分组密码及其研究

本文讨论了秘密密钥分组密码的设计原则,介绍了DES、IDEA等分组密码,指出了它们的一些弱点,如何更好地用人工神经网络设计分组密码是值得进一步深入研究的课题。     

深度学习在分组密码差分区分器上的研究应用

差分分析在分组密码分析领域是一种重要的研究方法, 针对分组密码的差分分析的重点在于找到一个轮数或者概率更大的差分区分器. 首先描述了通过深度学习技术构造差分区分器时所需要的数据集的构造方法, 并且分别基于卷积神经网络(convolutional neural networks, CNN)和残差神经网络(residual...     

FPGA implementation of an enhanced chaotic-KASUMI block cipher

The radio link is a broadcast channel used to transmit data over mobile networks. Because of the sensitivity of this network part, a security mechanism is used to ensure users’ information. For example, the third generation of mobile network security is based on the KASUMI block cipher, which is standardized by the Third Generation Partnership Project (3GPP). This work proposes an optimized and enhanced implementation of the KASUMI block cipher based on a chaotic generator. The purpose is to develop an efficient ciphering algorithm with better performance and good security robustness while preserving the standardization. The proposed design was implemented on several Xilinx Virtex Field Programmable Gate Arrays (FPGA) technologies. The synthesis results and a comparison with previous works prove the performance improvement of the proposed cipher block in terms of throughput, used hardware logic resources, and resistance against most cryptanalysis attacks.     

可重构密码协处理器的概念及其设计原理

提出了可重构密码协处理器的概念并论述了其设计原理。所谓可重构密码协处理器实际上是一个其内部逻辑电路结构和功能可被灵活改变的密码处理单元,它能够在主处理器的控制和驱动下灵活、快速地实现多种不同的密码操作,以便适应不同密码算法的需求。基于可重构密码协处理器的可重构密码系统具有灵活、快速、安全的特点,在保密通讯和网络安全等领域中具有良好的应用前景。     

Indirect control of a class of nonlinear dynamic systems

Identification and control designs are considered using neural networks for a class of nonlinear partially known dynamic systems. Real-time implementation of two designs, a neural identifier and a proposed neural controller, using an experimental system, comparisons with two other neural networks as well as conventional schemes, and an implementation architecture are reported. The proposed control design facilitates incorporation of available knowledge about the structure of the system. The study also illustrates the inherent capability of neural networks to handle nonlinearities and perform control effectively for a real world system, based on minimal system information.     

一种WSN的低开销分组加密安全协议

由于无线传感器网络的许多特性,使其安全方案必须兼顾安全与性能两方面的因素。由此提出了一种全新的分组加密安全协议,以线性同余发生器LCG为基础,适合构建可靠安全并资源有限的无线传感器网络。具体来说,分组加密是给原始数据包加入LCG生成的随机噪声和随机序列,以此来提高安全性。而且这种方式能够满足无线传感器网络的安全需求,并且开销低、效率高。     

用组合网络构造分组密码

本文提出了组合网络的概念,将此网络的输入、输出关系作加密变换,构造了一个秘密密钥分组密码,分析了其安全性。     

Design of effective neural network ensembles for image classification purposes

In the field of pattern recognition, the combination of an ensemble of neural networks has been proposed as an approach to the development of high performance image classification systems. However, previous work clearly showed that such image classification systems are effective only if the neural networks forming them make different errors. Therefore, the fundamental need for methods aimed to design ensembles of ‘error-independent’ networks is currently acknowledged. In this paper, an approach to the automatic design of effective neural network ensembles is proposed. Given an initial large set of neural networks, our approach is aimed to select the subset formed by the most error-independent nets. Reported results on the classification of multisensor remote-sensing images show that this approach allows one to design effective neural network ensembles.     

