Biblio

This paper describes a prototype of a novel Permanent Magnetic Elastomer (PME) sheet based skin sensor for robotic applications. Its working principle is to use a Hall effect transducer to measure the change of magnetic field. PME is a polymer that has Neodymium particles distributed inside it, after strong magnetization for anisotropy, the PME acquires strong remanent magnetization that can be comparable to that of a permanent magnet, in this work, we made improvement of the strength of the magnetic field of PME, so it achieved magnetic strength as high as 25 mT when there is no deformation. When external forces apply on the sensor, the deformation of PME causes a change in the magnetic field due to the change in the alignment of the magnetic particles. Compared with other soft magnetic sensors that employ similar technology, we implemented linear regression method to simplify the calibration, so we focus on the point right above the magnetometer. An MLX90393 chip is installed at the bottom of the PME as the magnetometer. Experimental results show that it can measure forces from 0.01–10 N. Calibration is confirmed effective even for shear directions when the surface of PME is less than 15 x 15 mm.

Based on the analysis of material performance data management requirements, a network-sharing scheme of material performance data is proposed. A material performance database system including material performance data collection, data query, data analysis, data visualization, data security management and control modules is designed to solve the problems of existing material performance database network sharing, data fusion and multidisciplinary support, and intelligent services Inadequate standardization and data security control. This paper adopts hierarchical access control strategy. After logging into the material performance database system, users can standardize the material performance data and store them to form a shared material performance database. The standardized material performance data of the database system shall be queried and shared under control according to the authority. Then, the database system compares and analyzes the material performance data obtained from controlled query sharing. Finally, the database system visualizes the shared results of controlled queries and the comparative analysis results obtained. The database system adopts the MVC architecture based on B/S (client/server) cross platform J2EE. The Third-party computing platforms are integrated in System. Users can easily use material performance data and related services through browsers and networks. MongoDB database is used for data storage, supporting distributed storage and efficient query.

It is proposed to address existing methodological issues in the educational process with the development of intellectual technologies and knowledge representation systems to improve the efficiency of higher education institutions. For this purpose, the structure of relational database is proposed, it will store the information about defended dissertations in the form of a set of attributes (heuristics), representing the mandatory qualification attributes of theses. An inference algorithm is proposed to process the information. This algorithm represents an artificial intelligence, its work is aimed at generating queries based on the applicant preferences. The result of the algorithm's work will be a set of choices, presented in ranked order. Given technologies will allow applicants to quickly become familiar with known scientific results and serve as a starting point for new research. The demand for co-researcher practice in solving the problem of updating the projective thinking methodology and managing the scientific research process has been justified. This article pays attention to the existing parallels between the concepts of technical and human sciences in the framework of their convergence. The concepts of being (economic good and economic utility) and the concepts of consciousness (humanitarian economic good and humanitarian economic utility) are used to form projective thinking. They form direct and inverse correspondences of technology and humanitarian practice in the techno-humanitarian mathematical space. It is proposed to place processed information from the language of context-free formal grammar dissertation abstracts in this space. The principle of data manipulation based on formal languages with context-free grammar allows to create new structures of subject areas in terms of applicants' preferences.It is believed that the success of applicants’ work depends directly on the cognitive training of applicants, which needs to be practiced psychologically. This practice is based on deepening the objectivity and adequacy qualities of obtaining information on the basis of heuristic methods. It requires increased attention and development of intelligence. The paper studies the use of heuristic methods by applicants to find new research directions leads to several promising results. These results can be perceived as potential options in future research. This contributes to an increase in the level of retention of higher education professionals.

The outsourcing of databases is very popular among IT companies and industries. It acts as a solution for businesses to ensure availability of the data for their users. The solution of outsourcing the database is to encrypt the data in a form where the database service provider can perform relational operations over the encrypted database. At the same time, the associated security risk of data leakage prevents many potential industries from deploying it. In this paper, we present a secure outsourcing database search scheme (BASDB) with the use of a smart contract for search operation over index of encrypted database and storing encrypted relational database in the cloud. Our proposed scheme BASDB is a simple and practical solution for effective search on encrypted relations and is well resistant to information leakage against attacks like search and access pattern leakage.

Recommenders are central in many applications today. The most effective recommendation schemes, such as those based on collaborative filtering (CF), exploit similarities between user profiles to make recommendations, but potentially expose private data. Federated learning and decentralized learning systems address this by letting the data stay on user's machines to preserve privacy: each user performs the training on local data and only the model parameters are shared. However, sharing the model parameters across the network may still yield privacy breaches. In this paper, we present Rex, the first enclave-based decentralized CF recommender. Rex exploits Trusted execution environments (TEE), such as Intel software guard extensions (SGX), that provide shielded environments within the processor to improve convergence while preserving privacy. Firstly, Rex enables raw data sharing, which ultimately speeds up convergence and reduces the network load. Secondly, Rex fully preserves privacy. We analyze the impact of raw data sharing in both deep neural network (DNN) and matrix factorization (MF) recommenders and showcase the benefits of trusted environments in a full-fledged implementation of Rex. Our experimental results demonstrate that through raw data sharing, Rex significantly decreases the training time by 18.3 x and the network load by 2 orders of magnitude over standard decentralized approaches that share only parameters, while fully protecting privacy by leveraging trustworthy hardware enclaves with very little overhead.

