Biblio

Physical Unclonable Functions (PUFs) are the secured hardware primitives to authenticate Integrated Circuits (ICs) from various unauthorized attacks. The secured key generation mechanism through PUFs is based on random Process Variations (PVs) inherited by the CMOS transistors. In this paper, we proposed a chaotic-based challenge generation mechanism to feed the arbiter PUFs. The chaotic property is introduced to increase the non-linearity in the arbitration mechanism thereby the uncertainty of the keys is attained. The chaotic sequences are easy to generate, difficult to intercept, and have the additional advantage of being in a large number Challenge-Response Pair (CRP) generation. The proposed design has a significant advantage in key generation with improved uniqueness and diffuseness of 47.33%, and 50.02% respectively. Moreover, the enhancement in the reliability of 96.14% and 95.13% range from −40C to 125C with 10% fluctuations in supply voltage states that it has prominent security assistance to the Internet of Things (IoT) enabled devices against malicious attacks.

A novel secure physical layer key generation method for Connected and Autonomous Vehicles (CAVs) against an attacker is proposed under fading and Additive White Gaussian Noise (AWGN). In the proposed method, a random sequence key is added to the demodulated sequence to generate a unique pre-shared key (PSK) to enhance security. Extensive computer simulation results proved that an attacker cannot extract the same legitimate PSK generated by the received vehicle even if identical fading and AWGN parameters are used both for the legitimate vehicle and attacker.

True Random Number Generator (TRNG) is an important hardware security primitive for system security. TRNGs are capable of providing random bits for initialization vectors in encryption engines, for padding and nonces in authentication protocols and for seeds to pseudo random number generators (PRNG). A TRNG needs to meet the same statistical quality standards as a physical unclonable function (PUF) with regard to randomness and uniqueness, and therefore one can envision a unified architecture for both functions. In this paper, we investigate a FPGA implementation of a TRNG using the Shift-register Reconvergent-Fanout (SiRF) PUF. The SiRF PUF measures path delays as a source of entropy within a engineered logic gate netlist. The delays are measured at high precision using a time-to-digital converter, and then processed into a random bitstring using a series of linear-time mathematical operations. The SiRF PUF algorithm that is used for key generation is reused for the TRNG, with simplifications that improve the bit generation rate of the algorithm. This enables the TRNG to leverage both fixed PUF-based entropy and random noise sources, and makes the TRNG resilient to temperature-voltage attacks. TRNG bitstrings generated from a programmable logic implementation of the SiRF PUF-TRNG on a set of FPGAs are evaluated using statistical testing tools.

Peer review is a widely utilized pedagogical feedback mechanism for engaging students, which has been shown to improve educational outcomes. However, we find limited discussion and empirical measurement of peer review in visualization coursework. In addition to engagement, peer review provides direct and diverse feedback and reinforces recently-learned course concepts through critical evaluation of others’ work. In this paper, we discuss the construction and application of peer review in a computer science visualization course, including: projects that reuse code and visualizations in a feedback-guided, continual improvement process and a peer review rubric to reinforce key course concepts. To measure the effectiveness of the approach, we evaluate student projects, peer review text, and a post-course questionnaire from 3 semesters of mixed undergraduate and graduate courses. The results indicate that course concepts are reinforced with peer review—82% reported learning more because of peer review, and 75% of students recommended continuing it. Finally, we provide a road-map for adapting peer review to other visualization courses to produce more highly engaged students.

Amount and variety of training data drastically affect the performance of CNNs. Thus, annotation methods are becoming more and more critical to collect data efficiently. In this paper, we propose a simple yet efficient Interactive Self-Annotation framework to cut down both time and human labor cost for video object bounding box annotation. Our method is based on recurrent self-supervised learning and consists of two processes: automatic process and interactive process, where the automatic process aims to build a supported detector to speed up the interactive process. In the Automatic Recurrent Annotation, we let an off-the-shelf detector watch unlabeled videos repeatedly to reinforce itself automatically. At each iteration, we utilize the trained model from the previous iteration to generate better pseudo ground-truth bounding boxes than those at the previous iteration, recurrently improving self-supervised training the detector. In the Interactive Recurrent Annotation, we tackle the human-in-the-loop annotation scenario where the detector receives feedback from the human annotator. To this end, we propose a novel Hierarchical Correction module, where the annotated frame-distance binarizedly decreases at each time step, to utilize the strength of CNN for neighbor frames. Experimental results on various video datasets demonstrate the advantages of the proposed framework in generating high-quality annotations while reducing annotation time and human labor costs.