A fast pseudo-stochastic sequential cipher generator based on RBMs

Based on Restricted Boltzmann machines, an improved pseudo-stochastic sequential cipher generator is proposed. It is effective and efficient because of the two advantages: this generator includes a stochastic neural network that can perform the calculation in parallel, that is to say, all elements are calculated simultaneously; unlimited number of sequential ciphers can be generated simultaneously for multiple encryption schemas. The periodicity and the correlation of the output sequential ciphers meet requirements for the design of encrypting sequential data. In the experiment, the generated sequential cipher is used to encrypt images, and better performance is achieved in terms of the key space analysis, the correlation analysis, the sensitivity analysis and the differential attack. To evaluate the efficiency of our method, a comparative study is performed with a prevalent method called “logistic map.” Our approach achieves a better performance on running time estimation. The experimental results are promising as the proposed method could promote the development of image protection in computer security.

     

A new neural network technique for the design of multilayered microwave shielded bandpass filters

In this work, we propose a novel technique based on neural networks, for the design of microwave filters in shielded printed technology. The technique uses radial basis function neural networks to represent the non linear relations between the quality factors and coupling coefficients, with the geometrical dimensions of the resonators. The radial basis function neural networks are employed for the first time in the design task of shielded printed filters, and permit a fast and precise operation with only a limited set of training data. Thanks to a new cascade configuration, a set of two neural networks provide the dimensions of the complete filter in a fast and accurate way. To improve the calculation of the geometrical dimensions, the neural networks can take as inputs both electrical parameters and physical dimensions computed by other neural networks. The neural network technique is combined with gradient based optimization methods to further improve the response of the filters. Results are presented to demonstrate the usefulness of the proposed technique for the design of practical microwave printed coupled line and hairpin filters. © 2008 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2009.     

Neural network design based on evolutionary programming

In this paper, a new design method for neural networks is presented based on evolutionary programming. By using an evolutionary algorithm the structure and weights of static neural networks can be simultaneously acquired. This method is further extended to design recurrent neural networks through introducing ‘delayed links’ into networks. Simulation results are also given to illustrate the efficiency of the proposed method.     

可重构密码协处理器的组成与结构

文章提出了一些关于可重构密码协处理器的组成与结构的设计思想和方法。可重构密码协处理器组成与结构是指可重构密码协处理器的组成模块及其相互之间的连接网络。可重构密码协处理器组成与结构的设计直接影响到可重构密码协处理器的性能,因此是可重构密码协处理器设计中的一个关键问题。     

Neural networks applications in concrete structures

This paper presents and discusses the applications of neural networks in concrete structures. It aims at introducing neural networks applications in structural design. The paper covers two applications of neural networks in concrete structures. Backpropagation networks are chosen for the proposed network, which is written using the programming package MAT-LAB. The overall results are compared and observed for the performance of the networks. Based on the applications it was found that neural networks are comparatively effective for a number of reasons, which include the amount of CPU memory consumed by neural networks is less than that consumed by conventional methods and their ease of use and implementation, neural networks provide both the users and the developers more flexibility to cope with different kinds of problems.     

基于神经网络的严反馈块非线性系统的鲁棒控制

针对非匹配不确定性的严反馈块非线性系统,基于神经网络提出一种鲁棒控制方法．利用Lyapunov稳定性定理推导出RBF神经网络的全调节律,用于处理系统中的非线性参数不确定性,提高了神经网络的在线逼近能力;采用神经网络和鲁棒控制方法,利用已知信息的同时,对控制系数矩阵未知时的设计问题进行处理,避免了控制器可能的奇异问题;引入非线性跟踪微分器,解决了Backstepping设计中的“计算膨胀”问题．运用Lyapunov稳定性定理证明了闭环系统的所有信号均最终一致有界．     

Finding the differential characteristics of block ciphers with neural networks

We have developed a model to represent the differential operation of block ciphers in order to help finding differential characteristics. Through this model, the whole space of differential characteristics for a block cipher is represented by a multi-level weighted directed graph. In this way, the problem of finding the best differential characteristic for a block cipher reduces to the problem of finding the minimum-weight multi-branch path between two known nodes in the proposed graph. In this paper, we use recurrent neural networks to find such a path in the differential operation graph of a block cipher. The path is found through minimization of the network cost function. We use the Hopfield network and the Boltzmann machine with and without chaos to minimize the cost function. Chaos is introduced to assist the network to escape from the local minima of the cost function. Experimental results indicate the usefulness of the approach and comparison of the performance of the used techniques shows that the Boltzmann machine algorithm incorporating simulated annealing produces the best result.     