Ransomware is an emerging threat that imposed a \$ 5 billion loss in 2017, rose to \$ 20 billion in 2021, and is predicted to hit \$ 256 billion in 2031. While initially targeting PC (client) platforms, ransomware recently leaped over to server-side databases-starting in January 2017 with the MongoDB Apocalypse attack and continuing in 2020 with 85,000 MySQL instances ransomed. Previous research developed countermeasures against client-side ransomware. However, the problem of server-side database ransomware has received little attention so far. In our work, we aim to bridge this gap and present DIMAQS (Dynamic Identification of Malicious Query Sequences), a novel anti-ransomware solution for databases. DIMAQS performs runtime monitoring of incoming queries and pattern matching using two classification approaches (Colored Petri Nets (CPNs) and Deep Neural Networks (DNNs)) for attack detection. Our system design exhibits several novel techniques like dynamic color generation to efficiently detect malicious query sequences globally (i.e., without limiting detection to distinct user connections). Our proof-of-concept and ready-to-use implementation targets MySQL servers. The evaluation shows high efficiency without false negatives for both approaches and a false positive rate of nearly 0%. Both classifiers show very moderate performance overheads below 6%. We will publish our data sets and implementation, allowing the community to reproduce our tests and results.

Cybersecurity is important in the field of information technology. One most recent pressing issue is information security. When we think of cybersecurity, the first thing that comes to mind is cyber-attacks, which are on the rise, such as Ransomware. Various governments and businesses take a variety of measures to combat cybercrime. People are still concerned about ransomware, despite numerous cybersecurity precautions. In ransomware, the attacker encrypts the victim’s files/data and demands payment to unlock the data. Cybersecurity is a collection of tools, regulations, security guards, security ideas, guidelines, risk management, activities, training, insurance, best practices, and technology used to secure the cyber environment, organization, and user assets. This paper analyses ransomware attacks, techniques for dealing with these attacks, and future challenges.

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.

An often overlooked but equally important aspect of unmanned aerial system (UAS) design is the security of their networking protocols and how they deal with cyberattacks. In this context, cyberattacks are malicious attempts to monitor or modify incoming and outgoing data from the system. These attacks could target anywhere in the system where a transfer of data occurs but are most common in the transfer of data between the control station and the UAS. A compromise in the networking system of a UAS could result in a variety of issues including increased network latency between the control station and the UAS, temporary loss of control over the UAS, or a complete loss of the UAS. A complete loss of the system could result in the UAS being disabled, crashing, or the attacker overtaking command and control of the platform, all of which would be done with little to no alert to the operator. Fortunately, the majority of higher-end, enterprise, and government UAS platforms are aware of these threats and take actions to mitigate them. However, as the consumer market continues to grow and prices continue to drop, network security may be overlooked or ignored in favor of producing the lowest cost product possible. Additionally, these commercial off-the-shelf UAS often use uniform, standardized frequency bands, autopilots, and security measures, meaning a cyberattack could be developed to affect a wide variety of models with minimal changes. This paper will focus on a low-cost educational-use UAS and test its resilience to a variety of cyberattack methods, including man-in-the-middle attacks, spoofing of data, and distributed denial-of-service attacks. Following this experiment will be a discussion of current cybersecurity practices for counteracting these attacks and how they can be applied onboard a UAS. Although in this case the cyberattacks were tested against a simpler platform, the methods discussed are applicable to any UAS platform attempting to defend against such cyberattack methods.

Power system robustness against high-impact low probability events is becoming a major concern. To depict distinct phases of a system response during these disturbances, an irregular polygon model is derived from the conventional trapezoid model and the model is analytically investigated for transmission system performance, based on which resiliency metrics are developed for the same. Furthermore, the system resiliency to windstorms is evaluated on the IEEE reliability test system (RTS) by performing steady-state and dynamic security assessment incorporating protection modelling and corrective action schemes using the Power System Simulator for Engineering (PSS®E) software. Based on the results of steady-state and dynamic analysis, modified resiliency metrics are quantified. Finally, this paper quantifies the interdependency of operational and infrastructure resiliency as they cannot be considered discrete characteristics of the system.

