Biblio

System assurance is the justified confidence that a system functions as intended and is free of exploitable vulnerabilities, either intentionally or unintentionally designed or inserted as part of the system at any time during the life cycle. The computation and communication backbone of Internet of Things (IoT) devices and other cyber-physical systems (CPS) makes them vulnerable to classes of threats previously not relevant for many physical control and computational systems. The design of resilient IoT systems encompasses vulnerabilities to adversarial disruption (Security), behavior in an operational environments (Function), and increasing interdependencies (Connectedness). System assurance can be met only through a comprehensive and aggressive systems engineering approach. Engineering methods to "design in" security have been explored in the United States through two separate research programs, one through the Systems Engineering Research Center (SERC) and one through the Defense Advanced Research Process Agency (DARPA). This paper integrates these two programs and discusses how assurance practices can be improved using new system engineering and system design strategies that rely on both functional and formal design methods.

Wireless Mesh Networks (WMN) are becoming inevitable in this world of high technology as it provides low cost access to broadband services. Moreover, the technologists are doing research to make WMN more reliable and secure. Subsequently, among wireless ad-hoc networking technologies, Bluetooth Low Energy (BLE) is gaining high degree of importance among researchers due to its easy availability in the gadgets and low power consumption. BLE started its journey from version 4.0 and announced the latest version 5 with mesh support capability. BLE being a low power and mesh supported technology is nowadays among the hot research topics for the researchers. Many of the researchers are working on BLE mesh technology to make it more efficient and smart. Apart from other variables of efficiency, like all communication networks, mesh network security is also of a great concern. In view of the aforesaid, this paper provides a comprehensive review on several works associated to the security in WMN and BLE mesh networks and the research related to the BLE security protocols. Moreover, after the detailed research on related works, this paper has discussed the pros and cons of the present developed mesh security mechanisms. Also, at the end after extracting the curx from the present research on WMN and BLE mesh security, this research study has devised some solutions as how to mitigate the BLE mesh network security lapses.

Over the past few years, virtual and mixed reality systems have evolved significantly yielding high immersive experiences. Most of the metaphors used for interaction with the virtual environment do not provide the same meaningful feedback, to which the users are used to in the real world. This paper proposes a cyber-glove to improve the immersive sensation and the degree of embodiment in virtual and mixed reality interaction tasks. In particular, we are proposing a cyber-glove system that tracks wrist movements, hand orientation and finger movements. It provides a decoupled position of the wrist and hand, which can contribute to a better embodiment in interaction and manipulation tasks. Additionally, the detection of the curvature of the fingers aims to improve the proprioceptive perception of the grasping/releasing gestures more consistent to visual feedback. The cyber-glove system is being developed for VR applications related to real estate promotion, where users have to go through divisions of the house and interact with objects and furniture. This work aims to assess if glove-based systems can contribute to a higher sense of immersion, embodiment and usability when compared to standard VR hand controller devices (typically button-based). Twenty-two participants tested the cyber-glove system against the HTC Vive controller in a 3D manipulation task, specifically the opening of a virtual door. Metric results showed that 83% of the users performed faster door pushes, and described shorter paths with their hands wearing the cyber-glove. Subjective results showed that all participants rated the cyber-glove based interactions as equally or more natural, and 90% of users experienced an equal or a significant increase in the sense of embodiment.

We study a model where a data collector obtains data from users through a payment mechanism to learn the underlying state from the elicited data. The private signal of each user represents her individual knowledge about the state. Through social interactions, each user can also learn noisy versions of her friends' signals, which is called group signals. Based on both her private signal and group signals, each user makes strategic decisions to report a privacy-preserved version of her data to the data collector. We develop a Bayesian game theoretic framework to study the impact of social learning on users' data reporting strategies and devise the payment mechanism for the data collector accordingly. Our findings reveal that, the Bayesian-Nash equilibrium can be in the form of either a symmetric randomized response (SR) strategy or an informative non-disclosive (ND) strategy. A generalized majority voting rule is applied by each user to her noisy group signals to determine which strategy to follow. When a user plays the ND strategy, she reports privacy-preserving data completely based on her group signals, independent of her private signal, which indicates that her privacy cost is zero. Both the data collector and the users can benefit from social learning which drives down the privacy costs and helps to improve the state estimation at a given payment budget. We derive bounds on the minimum total payment required to achieve a given level of state estimation accuracy.

