Using deep learning to combine static and dynamic power analyses of cryptographic circuits

Side-channel attacks have shown to be efficient tools in breaking cryptographic hardware. Many conventional algorithms have been proposed to perform side-channel attacks exploiting the dynamic power leakage. In recent years, with the development of processing technology, static power has emerged as a new potential source for side-channel leakage. Both types of power leakage have their advantages and disadvantages. In this work, we propose to use the deep neural network technique to combine the benefits of both static and dynamic power. This approach replaces the classifier in template attacks with our proposed long short-term memory network schemes. Hence, instead of deriving a specific probability density model for one particular type of power leakage, we gain the ability of combining different leakage sources using a structural algorithm. In this paper, we propose three schemes to combine the static and dynamic power leakage. The performance of these schemes is compared using simulated test circuits designed with a 45-nm library.     

A multilevel sampled‐data approach for resilient navigation and control of autonomous systems

Autonomous systems are rapidly becoming an integrated part of the modern life. Safe and secure navigation and control of these systems present significant challenges in the presence of uncertainties, physical failures, and cyber attacks. In this paper, we formulate a navigation and control problem for autonomous systems using a multilevel control structure, in which the high‐level reference commands are limited by a saturation function, whereas the low‐level controller tracks the reference by compensating for disturbances and uncertainties. For this purpose, we consider a class of nested, uncertain, multiple‐input–multiple‐output systems subject to reference command saturation, possibly with nonminimum phase zeros. A multirate output‐feedback adaptive controller is developed as the low‐level controller. The sampled‐data (SD) design of this controller facilitates the direct implementation on digital computers, where the input/output signals are available at discrete time instances with different sampling rates. In addition, stealthy zero‐dynamics attacks become detectable by considering a multirate SD formulation. Robust stability and performance of the overall closed‐loop system with command saturation and multirate adaptive control are analyzed. Simulation scenarios for navigation and control of a fixed‐wing drone under failures/attacks are provided to validate the theoretical findings.     

Copy-move forgery detection of duplicated objects using accurate PCET moments and morphological operators

This paper presents a new method for copy-move forgery detection of duplicated objects. A bounding rectangle is drawn around the detected object to form a sub-image. Morphological operator is used to remove the unnecessary small objects. Highly accurate polar complex exponential transform moments are used as features for the detected objects. Euclidian distance and correlation coefficient between the feature vectors are calculated and used for searching the similar objects. A set of 20 forged images with duplicated objects is carefully selected from previously published works. Additional 80 non-forged images are edited by the authors and forged by duplicating different kinds of objects. Numerical simulation is performed where the results show that the proposed method successfully detect different kinds of duplicated objects. The proposed method is much faster than the previously existing methods. Also, it exhibits high robustness to various attacks such as additive white Gaussian noise, JPEG compression, rotation, and scaling.     

DEFIDNET: A framework for optimal allocation of cyberdefenses in Intrusion Detection Networks

Intrusion Detection Networks (IDN) are distributed cyberdefense systems composed of different nodes performing local detection and filtering functions, as well as sharing information with other nodes in the IDN. The security and resilience of such cyberdefense systems are paramount, since an attacker will try to evade them or render them unusable before attacking the end systems. In this paper, we introduce a system model for IDN nodes in terms of their logical components, functions, and communication channels. This allows us to model different IDN node roles (e.g., detectors, filters, aggregators, correlators, etc.) and architectures (e.g., hierarchical, centralized, fully distributed, etc.). We then introduce a threat model that considers adversarial actions executed against particular IDN nodes, and also the propagation of such actions throughout connected nodes. Based on such models, we finally introduce a countermeasure allocation model based on a multi-objective optimization algorithm to obtain optimal allocation strategies that minimize both risk and cost. Our experimental results obtained through simulation with different IDN architectures illustrate the benefit of our framework to design and reconfigure cyberdefense systems optimally.     

钓鱼网站检测研究现状与发展趋势的计量分析

从包含相关SCI、CPCI-S与BKCI-S等数据库的Web of Science核心合集数据库出发，对数据库中存在的近13年的国内外文献使用Cite Space与Carrot2分析工具进行聚类分析。力求系统客观地得到国际上钓鱼网站检测技术研究领域的关注热点、研究脉络、著名机构以及核心刊物等研究现状。筛选出具有代表性的文章，采取少量顶级文章精读、部分知名文章粗读的方式对目前钓鱼网站检测技术所涉及到的方法做整理与归类。根据上文的分析对钓鱼网站检测技术的发展方向做了规律性展望，以求对此方向下相关研究人员的研究提供一定的参考。     