Resiliency of cyber-physical systems (CPSs) against malicious attacks has been a topic of active research in the past decade due to widely recognized importance. Resilient CPS is capable of tolerating some attacks, operating at a reduced capacity with core functions maintained, and failing gracefully to avoid any catastrophic consequences. Existing work includes an architecture for hierarchical control systems, which is a subset of CPS with wide applicability, that is tailored for resiliency. Namely, the architecture consists of local, network and supervision layers and features such as simplex structure, resource isolation by hypervisors, redundant sensors/actuators, and software defined network capabilities. Existing work also includes methods of ensuring a level of resiliency at each one of the layers, respectively. However, for a holistic system level resiliency, individual methods at each layers must be coordinated in their deployment because all three layers interact for the operation of CPS. For this purpose, a resiliency coordinator for CPS is proposed in this work. The resiliency coordinator is the interconnection of central resiliency coordinator in the supervision layer, network resiliency coordinator in the network layer, and finally, local resiliency coordinators in multiple physical systems that compose the physical layer. We show, by examples, the operation of the resiliency coordinator and illustrate that RC accomplishes a level of attack resiliency greater than the sum of resiliency at each one of the layers separately.

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.

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.

The recent experience in the use of virtual reality (VR) technology has shown that users prefer Electromyography (EMG) sensor-based controllers over hand controllers. The results presented in this paper show the potential of EMG-based controllers, in particular the Myo armband, to identify a computer system user. In the first scenario, we train various classifiers with 25 keyboard typing movements for training and test with 75. The results with a 1-dimensional convolutional neural network indicate that we are able to identify the user with an accuracy of 93% by analyzing only the EMG data from the Myo armband. When we use 75 moves for training, accuracy increases to 96.45% after cross-validation.

User authentication based on muscle tension manifested during password typing seems to be an interesting additional layer of security. It represents another way of verifying a person’s identity, for example in the context of continuous verification. In order to explore the possibilities of such authentication method, it was necessary to create a capturing software that records and stores data from EMG (electromyography) sensors, enabling a subsequent analysis of the recorded data to verify the relevance of the method. The work presented here is devoted to the design, implementation and evaluation of such a solution. The solution consists of a protocol and a software application for collecting multimodal data when typing on a keyboard. Myo armbands on both forearms are used to capture EMG and inertial data while additional modalities are collected from a keyboard and a camera. The user experience evaluation of the solution is presented, too.

Kerberos protocol is a derivative type of server used for the authentication purpose. Kerberos is a network-based authentication protocol which communicates the tickets from one network to another in a secured manner. Kerberos protocol encrypts the messages and provides mutual authentication. Kerberos uses the symmetric cryptography which uses the public key to strengthen the data confidentiality. The KDS Key Distribution System gives the center of securing the messages. Kerberos has certain disadvantages as it provides public key at both ends. In this proposed approach, the Kerberos are secured by using the HMAC Hash-based Message Authentication Code which is used for the authentication of message for integrity and authentication purpose. It verifies the data by authentication, verifies the e-mail address and message integrity. The computer network and security are authenticated by verifying the user or client. These messages which are transmitted and delivered have to be integrated by authenticating it. Kerberos authentication is used for the verification of a host or user. Authentication is based on the tickets on credentials in a secured way. Kerberos gives faster authentication and uses the unique ticketing system. It supports the authentication delegation with faster efficiency. These encrypt the standard by encrypting the tickets to pass the information.

The prevalence of mobile devices (smartphones) along with the availability of high-speed internet access world-wide resulted in a wide variety of mobile applications that carry a large amount of confidential information. Although popular mobile operating systems such as iOS and Android constantly increase their defenses methods, data shows that the number of intrusions and attacks using mobile applications is rising continuously. Experts use techniques to detect malware before the malicious application gets installed, during the runtime or by the network traffic analysis. In this paper, we first present the information about different categories of mobile malware and threats; then, we classify the recent research methods on mobile malware traffic detection.

Driven by the progress of data and compute-intensive methods in various scientific domains, there is an in-creasing demand from researchers working with highly sensitive data to have access to the necessary computational resources to be able to adapt those methods in their respective fields. To satisfy the computing needs of those researchers cost-effectively, it is an open quest to integrate reliable security measures on existing High Performance Computing (HPC) clusters. The fundamental problem with securely working with sensitive data is, that HPC systems are shared systems that are typically trimmed for the highest performance - not for high security. For instance, there are commonly no additional virtualization techniques employed, thus, users typically have access to the host operating system. Since new vulnerabilities are being continuously discovered, solely relying on the traditional Unix permissions is not secure enough. In this paper, we discuss a generic and secure workflow that can be implemented on typical HPC systems allowing users to transfer, store and analyze sensitive data. In our experiments, we see an advantage in the asynchronous execution of IO requests, while reaching 80 % of the ideal performance.

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.

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.

The present industrial scenario requires frequent transfer of data between remote servers and on premise devices and hence the risk of attacks on these data cannot be overlooked. Such security risk is even aggravated in case of sensitive information being compromised due to inefficient security implementations. Various forms of security implementations are being discussed and experimented for the same. With the introduction of devices with better processing capabilities, Public Key Infrastructure is a very popular technique being widely implemented, wherein symmetric and asymmetric key based encryptions are used inorder to secure the data being transferred and it has proven to be an effective technique. The PKI however suffers certain drawbacks and it is evident from the attacks. A system specifically designed for scenarios such as a factory having a centralised device management system requiring multiple devices to communicate and upload data safely to server is being put forward in this paper.