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1. P-HIP: A Lightweight and Privacy-Aware Host Identity Protocol for Internet of Things

   https://doi.org/10.1109/JIOT.2020.3009024  

   Hossain, Mahmud ; Hasan, Ragib ( July 2020 , IEEE Internet of Things Journal)

   null (Ed.)

   The Host Identity Protocol (HIP) has emerged as the most suitable solution to uniquely identify smart devices in the mobile and distributed Internet of Things (IoT) systems, such as smart cities, homes, cars, and healthcare. The HIP provides authentication methods that enable secure communications between HIP peers. However, the authentication methods provided by the HIP cannot be adopted by the IoT devices with limited processing power because of the computation-intensive cryptographic operations involved in hash generation, signature validation, and session key establishment. Moreover, IoT devices cannot utilize the HIP as is to communicate securely in the low power and lossy networks as there is a considerable communication overhead, such as packet fragmentation and reassembly, for exchanging certificates over a lossy link. Additionally, the use of static host identifiers makes IoT devices vulnerable to cyber espionage and user-targeted attacks. In this article, we propose an authentication scheme, P-HIP, that protects the identity privacy of an IoT device by enabling the device to compute and use unique host identifiers from networks to networks and sessions to sessions. To make the HIP suitable for resource-constrained IoT devices, P-HIP provides methods that unburden IoT devices from computation-intensive operations, such as modular exponentiation, involved in authentication and session-key exchange. Additionally, P-HIP minimizes the communication overheads for exchanging certificates in lossy networks. We implement a prototype of P-HIP on Contiki enabled IoT that shows P-HIP can reduce computation costs, communication overheads, and the session-key establishment time when used by low-powered devices in a lossy network. 

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2. Embodied mixed reality with passive haptics in STEM education: randomized control study with chemistry titration

   https://doi.org/10.3389/frvir.2023.1047833  

   Johnson-Glenberg, Mina C. ; Yu, Christine S. ; Liu, Frank ; Amador, Charles ; Bao, Yueming ; Yu, Shufan ; LiKamWa, Robert ( July 2023 , Frontiers in Virtual Reality)

   Researchers, educators, and multimedia designers need to better understand how mixing physical tangible objects with virtual experiences affects learning and science identity. In this novel study, a 3D-printed tangible that is an accurate facsimile of the sort of expensive glassware that chemists use in real laboratories is tethered to a laptop with a digitized lesson. Interactive educational content is increasingly being placed online, it is important to understand the educational boundary conditions associated with passive haptics and 3D-printed manipulables. Cost-effective printed objects would be particularly welcome in rural and low Socio-Economic (SES) classrooms. A Mixed Reality (MR) experience was created that used a physical 3D-printed haptic burette to control a computer-based chemistry titration experiment. This randomized control trial study with 136 college students had two conditions: 1) low-embodied control (using keyboard arrows), and 2) high-embodied experimental (physically turning a valve/stopcock on the 3D-printed burette). Although both groups displayed similar significant gains on the declarative knowledge test, deeper analyses revealed nuanced Aptitude by Treatment Interactions (ATIs). These interactionsfavored the high-embodied experimental group that used the MR devicefor both titration-specific posttest knowledge questions and for science efficacy and science identity. Those students with higher prior science knowledge displayed higher titration knowledge scores after using the experimental 3D-printed haptic device. A multi-modal linguistic and gesture analysis revealed that during recall the experimental participants used the stopcock-turning gesture significantly more often, and their recalls created a significantly different Epistemic Network Analysis (ENA). ENA is a type of 2D projection of the recall data, stronger connections were seen in the high embodied group mainly centering on the key hand-turning gesture. Instructors and designers should consider the multi-modal and multi-dimensional nature of the user interface, and how the addition of another sensory-based learning signal (haptics) might differentially affect lower prior knowledge students. One hypothesis is that haptically manipulating novel devices during learning may create more cognitive load. For low prior knowledge students, it may be advantageous for them to begin learning content on a more ubiquitous interface (e.g., keyboard) before moving them to more novel, multi-modal MR devices/interfaces.