This paper proposes a double-layer message security scheme that is implemented in two stages. First, the secret data is encrypted using the AES algorithm with a 256-bit key. Second, least significant bit (LSB) embedding is carried out, by hiding the secret message into an image of an information matrix. A number of performance evaluation metrics are discussed and computed for the proposed scheme. The obtained results are compared to other schemes in literature and show the superiority of the proposed scheme.

Hardware implementation of many of today's applications such as those in automotive, telecommunication, bio, and security, require heavy repeated computations, and concurrency in the execution of these computations. These requirements are not easily satisfied by existing embedded systems. This paper proposes an embedded system architecture that is enhanced by an array of accelerators, and a bussing system that enables concurrency in operation of accelerators. This architecture is statically configurable to configure it for performing a specific application. The embedded system architecture and architecture of the configurable accelerators are discussed in this paper. A case study examines an automotive application running on our proposed system.

IT technology is a vital part of our everyday life and society. Additionally, as it is present in strategic domains like the military, healthcare or critical infrastructure, the aspect of protection, i.e. cybersecurity is of utmost importance. In recent years, the demand for cybersecurity experts is exponentially rising. Additionally, the field of cybersecurity is very much interdisciplinary and therefore requires a broad set of skills. Renowned organisations as ACM or IEEE have recognized the importance of cybersecurity experts and proposed guidelines for higher education training of such professionals. This paper presents an overview of a cybersecurity education model from the Information Systems and Information Technology perspective together with a good example and experience of the University of Maribor. The presented education model is shaped according to the guidelines by the Joint Task Force on Cybersecurity Education and the expectations of the Slovene industry regarding the knowledge and skills their future employees should possess.

With the advancement in technology, inclusion of Information and Communication Technology (ICT) in the conventional Electrical Power Grid has become evident. The combination of communication system with physical system makes it cyber-physical system (CPS). Though the advantages of this improvement in technology are numerous, there exist certain issues with the system. Security and privacy concerns of a CPS are a major field and research and the insight of which is content of this paper.

Named Data Networking (or NDN) represents a potential new approach to the current host based Internet architecture which prioritize content over the communication between end nodes. NDN relies on caching functionalities and local data storage, such as a content request could be satisfied by any node holding a copy of the content in its storage. Due to the fact that users in the same network domain can share their cached content with each other and in order to reduce the transmission cost for obtaining the desired content, a cooperative network coding mechanism is proposed in this paper. We first formulate our optimal coding and homomorphic signature scheme as a MIP problem and we show how to leverage Software Defined Networking to provide seamless implementation of the proposed solution. Evaluation results demonstrate the efficiency of the proposed coding scheme which achieves better performance than conventional NDN with random coding especially in terms of transmission cost and security.

Cryptosystems are essential for computer and communication security, e.g., RSA or ECDSA in PGP Email clients and AES in full disk encryption. In practice, the cryptographic keys are loaded and stored in RAM as plain-text, and therefore vulnerable to cold-boot attacks exploiting the remanence effect of RAM chips to directly read memory data. To tackle this problem, we propose Copker, a cryptographic engine that implements asymmetric cryptosystems entirely within the CPU, without storing any plain-text sensitive data in RAM. Copker supports the popular asymmetric cryptosystems (i.e., RSA and ECDSA), and deterministic random bit generators (DRBGs) used in ECDSA signing. In its active mode, Copker stores kilobytes of sensitive data, including the private key, the DRBG seed and intermediate states, only in on-chip CPU caches (and registers). Decryption/signing operations are performed without storing any sensitive information in RAM. In the suspend mode, Copker stores symmetrically-encrypted private keys and DRBG seeds in memory, while employs existing solutions to keep the key-encryption key securely in CPU registers. Hence, Copker releases the system resources in the suspend mode. We implement Copker with the support of multiple private keys. With security analyses and intensive experiments, we demonstrate that Copker provides cryptographic services that are secure against cold-boot attacks and introduce reasonable overhead.

