Biblio

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.

In this paper, a novel anti-jamming mechanism is proposed to analyze and enhance the security of adversarial Internet of Battlefield Things (IoBT) systems. In particular, the problem is formulated as a dynamic psychological game between a soldier and an attacker. In this game, the soldier seeks to accomplish a time-critical mission by traversing a battlefield within a certain amount of time, while maintaining its connectivity with an IoBT network. The attacker, on the other hand, seeks to find the optimal opportunity to compromise the IoBT network and maximize the delay of the soldier's IoBT transmission link. The soldier and the attacker's psychological behavior are captured using tools from psychological game theory, with which the soldier's and attacker's intentions to harm one another are considered in their utilities. To solve this game, a novel learning algorithm based on Bayesian updating is proposed to find an ∈ -like psychological self-confirming equilibrium of the game.

With the growth of smartphone sales and app usage, fingerprinting and identification of smartphone apps have become a considerable threat to user security and privacy. Traffic analysis is one of the most common methods for identifying apps. Traditional countermeasures towards traffic analysis includes traffic morphing and multipath routing. The basic idea of multipath routing is to increase the difficulty for adversary to eavesdrop all traffic by splitting traffic into several subflows and transmitting them through different routes. Previous works in multipath routing mainly focus on Wireless Sensor Networks (WSNs) or Mobile Ad Hoc Networks (MANETs). In this paper, we propose a multipath routing scheme for smartphones with edge network assistance to mitigate traffic analysis attack. We consider an adversary with limited capability, that is, he can only intercept the traffic of one node following certain attack probability, and try to minimize the traffic an adversary can intercept. We formulate our design as a flow routing optimization problem. Then a heuristic algorithm is proposed to solve the problem. Finally, we present the simulation results for our scheme and justify that our scheme can effectively protect smartphones from traffic analysis attack.

Completely Automated Public Turing Test to Tell Computers and Humans Apart (CAPTCHA) is an important human-machine distinction technology for website to prevent the automatic malicious program attack. CAPTCHA recognition studies can find security breaches in CAPTCHA, improve CAPTCHA technology, it can also promote the technologies of license plate recognition and handwriting recognition. This paper proposed a method based on Convolutional Neural Network (CNN) model to identify CAPTCHA and avoid the traditional image processing technology such as location and segmentation. The adaptive learning rate is introduced to accelerate the convergence rate of the model, and the problem of over-fitting and local optimal solution has been solved. The multi task joint training model is used to improve the accuracy and generalization ability of model recognition. The experimental results show that the model has a good recognition effect on CAPTCHA with background noise and character adhesion distortion.

Pre-Silicon hardware Trojan detection has been studied for years. The most popular benchmark circuits are from the Trust-Hub. Their common feature is that the probability of activating hardware Trojans is very low. This leads to a series of machine learning based hardware Trojan detection methods which try to find the nets with low signal probability of 0 or 1. On the other hand, it is considered that, if the probability of activating hardware Trojans is high, these hardware Trojans can be easily found through behaviour simulations or during functional test. This paper explores the "grey zone" between these two opposite scenarios: if the activation probability of a hardware Trojan is not low enough for machine learning to detect it and is not high enough for behaviour simulation or functional test to find it, it can escape from detection. Experiments show the existence of such hardware Trojans, and this paper suggests a new set of hardware Trojan benchmark circuits for future study.

In this paper, a novel game-theoretic framework is introduced to analyze and enhance the security of adversarial Internet of Battlefield Things (IoBT) systems. In particular, a dynamic, psychological network interdiction game is formulated between a soldier and an attacker. In this game, the soldier seeks to find the optimal path to minimize the time needed to reach a destination, while maintaining a desired bit error rate (BER) performance by selectively communicating with certain IoBT devices. The attacker, on the other hand, seeks to find the optimal IoBT devices to attack, so as to maximize the BER of the soldier and hinder the soldier's progress. In this game, the soldier and attacker's first- order and second-order beliefs on each others' behavior are formulated to capture their psychological behavior. Using tools from psychological game theory, the soldier and attacker's intention to harm one another is captured in their utilities, based on their beliefs. A psychological forward induction-based solution is proposed to solve the dynamic game. This approach can find a psychological sequential equilibrium of the game, upon convergence. Simulation results show that, whenever the soldier explicitly intends to frustrate the attacker, the soldier's material payoff is increased by up to 15.6% compared to a traditional dynamic Bayesian game.

Due to flexibility, low cost and rapid deployment, wireless sensor networks (WSNs)have been drawing more and more interest from governments, researchers, application developers, and manufacturers in recent years. Nowadays, we are in the age of industry 4.0, in which the traditional industrial control systems will be connected with each other and provide intelligent manufacturing. Therefore, WSNs can play an extremely crucial role to monitor the environment and condition parameters for smart factories. Nevertheless, the introduction of the WSNs reveals the weakness, especially for industrial applications. Through the vulnerability of IWSNs, the latent attackers were likely to invade the information system. Risk evaluation is an overwhelmingly efficient method to reduce the risk of information system in order to an acceptable level. This paper aim to study the security issues about IWSNs as well as put forward a practical solution to evaluate the risk of IWSNs, which can guide us to make risk evaluation process and improve the security of IWSNs through appropriate countermeasures.

The majority of available wearable computing devices require communication with Internet servers for data analysis and storage, and rely on a paired smartphone to enable secure communication. However, many wearables are equipped with WiFi network interfaces, enabling direct communication with the Internet. Secure communication protocols could then run on these wearables themselves, yet it is not clear if they can be efficiently supported.,,,,In this paper, we show that wearables are ready for direct and secure Internet communication by means of experiments with both controlled local web servers and Internet servers. We observe that the overall energy consumption and communication delay can be reduced with direct Internet connection via WiFi from wearables compared to using smartphones as relays via Bluetooth. We also show that the additional HTTPS cost caused by TLS handshake and encryption is closely related to the number of parallel connections, and has the same relative impact on wearables and smartphones.

This paper investigates physical layer security of non-orthogonal multiple access (NOMA) in cognitive radio (CR) networks. The techniques of NOMA and CR have improved the spectrum efficiency greatly in the traditional networks. Because of the difference in principles of spectrum improving, NOMA and CR can be combined together, i.e. CR NOMA network, and have great potential to improving the spectrum efficiency. However the physical layer security in CR NOMA network is different from any single network of NOMA or CR. We will study the physical layer security in underlay CR NOMA network. Firstly, the wiretap network model is constructed according to the technical characteristics of NOMA and CR. In addition, new exact and asymptotic expressions of the security outage probability are derived and been confirmed by simulation. Ultimately, we have studied the effect of some critical factors on security outage probability after simulation.

This paper presents a framework for privacy-preserving video delivery system to fulfill users' privacy demands. The proposed framework leverages the inference channels in sensitive behavior prediction and object tracking in a video surveillance system for the sequence privacy protection. For such a goal, we need to capture different pieces of evidence which are used to infer the identity. The temporal, spatial and context features are extracted from the surveillance video as the observations to perceive the privacy demands and their correlations. Taking advantage of quantifying various evidence and utility, we let users subscribe videos with a viewer-dependent pattern. We implement a prototype system for off-line and on-line requirements in two typical monitoring scenarios to construct extensive experiments. The evaluation results show that our system can efficiently satisfy users' privacy demands while saving over 25% more video information compared to traditional video privacy protection schemes.