects. This result is supportive of the earlier theoretical predictions, but at the same time highlights the difficulties in identifying the sought-after quantum emitters with interesting properties related to the incorporation of Zr ions into the AlN lattice by fluorescence alone. The results of this study largely contribute to the field of creating quantum emitters in AlN by ion irradiation and direct future studies emphasizing the ne

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