Recent years have witnessed several educational innovations to provide effective and engaging classroom instruction with the integration of immersive interactions based on augmented reality and virtual reality (AR/VR). This paper outlines the development of an ARCore-based application (app) that can impart interactive experiences for hands-on learning in engineering laboratories. The ARCore technology enables a smartphone to sense its environment and detect horizontal and vertical surfaces, thus allowing the smartphone to estimate any position in its workspace. In this mobile app, with touch-based interaction and AR feedback, the user can interact with a wheeled mobile robot and reinforce the concepts of kinematics for a differential drive mobile robot. The user experience is evaluated and system performance is validated through a user study with participants. The assessment shows that the proposed AR interface for interacting with the experimental setup is intuitive, easy to use, exciting, and recommendable.

Trip planning, which targets at planning a trip consisting of several ordered Points of Interest (POIs) under user-provided constraints, has long been treated as an important application for location-based services. The goal of trip planning is to maximize the chance that the users will follow the planned trip while it is difficult to directly quantify and optimize the chance. Conventional methods either leverage statistical analysis to rank POIs to form a trip or generate trips following pre-defined objectives based on constraint programming to bypass such a problem. However, these methods may fail to reflect the complex latent patterns hidden in the human mobility data. On the other hand, though there are a few deep learning-based trip recommendation methods, these methods still cannot handle the time budget constraint so far. To this end, we propose a TIme-aware Neural Trip Planning (TINT) framework to tackle the above challenges. First of all, we devise a novel attention-based encoder-decoder trip generator that can learn the correlations among POIs and generate trips under given constraints. Then, we propose a specially-designed reinforcement learning (RL) paradigm to directly optimize the objective to obtain an optimal trip generator. For this purpose, we introduce a discriminator, which distinguishes the generated trips from real-life trips taken by users, to provide reward signals to optimize the generator. Subsequently, to ensure the feedback from the discriminator is always instructive, we integrate an adversarial learning strategy into the RL paradigm to update the trip generator and the discriminator alternately. Moreover, we devise a novel pre-training schema to speed up the convergence for an efficient training process. Extensive experiments on four real-world datasets validate the effectiveness and efficiency of our framework, which shows that TINT could remarkably outperform the state-of-the-art baselines within short response time.

In this paper, an implementation of a human in the loop (HITL) technique for robot navigation in an indoor environment is described. The HITL technique is integrated into the reinforcement learning algorithms for mobile robot navigation. Reinforcement algorithms, specifically Q-learning and SARSA, are used combined with HITL since these algorithms are good in exploration and navigation. Turtlebot3 has been used as the robot for validating the algorithms by implementing the system using Robot Operating System and Gazebo. The robot-assisted with human feedback was found to be better in navigation task execution when compared to standard algorithms without using human in the loop. This is a work in progress and the next step of this research is exploring other reinforced learning methods and implementing them on a physical robot.

In present research, an empirical model for building and maintaining students' intrinsic motivation to learn is proposed. Unlike many other models of motivation, this model is not based on psychological theories but is derived directly from empirical observations made by experienced learners and educators. Thanks to empirical nature of the proposed model, its application to educational practice may be more straightforward in comparison with assumptions-based motivation theories. Interestingly, the structure of the proposed model resembles to some extent the structure of the oscillator circuit containing an amplifier and a positive feedback loop.

