Biblio

The rapid growth of power Internet of Things devices has led to traditional data security sharing mechanisms that are no longer suitable for attribute and permission management of massive devices. In response to this problem, this article proposes a blockchain-based data security sharing mechanism for the power Internet of Things, which reduces the risk of data leakage through decentralization in the architecture and promotes the integration of multiple information and methods.

This paper studies the data-driven stealthy actuator attack against cyber-physical systems. The objective of the attacker is to add a certain bias to the output while keeping the detection rate of the χ2 detector less than a certain value. With the historical input and output data, the parameters of the system are estimated and the attack signal is the solution of a convex optimization problem constructed with the estimated parameters. The extension to the case of arbitrary detectors is also discussed. A numerical example is given to verify the effectiveness of the attack.

Wireless networking opens up many opportunities to facilitate miniaturized robots in collaborative tasks, while the openness of wireless medium exposes robots to the threats of Sybil attackers, who can break the fundamental trust assumption in robotic collaboration by forging a large number of fictitious robots. Recent advances advocate the adoption of bulky multi-antenna systems to passively obtain fine-grained physical layer signatures, rendering them unaffordable to miniaturized robots. To overcome this conundrum, this paper presents ScatterID, a lightweight system that attaches featherlight and batteryless backscatter tags to single-antenna robots to defend against Sybil attacks. Instead of passively "observing" signatures, ScatterID actively "manipulates" multipath propagation by using backscatter tags to intentionally create rich multipath features obtainable to a single-antenna robot. These features are used to construct a distinct profile to detect the real signal source, even when the attacker is mobile and power-scaling. We implement ScatterID on the iRobot Create platform and evaluate it in typical indoor and outdoor environments. The experimental results show that our system achieves a high AUROC of 0.988 and an overall accuracy of 96.4% for identity verification.

Compressive sensing (CS) is regarded as one of the promising solutions for IoT data encryption as it achieves simultaneous sampling, compression, and encryption. Theoretical work in the literature has proved that CS provides computational secrecy. It also provides asymptotic perfect secrecy for Gaussian sensing matrix with constraints on input signal. In this paper, we design an attack decoding algorithm based on block compressed sensing decoding algorithm to perform ciphertext-only attack on real-life time series IoT data. It shows that it is possible to retrieve vital information in the plaintext under some conditions. Furthermore, it is also applied to a State-of-the Art CS-based encryption scheme for smart grid, and the power profile is reconstructed using ciphertext-only attack. Additionally, the statistical analysis of Gaussian and Binomial measurements is conducted to investigate the randomness provided by them.

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.

With the development of cloud computing the topology properties of data center network are important to the computing resources. Recently a data center network structure - BCCC is proposed, which is recursively built structure with many good properties. and expandability. The Hamiltonian and expandability in data center network structure plays an extremely important role in network communication. This paper described the Hamiltonian and expandability of the expandable data center network for BCCC structure, the important role of Hamiltonian and expandability in network traffic.

We report a an experimental study of device-independent quantum random number generation based on an detection-loophole free Bell test with entangled photons. After considering statistical fluctuations and applying an 80 Gb × 45.6 Mb Toeplitz matrix hashing, we achieve a final random bit rate of 114 bits/s, with a failure probability less than 10-5.

Wearable devices are being more popular in our daily life. Especially, smart wristbands are booming in the market recently, which can be used to monitor health status, track fitness data, or even do medical tests, etc. For this reason, smart wristbands can obtain a lot of personal data. Hence, users and manufacturers should pay more attention to the security aspects of smart wristbands. However, we have found that some Bluetooth Low Energy based smart wristbands have very weak or even no security protection mechanism, therefore, they are vulnerable to replay attacks, man-in-the-middle attacks, brute-force attacks, Denial of Service (DoS) attacks, etc. We have investigated four different popular smart wristbands and a smart watch. Among them, only the smart watch is protected by some security mechanisms while the other four smart wristbands are not protected. In our experiments, we have also figured out all the message formats of the controlling commands of these smart wristbands and developed an Android software application as a testing tool. Powered by the resolved command formats, this tool can directly control these wristbands, and any other wristbands of these four models, without using the official supporting applications.

The inevitable temperature raise leads to the demagnetization of permanent magnet synchronous motor (PMSM), that is undesirable in the application of electrical vehicle. This paper presents a nonlinear demagnetization model taking into account temperature with the Wiener structure and neural network characteristics. The remanence and intrinsic coercivity are chosen as intermediate variables, thus the relationship between motor temperature and maximal permanent magnet flux is described by the proposed neural Wiener model. Simulation and experimental results demonstrate the precision of temperature dependent demagnetization model. This work makes the basis of temperature compensation for the output torque from PMSM.