Retracted: State estimation of Takagi-Sugeno fuzzy system in networked control system with stochastic time delays under unknown attacks

This article concerns the event-triggered fuzzy filter design for Takagi-Sugeno (T-S) fuzzy systems subject to deception attacks under the stochastic multiple time-varying delays. A sequence of random variables, which are mutually independent but obey the Bernoulli distribution, is introduced to account for the randomly occurring communication delays. In order to efficiently utilize limited network communication bandwidth resources, the event-triggering scheme is adopted. A fuzzy filter with the attacked input signal is presented. Moreover, due to communication delays caused by event-triggering schemes and transmission, the filter adopts non-synchronous premise variables with the system. Then, by utilizing a model transformation technique, the fuzzy systems are developed. Furthermore, using the piecewise Lyapunov functional method technique, the resulting criterion provides sufficient conditions to ensure that fuzzy systems under deception attacks are stochastically stable with an H_∞ performance. Accordingly, the conditions for the co-design of the fuzzy filter and event-triggering schemes are given. Finally, numerical simulation with the industrial process provided to verify the proposed event-triggered design.     

Memory-based dynamic event-triggered control for networked interval type-2 fuzzy systems subject to DoS attacks

In this article, the memory-based dynamic event-triggered controller design issue is investigated for networked interval type-2 (IT2) fuzzy systems under non-periodic denial-of-service (DoS) attacks. For saving limited network bandwidth, a novel memory-based dynamic event-triggered mechanism (DETM) is proposed to schedule data communication. Unlike existing event-triggered generators, the developed memory-based DETM can utilize a series of newly released signals and further save network resources by introducing interval dynamic variables. Moreover, to improve design flexibility, an IT2 fuzzy controller with freely selectable fuzzy rule number and premise membership functions (MFs) is synthesized. Then, a new switched time-delay system with imperfectly matched MFs is established under the consideration of memory-based DETM and DoS attacks simultaneously. Besides, based on the property of MFs, the boundary information of membership grades and slack matrices are introduced in the stability analysis. Furthermore, by using a piecewise Lyapunov–Krasovskii method, membership-functions-dependent criteria are deduced to ensure the asymptotic stability of built fuzzy switched systems. Finally, the effectiveness of proposed control strategies is demonstrated by simulation examples.     

Image-Moment Based Affine Invariant Watermarking Scheme Utilizing Neural Networks

Watermarking,as a technique for copyright pro-tection,has drawn significant attention from manyresearchers and there has been intensive research inthis areainrecent years.Robustness of watermarkingstill remains one of the key issues for practical appli-cations.Aserious problemconstrainingsome practicalexplorations of watermarking technologyis the insuf-ficient robustness of existing watermarking algorithmsagainst geometric distortionsuch as rotation,scaling,and translation(RST),etc[1].Designi…     

Detection and mitigation of actuator attacks on small unmanned aircraft systems

Unmanned aircraft systems (UAS) are susceptible to malicious attacks originated by intelligent adversaries, and the actuators constitute one of the critical attack surfaces. In this paper, the problem of detecting and mitigating attacks on the actuators of a small UAS is addressed. Three possible solutions of differing complexity and effectiveness are proposed to address the problem. The first method involves an active detection strategy, whereby carefully designed excitation signals are superimposed on the control commands to increase the detectability of the attack. In the second method, an unknown input observer is designed, which in addition to detecting the attack also estimates the magnitude of the attack. The third method entails designing an actuator system that makes use of variable frequency pulse-width modulated signals to improve the resilience of the actuator against malicious attacks. The effectiveness of the proposed methods is demonstrated using flight experiments and realistic MATLAB simulations that incorporate exogenous disturbances, such as steady winds, atmospheric turbulence, and measurement noise.     

Geometrically resilient color image zero-watermarking algorithm based on quaternion Exponent moments

Although research on zero-watermarking has made great progress in recent years, most of it has been focused on grayscale images rather than color ones, and cannot resist geometric attacks efficiently. In this paper, we discuss properties of quaternion Exponent moments (QEMs) in detail and propose a robust color image zero-watermarking algorithm which is robust to geometric attacks. We first compute and select robust QEMs of the original color image, and then a binary feature image is constructed using the magnitude of the selected moments. Eventually, a bitwise exclusive-or is applied on the binary feature image and a scrambled binary logo to generate the zero-watermark image. Experimental results show that the proposed zero-watermarking algorithm is robust to both geometric attacks and common image processing attacks effectively. Compared to similar zero-watermarking algorithms and traditional watermarking algorithms based on QEMs, the proposed zero-watermarking algorithm has better performance.