Learning movement control is a fundamental process integral to infant development. However, it is still unclear how infants learn to control leg movement. This work explores the potential of using socially assistive robots to provide real-time adaptive reinforcement learning for infants. Ten 6 to 8-month old typically-developing infants participated in a study where a robot provided reinforcement when the infant’s right leg acceleration fell within the range of 9 to 20 m/s2. If infants increased the proportion of leg accelerations in this band, they were categorized as "performers". Six of the ten participating infants were categorized as performers; the performer subgroup increased the magnitude of acceleration, proportion of target acceleration for right leg, and ratio of right/left leg acceleration peaks within the target acceleration band and their right legs increased movement intensity from the baseline to the contingency session. The results showed infants specifically adjusted their right leg acceleration in response to a robot- provided reward. Further study is needed to understand how to improve human-robot interaction policies for personalized interventions for young infants.

Recent scientific results show that audio feedback associated with body movements can be fundamental during the development to learn new spatial concepts [1], [2]. Within the weDraw project [3], [4], we have investigated how this link can be useful to learn mathematical concepts. Here we present a study investigating how mathematical skills changes after multisensory training based on human-computer interaction (RobotAngle and BodyFraction activities). We show that embodied angle and fractions exploration associated with audio and visual feedback can be used in typical children to improve cognition of spatial mathematical concepts. We finally present the exploitation of our results: an online, optimized version of one of the tested activity to be used at school. The training result suggests that audio and visual feedback associated with body movements is informative for spatial learning and reinforces the idea that spatial representation development is based on sensory-motor interactions.

In recent years, the use of the Internet of Things (IoT) has increased rapidly in different areas. Due to many IoT applications, many limitations have emerged such as power consumption and limited resources. The security of connected devices is becoming more and more a primary need for the reliability of systems. Among other things, power consumption remains an essential constraint with a major impact on the quality of the encryption system. For these, several lightweight cryptography algorithms were proposed and developed. The PRESENT algorithm is one of the lightweight block cipher algorithms that has been proposed for a highly restrictive application. In this paper, we have proposed an efficient hardware serial architecture that uses 16 bits for data path encryption. It uses fewer FPGA resources and achieves higher throughput compared to other existing hardware applications.

Scan-based test methodology is one of the most popular test techniques in VLSI circuits. This methodology increases the testability which in turn improves the fault coverage. For this purpose, the technique uses a chain of scan cells. This becomes a source of attack for an attacker who can observe / control the internal states and use the information for malicious purposes. Hence, security becomes the main concern in the Integrated Circuit (IC) domain since scan chains are the main reason for leakage of confidential information during testing phase. These leakages will help attackers in reverse engineering. Measures against such attacks have to be taken by encrypting the data which flows through the scan chains. Lightweight ciphers can be used for scan chain encryption. In this work, encryption of scan data is done for ISCAS-89 benchmarks and the performance and security properties are evaluated. Lightweight stream and block ciphers are used to perform scan encryption. A comparative analysis between the two techniques is performed in par with the functions related to design cost and security properties.

Recent years have witnessed impressive advances in technology which led to the rapid growth of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs) using numerous low-powered devices with a huge number of actuators and sensors. These devices gather and exchange data over the internet and generate enormous amounts of data needed to be secured. Although traditional cryptography provides an efficient means of addressing device and communication confidentiality, integrity, and authenticity issues, it may not be appropriate for very resource-constrained systems, particularly for end-nodes such as a simply connected sensor. Thus, there is an ascent need to use lightweight cryptography (LWC) providing the needed level of security with less complexity, area and energy overhead. In this paper, four lightweight cryptographic algorithms called PRESENT, LED, Piccolo, and SPARX were implemented over a Contiki-based IoT operating system, dedicated for IoT platforms, and assessed regarding RAM and ROM usage, power and energy consumption, and CPU cycles number. The Cooja network simulator is used in this study to determine the best lightweight algorithms to use in IoT applications utilizing wireless sensor networks technology.

With the development of the Internet of Things (IoT), the demand for lightweight cipher came into being. At the same time, the security of lightweight cipher has attracted more and more attention. FESH algorithm is a lightweight cipher proposed in 2019. Relevant studies have proved that it has strong ability to resist differential attack and linear attack, but its research on resisting side-channel attack is still blank. In this paper, we first introduce a correlation power analysis for FESH algorithm and prove its effectiveness by experiments. Then we propose a mask scheme for FESH algorithm, and prove the security of the mask. According to the experimental results, protected FESH only costs 8.6%, 72.3%, 16.7% of extra time, code and RAM.