This paper designs three distribution devices for the smart grid, which are, respectively, novel transformer with dc bias restraining ability, energy-saving contactor, and controllable reactor with adjustable intrinsic magnetic state based on the magnetically tunable nanocomposite material core. First, the magnetic performance of this magnetic material was analyzed and the magnetic properties processing method was put forward. One kind of nanocomposite which is close to the semihard magnetic state with low coercivity and high remanence was attained. Nanocomposite with four magnetic properties was processed and prepared using the distribution devices design. Second, in order to adjust the magnetic state better, the magnetization and demagnetization control circuit based on the single-phase supply power of rectification and inverter for the nanocomposite magnetic performance adjustment has been designed, which can mutual transform the material's soft and hard magnetic phases. Finally, based on the nanocomposite and the control circuit, a novel power transformer, an energy-saving contactor, and a magnetically controllable reactor were manufactured for the smart grid. The maintained remanence of the nanocomposite core after the magnetization could neutralize the dc bias magnetic flux in the transformer main core without changing the transformer neutral point connection mode, could pull in the contactor movable core instead of the traditional electromagnetic-type fixed core, and could adjust the reactor core saturation degree instead of the traditional electromagnetic coil. The simulation and experimental results verify the correctness of the design, which provides reliable, intelligent, interactive, and energy-saving power equipment for the smart power grids safe operation.

This paper proposes a novel kind of direct-current and alternate-decay-current hybrid integrative magnetization and demagnetization power supplies applied to transformer dc bias treatment based on a nanocomposite magnetic material. First, according to the single-phase transformer structure, one dc bias magnetic compensation mechanism was provided. The dc bias flux in the transformer main core could be eliminated directionally by utilizing the material remanence. Second, for the rapid response characteristic of the magnetic material to an external magnetic field, one positive and negative dc magnetization superimposed decaying ac demagnetization hybrid integrative power supplies based on single-phase rectifier circuit and inverter circuit was designed. In order to accurately control the magnetic field strength by which a good de/-magnetization effect could be achieved, this paper adopts the double-loop control technology of the magnetic field strength and magnetizing current for the nanocomposite magnetic state adjustment. Finally, two 10 kVA transformers and the experiment module of the hybrid integrative power supplies were manufactured and built. Experimental results showed that the integrated power supplies have good de/-magnetization effect and practicability, proving the validity and feasibility of the proposed scheme.

Now a day document such as Degree certificate can be easily forged fully or partially modifying obtained score result like GPA (Grade Point Average). Digital signature are used to detect unauthorized modification to data and to authenticate the identity of signatory. The Quick Response (QR) code was designed for storage information and high-speed readability. This paper proposed a method that QR code will contain a digital signature with the student data such as degree holder's name, major program, GPA obtained and more, which will be signed by Higher Educational Institute (HEI). In order to use this system, all HEI have to register in central system, the central system provide another system that will deploy in each HEI. All digitally signed certificate generating process are offline. To verify the digital signature signed with QR code, we developed specific smart phone application which will scan and authenticate the certificate without the need to address the certificate issuing institution and gaining access to user's security credentials.

The choice of threshold is an important part of fault diagnosis. Most of the current methods use a constant threshold for detection and it is difficult to meet the robustness and sensitivity requirements of the diagnosis system. This article develops a dynamic threshold algorithm for aircraft engine fault detection and isolation systems. The algorithm firstly analyzes the bounded norm uncertainty that may appear in the process of model based on the state space equation, and gives the time domain response range calculation formula under the influence of uncertain parameters; then the Kalman filter is combined to calculate the threshold with the real-time change of state; the simulation is performed at the end. The simulation results show that dynamic threshold range changes with status in real time.

In fault diagnosis of industrial process, there are usually more than one variable that are faulty. When multiple faults occur, the generalized reconstruction-based contribution can be helpful while traditional RBC may make mistakes. Due to the correlation between the variables, these faults usually propagate to other normal variables, which is called smearing effect. Thus, it is helpful to consider the pervious fault diagnosis results. In this paper, a data-driven fault diagnosis method which is based on generalized RBC and bayesian decision is presented. This method combines multi-dimensional RBC and bayesian decision. The proposed method improves the diagnosis capability of multiple and minor faults with greater noise. A numerical simulation example is given to show the effectiveness and superiority of the proposed method.