    

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3. Behavioral Fingerprinting of IoT Devices

   https://doi.org/10.1145/3266444.3266452  

   Bezawada, Bruhadeshwar ; Bachani, Maalvika ; Peterson, Jordan ; Shirazi, Hossein ; Ray, Indrakshi ; Ray, Indrajit ( October 2018 , Proceedings of the 2018 Workshop on Attacks and Solutions in Hardware Security)

   The Internet-of-Things (IoT) has brought in new challenges in device identification --what the device is, and authentication --is the device the one it claims to be. Traditionally, the authentication problem is solved by means of a cryptographic protocol. However, the computational complexity of cryptographic protocols and/or problems related to key management, render almost all cryptography based authentication protocols impractical for IoT. The problem of device identification is, on the other hand, sadly neglected. Almost always an artificially created identity is softly associated with the device. We believe that device fingerprinting can be used to solve both these problems effectively. In this work, we present a methodology to perform IoT device behavioral fingerprinting that can be employed to undertake strong device identification. A device behavior is approximated using features extracted from the network traffic of the device. These features are used to train a machine learning model that can be used to detect similar device-types. We validate our approach using five-fold cross validation; we report a identification rate of 93-100 and a mean accuracy of 99%, across all our experiments. Furthermore, we show preliminary results for fingerprinting device categories, i.e., identifying different devices having similar functionality. 

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4. Weighted Group Decision Making Using Multi-identity Physical Unclonable Functions

   Bu, Lake ; Kinsy, Michel A. ( August 2018 , 28th International Conference on Field Programmable Logic and Applications (FPL))

   To enable next-generation distributed and connected computing systems, we must address the context-aware chip authentication challenge. An important remaining gap in the design of these systems is the enabling of multi-personality authentication to support applications or schemes requiring a single device to own manifold legitimate identities. In this work, we propose a Multi-identity Physical Unclonable Function (Mi-PUF) assisted weighted group decision making scheme. The Mi-PUF approach enables individual devices to be authenticated and associated with multiple identities in order to hold different number of ballots. Hence, devices with higher impact in a decision making network will have more weight than the less influential ones. Besides the introduction of the scheme, the design and FPGA implementation details of the Mi-PUF are explored and presented. 

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5. Behavioral fingerprinting of Internet‐of‐Things devices

   https://doi.org/10.1002/widm.1337  

   Bezawada, Bruhadeshwar ; Ray, Indrakshi ; Ray, Indrajit ( September 2019 , WIREs Data Mining and Knowledge Discovery)

   Rapid advances in the Internet‐of‐Things (IoT) domain have led to the development of several useful and interesting devices that have enhanced the quality of home living and industrial automation. The vulnerabilities in the IoT devices have rendered them susceptible to compromise and forgery. The problem of device authentication, that is, the question of whether a device's identity is what it claims to be, is still an open problem. Device fingerprinting seems to be a promising authentication mechanism. Device fingerprinting profiles a device based on information available about the device and generate a robust, verifiable and unique identity for the device. Existing approaches for device fingerprinting may not be feasible or cost‐effective for the IoT domain due to the resource constraints and heterogeneity of the IoT devices. Due to resource and cost constraints, behavioral fingerprinting provides promising directions for fingerprinting IoT devices. Behavioral fingerprinting allows security researchers to understand the behavioral profile of a device and to establish some guidelines regarding the device operations. In this article, we discuss existing approaches for behavioral fingerprinting of devices in general and evaluate their applicability for IoT devices. Furthermore, we discuss potential approaches for fingerprinting IoT devices and give an overview of some of the preliminary attempts to fingerprint IoT devices. We conclude by highlighting the future research directions for fingerprinting in the IoT domain.

   This article is categorized under:

   Application Areas > Science and Technology

   Application Areas > Internet

   Technologies > Machine Learning

   Application Areas > Industry Specific Applications

    

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