This paper explores high throughput architectures for the substitution modules, which are an integral component of encryption algorithms. The security algorithms chosen belong to the category of lightweight crypto-primitives suitable for pervasive computing. The focus of this work is on the implementation of encryption algorithms on hardware platforms to improve speed and facilitate optimization in the area and power consumption of the design. In this work, the architecture for the encryption algorithms' substitution box (S-box) is modified using switching circuits (i.e., MUX-based) along with a logic generator and included in the overall cipher design. The modified architectures exhibit high throughput and consume less energy in comparison to the state-of-the-art designs. The percentage increase in throughput or maximum frequency differs according to the chosen algorithms discussed elaborately in this paper. The evaluation of various metrics specific to the design are executed at RFID-specific frequency so that they can be deployed in an IoT environment. The designs are mainly simulated and compared on Nexys4 DDR FPGA platform, along with a few other FPGAs, to meet similar design and implementation environments for a fair comparison. The application of the proposed S-box modification is explored for the healthcare scenario with promising results.

In this paper, two lightweight cryptography methods were introduced and developed on hardware. The PRESENT lightweight block cipher, and the DM-PRESENT lightweight hash function were implemented on Intel FPGA. The PRESENT core with 64-bit block data and 80-bit data key consumes 2,945 logic element, 1,824 registers, and 273,408 memory bits. Meanwhile, the DM-PRESENT core with 64-bit input and 80-bit key consumes 2,336 logic element, 1,380 registers, and 273,408 memory bits. The PRESENT core with 128-bit key and DM-PRESENT based on this core were also implemented. These cores were simulated for functional verification and embedded in NIOS II for implementation possibility on hardware. They consumed less logic resources and power consumption compared with conventional cryptography methods.

Lightweight cryptography is a novel diversion from conventional cryptography that targets internet-of-things (IoT) platform due to resource constraints. In comparison, it offers smaller cryptographic primitives such as shorter key sizes, block sizes and lesser energy drainage. The main focus can be seen in algorithm developments in this emerging subject. Thus, verification is carried out based upon theoretical (mathematical) proofs mostly. Among the few available side-channel analysis studies found in literature, the highest percentage is taken by power attacks. PRESENT is a promising lightweight block cipher to be included in IoT devices in the near future. Thus, the emphasis of this paper is on lightweight cryptology, and our investigation shows unavailability of a correlation electromagnetic analysis (CEMA) of it. Hence, in an effort to fill in this research gap, we opted to investigate the capabilities of CEMA against the PRESENT algorithm. This work aims to determine the probability of secret key leakage with a minimum number of electromagnetic (EM) waveforms possible. The process initially started from a simple EM analysis (SEMA) and gradually enhanced up to a CEMA. This paper presents our methodology in attack modelling, current results that indicate a probability of leaking seven bytes of the key and upcoming plans for optimisation. In addition, introductions to lightweight cryptanalysis and theories of EMA are also included.

The design of efficient and secure cryptographic algorithms is a fundamental problem of cryptography. Due to the tight cost and constrained resources devices such as Radio-Frequency IDentification (RFID), wireless sensors, smart cards, health-care devices, lightweight cryptography has received a great deal of attention. Recent research mainly focused on designing optimized cryptographic algorithms which trade offs between security performance, time consuming, energy consumption and cost. In this paper, we present two chaotic stream ciphers based on chaos and we report the results of a comparative performance evaluation study. Compared to other crypto-systems of the literature, we demonstrate that our designed stream ciphers are suitable for practical secure applications of the Internet of Things (IoT) in a constrained resource environment.

In the context of the Internet of Things (IoT), lightweight block ciphers are of vital importance. Due to the nature of the devices involved, traditional security solutions can add overhead and perhaps inhibit the application's objective due to resource limits. Lightweight cryptography is a novel suite of ciphers that aims to provide hardware-constrained devices with a high level of security while maintaining a low physical cost and high performance. In this paper, we are going to evaluate the performance of some of the recently proposed lightweight block ciphers (GIFT-COFB, Romulus, and TinyJAMBU) on the Arduino Due. We analyze data on each algorithm's performance using four metrics: average encryption and decryption execution time; throughput; power consumption; and memory utilization. Among our chosen ciphers, we find that TinyJAMBU and GIFT-COFB are excellent choices for resource-constrained IoT devices.