High-temperature magnetic properties of the anisotropic bulk SmCo7/Fe(Co) nanocomposite magnets prepared by multistep deformation have been investigated and compared with the corresponding isotropic nanocomposites. The anisotropic SmCo7/Fe(Co) nanocomposites with a Fe(Co) fraction of 28% exhibit much higher energy products than the corresponding isotropic nanocomposites at both room and high temperatures. These magnets show a small remanence (α = -0.022%/K) and a coercivity (β = -0.25%/K) temperature coefficient which can be comparable to those of the conventional SmCo5 and Sm2Co17 high-temperature magnets. The magnetic properties of these nanocomposites at high temperatures are sensitive to the weight fractions of the Fe(Co) phase. This paper demonstrates that the anisotropic bulk SmCo7/Fe(Co) nanocomposites have better high-temperature magnetic properties than the corresponding isotropic ones.

The process of information and transformation of society has become a habit in modem people. We are accustomed to using the mobile phone for all kinds of operations, such as: sweep code to order meals, buy tickets and payment, thanks to the popularity of QR code technology in our country. There are many applications in the market with the function of scanning QR code, however, some QR codes can only be parsed by the specified application software. For instance, it can not work when using Alipay scanning QR code which configured by WeChat payment certificate Web program. The user will not be able to pay for such operations. For a product or service provider, different QR codes need to be created for different applications; for a user, a certain business operation needs to face multiple QR codes to select corresponding software in the device. The integration of QR code technology has become a key breakthrough point to improve the competitiveness of enterprises.

The number of Virtual Reality applications has increased tremendously in the recent years to the point where every single digital entertainment company is investing heavily in VR systems. This increase in VR products demands the improvement in the evaluation of VR experience since current evaluations require an attendee per survey taker and can only move onto the next survey taker after the current survey is done. Traditional evaluations also require many evaluation machines if done digitally, costing survey takers unnecessary expenses. "MasQueRade" is a QR code based instant user feedback online system. This system allows users to scan the QR code on their VR sanitary masks and access an online evaluation system on their own mobile phones. This enables users to conduct the evaluation on their own free time and decreases the expenses surveyors have to spend on machines, therefore greatly decreases the manpower and time required to conduct the evaluations. While this approach to solving the issue of obtaining user feedback may sound elementary, the amount of efforts and resources "MasQueRade" saves by transferring the evaluation from a paper or digital form into an online database gives near infinite possibilities in the future of gathering feedback and evaluation. This paper seeks to explain the functions of "MasQueRade" and the results the team obtains during Anime Expo 2017 and propose a real-time live user VR commentary system drawing inputs form the attendees.

Principal Component Analysis (PCA) is a basic method of fault diagnosis based on multivariate statistical analysis. It utilizes the linear correlation between multiple process variables to implement process fault diagnosis and has been widely used. Traditional PCA fault diagnosis ignores the impact of faults with different magnitudes on detection accuracy. Based on a variety of data processing methods, this paper proposes a multi-level and multi-granularity principal component analysis method to make the detection results more accurate.

Electric powertrains are widely used in automotive and renewable energy industries. Reliable diagnosis for defects in the critical components such as bearings, gears and stator windings, is important to prevent failures and enhance the system reliability and power availability. Most of existing fault diagnosis methods are based on specific characteristic frequencies to single faults at constant speed operations. Once multiple faults occur in the system, such a method may not detect the faults effectively and may give false alarms. Furthermore, variable speed operations render a challenge of analysing nonstationary signals. In this work, a deep learning-based fault diagnosis method is proposed to detect common faults in the electric powertrains. The proposed method is based on pattern recognition using convolutional neural network to detect effectively not only single faults at constant speed but also multiple faults in variable speed operations. The effectiveness of the proposed method is validated via an in-house experimental setup.