Probabilistic design of layered neural networks based on theirunified framework

Proposes three ways of designing artificial neural networks based on a unified framework and uses them to develop new models. First, the authors show that artificial neural networks can be understood as probability density functions with parameters. Second, the authors propose three design methods for new models: a method for estimating the occurrence probability of the inputs, a method for estimating the variance of the outputs, and a method for estimating the simultaneous probability of inputs and outputs. Third, the authors design three new models using the proposed methods: a neural network with occurrence probability estimation, a neural network with output variance estimation, and a probability competition neural network. The authors' experimental results show that the proposed neural networks have important abilities in information processing; they can tell how often a given input occurs, how widely the outputs are distributed, and from what kinds of inputs a given output is inferred.     

Design of neural networks for fast convergence and accuracy:dynamics and control

A procedure for the design and training of artificial neural networks, used for rapid and efficient controls and dynamics design and analysis for flexible space systems, has been developed. Artificial neural networks are employed, such that once property trained, they provide a means of evaluating the impact of design changes rapidly. Specifically, two-layer feedforward neural networks are designed to approximate the functional relationship between the component/spacecraft design changes and measures of its performance or nonlinear dynamics of the system/components. A training algorithm, based on statistical sampling theory is presented, which guarantees that the trained networks provide a designer-specified degree of accuracy in mapping the functional relationship. Within each iteration of this statistical-based algorithm, a sequential design algorithm is used for the design and training of the feedforward network to provide rapid convergence to the network goals. Here, at each sequence a new network is trained to minimize the error of previous network. The proposed method should work for applications wherein an arbitrary large source of training data can be generated. Two numerical examples are performed on a spacecraft application in order to demonstrate the feasibility of the proposed approach.     

Adaptive L_2 control of nonlinear systems using neural networks

An adaptive neural network controller is developed to achieve output-tracking of a class of nonlinear systems. The global L2 stability of the closed-loop system is established. The proposed control design overcomes the limitation of the conventional adaptive neural control design where the modeling error brought by neural networks is assumed to be bounded over a compact set. Moreover,the generalized matching conditions are also relaxed in the proposed L2 control design as the gains for the external disturbances entering the system are allowed to have unknown upper bounds.     

基于子空间信息量准则的前向神经网络设计理论与方法研究

从理论上提出了子空间信息量(SIQ)及其准则(SIQC)的概念；在此基础上阐述了基于上述准则的前向神经网络设计的相关理论，包括前向神经网络隐含层信息量(HLIQ)、存在性和逼近定理，给出了选择隐含层神经元数、权值向量集和隐含层激励函数的指导方向；提出了基于上述理论的一种可行的次优网络设计算法；最后，详细分析了网络性能指标及其影响因素，上述理论和方法完全克服了传统学习算法的各种弊端，丰富了前向神经网络设计领域的理论依据，具有较大的理论指导和实际应用价值，文中通过具体实例验证了上述理论和方法的可行性和优越性．     

Adaptive L2 control of nonlinear systems using neural networks

An adaptive neural network controller is developed to achieve output-tracking of a class of nonlinear systems. The global L2 stability of the closed-loop system is established. The proposed control design overcomes the limitation of the conventional adaptive neural control design where the modeling error brought by neural networks is assumed to be bounded over a compact set.Moreover,the generalized matching conditions are also relaxed in the proposed L2 control design as the gains for the external disturbances entering the system are allowed to have unknown upper bounds.