Fruit-80, an ultra-lightweight stream cipher with 80-bit secret key, is oriented toward resource constrained devices in the Internet of Things. In this paper, we propose area and speed optimization architectures of Fruit-80 on FPGAs. The area optimization architecture reuses NFSR&LFSR feedback functions and achieves the most suitable ratio of look-up-tables and flip-flops. The speed optimization architecture adopts a hybrid approach for parallelization and reduces the latency of long data paths by pre-generating primary feedback and inserting flip-flops. In conclusion, the optimal throughput-to-area ratio of the speed optimization architecture is better than that of Grain v1. The area optimization architecture occupies only 35 slices on Xilinx Spartan-3 FPGA, smaller than that of Grain and other common stream ciphers. To the best of our knowledge, this result sets a new record of the minimum area in lightweight cipher implementations on FPGA.

In the process of compiling the power-cut window period of the power grid equipment maintenance plan, problems such as omission of constraints are prone to occur due to excessive reliance on manual experience. In response to these problems, this paper proposes a method for mining key features of the power-cut window based on grey relational analysis. Through mining and analysis of the historical operation data of the power grid, the operation data of new energy, and the historical power-cut information of equipment, the indicators that play a key role in the arrangement of the outage window period of the equipment maintenance plan are found. Then use the key indicator information to formulate the window period. By mining the relationship between power grid operation data and equipment power outages, this paper can give full play to the big data advantages of the power grid, improve the accuracy and efficiency of the power-cut window period.

Ant Colony Optimization is applied to design a suitable and shortest route between the starting node point and the end node point in the Wireless Sensor Network (WSN). In general ant colony algorithm plays a good role in path planning process that can also applied in improving the network security. Therefore to protect the network from the malicious nodes an ACO based Dynamic Key Management (ACO-DKM) scheme is proposed. The routes are diagnosed through ACO method also the actual coverage distance and pheromone updating strategy is updated simultaneously that prevents the node from continuous monitoring. Simulation analysis gives the efficiency of the proposed scheme.

With the ever-increasing use of large-scale IoT networks in different sectors of the industry, it has become critical to realise seamless and secure communication between devices in the network. Realising secure group communication in the IoT requires solving the problem of group-key establishment. In this work, we solve the problem by designing a new lattice-based Key Encapsulation Mechanism (KEM) for resource-constrained devices that enable the distribution of a symmetric key or any other data between all the devices in a given network. This is achieved by coupling multiple private keys to a unique public key. Moreover, we present a proof-of-concept implementation based on the GGH algorithm. The results show it is feasible to use lattice-based cryptography to allow for seamless and secure group communications within a decentralised IoT network. It has been bench-marked against other common post-quantum constructs and proven to be more practical with respect to memory consumption and security, although considerably slower due to lack of optimisation in the implementation.

There is an increase in interest and necessity for an interoperable and efficient railway network across Europe, creating a key distribution problem between train and trackside entities’ key management centres (KMC). Train and trackside entities establish a secure session using symmetric keys (KMAC) loaded beforehand by their respective KMC using procedures that are not scalable and prone to operational mistakes. A single system would simplify the KMAC distribution between KMCs; nevertheless, it is difficult to place the responsibility for such a system for the whole European area within one central organization. A single system could also expose relationships between KMCs, revealing information, such as plans to use an alternative route or serve a new region, jeopardizing competitive advantage. This paper proposes a scalable and decentralised key management system that allows KMC to share cryptographic keys using transactions while keeping relationships anonymous. Using non-interactive proofs of knowledge and assigning each entity a private and public key, private key owners can issue valid transactions while all system actors can validate them. Our performance analysis shows that the proposed system is scalable when a proof of concept is implemented with settings close to the expected railway landscape in 2030.