The applications in the critical infrastructure systems pose simultaneous resilience and performance requirements to the underlying computer network. To meet such requirements, the networks that use the store-and-forward paradigm poses stringent conditions on the redundancy in the network topology and results in problems that becoming computationally challenging to solve at scale. However, with the advent of programmable data-planes, it is now possible to use linear network coding (NC) at the intermediate network nodes to meet resilience requirements of the applications. To that end, we propose an architecture that realizes linear NC in programmable networks by decomposing the linear NC functions into the atomic coding primitives. We designed and implemented the primitives using the features offered by the P4 ecosystem. Using an empirical evaluation, we show that the theoretical gains promised by linear network coding can be realized with a per-packet processing cost.

A novel scheme is proposed to authenticate sensors and detect data integrity attacks in a Cyber Physical System (CPS). The proposed technique uses the hardware characteristics of a sensor and physics of a process to create unique patterns (herein termed as fingerprints) for each sensor. The sensor fingerprint is a function of sensor and process noise embedded in sensor measurements. Uniqueness in the noise appears due to manufacturing imperfections of a sensor and due to unique features of a physical process. To create a sensor's fingerprint a system-model based approach is used. A noise-based fingerprint is created during the normal operation of the system. It is shown that under data injection attacks on sensors, noise pattern deviations from the fingerprinted pattern enable the proposed scheme to detect attacks. Experiments are performed on a dataset from a real-world water treatment (SWaT) facility. A class of stealthy attacks is designed against the proposed scheme and extensive security analysis is carried out. Results show that a range of sensors can be uniquely identified with an accuracy as high as 98%. Extensive sensor identification experiments are carried out on a set of sensors in SWaT testbed. The proposed scheme is tested on a variety of attack scenarios from the reference literature which are detected with high accuracy

This work studies the optimization of spectrum pooling for the downlink of a multi-tenant Cloud Radio Access Network (C-RAN) system in the presence of inter-tenant privacy constraints. The spectrum available for downlink transmission is partitioned into private and shared subbands, and the participating operators cooperate to serve the user equipments (UEs) on the shared subband. The network of each operator consists of a cloud processor (CP) that is connected to proprietary radio units (RUs) by means of finite-capacity fronthaul links. In order to enable inter-operator cooperation, the CPs of the participating operators are also connected by finite-capacity backhaul links. Inter-operator cooperation may hence result in loss of privacy. The problem of optimizing the bandwidth allocation, precoding, and fronthaul/backhaul compression strategies is tackled under constraints on backhaul and fronthaul capacity, as well as on per-RU transmit power and inter-onerator privacy.

In this letter, ferroelectric memory performance of TiN/Y-doped-HfO2 (HYO)/TiN capacitors is investigated under proton radiation with 3-MeV energy and different fluence (5e13, 1e14, 5e14, and 1e15 ions/cm2). X-ray diffraction patterns confirm that the orthorhombic phase Pbc21 of HYOfilm has no obvious change after proton radiation. Electrical characterization results demonstrate slight variations of the permittivity and ferroelectric hysteresis loop after proton radiation. The remanent polarization (2Pr) of the capacitor decreases with increasing proton fluence. But the decreasing trend of 2Pr is suppressed under high electric fields. Furthermore, the 2Pr degradation with cycling is abated by proton radiation. These results show that the HYO-based ferroelectric memory is highly resistive to proton radiation, which is potentially useful for space applications.

Multishot network coding is considered in a worst-case adversarial setting in which an omniscient adversary with unbounded computational resources may inject erroneous packets in up to t links, erase up to ρ packets, and wire-tap up to μ links, all throughout ℓ shots of a (random) linearly-coded network. Assuming no knowledge of the underlying linear network code (in particular, the network topology and underlying linear code may change with time), a coding scheme achieving zero-error communication and perfect secrecy is obtained based on linearized Reed-Solomon codes. The scheme achieves the maximum possible secret message size of ℓn'-2t-ρ-μ packets, where n' is the number of outgoing links at the source, for any packet length m ≥ n' (largest possible range), with only the restriction that ℓ\textbackslashtextless;q (size of the base field). By lifting this construction, coding schemes for non-coherent communication are obtained with information rates close to optimal for practical instances. A Welch-Berlekamp sum-rank decoding algorithm for linearized Reed-Solomon codes is provided, having quadratic complexity in the total length n = ℓn', and which can be adapted to handle not only errors, but also erasures, wire-tap observations and non-coherent communication.