如何防范算法求逆

假如您不幸遇到对 Win 32 应用环境有足够了解的对手,以至于您的软件最终还是被凶悍的调试器任意蹂躏。但是您还远没有被打败,如果反调试技术(Anti-Debug)作为软件保护的第一道防线已经失守,您的对手只不过是掌握了一大堆汇编代码而已,毕竟代码和算法之间还是有相当距离的,所以您还有第二道防线可守——抗分析。在这道防线里,您有很多办法可以限制破解者掌握您的加密算法,从而阻止注册机或者破解补丁的出现。     

单向Hash函数的一种构造方法

单向Ｈａｓｈ函数已成为密码学的一个重要组成部分，给定任一定长单向Ｈａｓｈ函数ｆ：∑＾ｍ→∑，ｍ〉ｔ，本文给出了利用ｆ构造一单向Ｈａｓｈ函数Ｆ的一种新方法，该方法易于并行化。     

一般实矩阵求逆的几种算法及实用程序

对矩阵求逆的选全土元变量置换法、ＬＵ分解法、Ｈｏｕｓｅｈｏｌｄｅｒ变找法及共轭斜量法这四种算法并给实用程序,并从运算速度、运算精度和占用存储空间大小诸方面作了简单比较。     

一种简单实用的单向HASH函数

介绍了单向ＨＡＳＨ函数的基本概念及其应用,在ＳＨＡ－１的基础上提出了一个简化的单向ＨＡＳＨ函数,并将其应用于智能卡应用系统中。     

矩阵求逆算法在Cell上的并行

本文分析和介绍了如何在Cell上实现矩阵求逆的并行算法,从而提高矩阵求逆的运算速度,提到的矩阵求逆并行算法对于其他的多核并行处理器具有通用性。     

单向累计函数技术分析

单向累计函数是与密码学密切相关的一门新兴技术。本文综述了单向累积函数及其相关的基本技术。详细分析了单向累积函数的构造方法,包括RSA单项累积函数和无冲突单向累积函数。在讨论了单向累积函数的技术发展方向之后,对其应用前景和应用领域做了详细介绍。     

STAP中的协方差矩阵求逆快速算法研究

研究机载雷达平台问题,空时自适应处理(STAP)是新一代机载相控阵雷达杂波抑制与目标检测的关键技术.在工程化过程中.运算量巨大是其面临的首要问题.为了改善STAP在采样矩阵维数较高情况下求逆运算的实时性能和有效提高雷达抑制杂波和抗干扰能力,提出了一种改进的Strassen矩阵求逆算法,算法结合了Strassen矩阵求逆的高效性以及采样协方差矩阵是Hermite正定阵的特点,具有运算最小,算法结构简单,便于工程实现的优点.DSP处理器的实测数据证明了方法的有效性和可行性.结果表明与目前工程中改进方法性能改善效果明显.     

基于单向函数的动态密钥分存方案

给出了一个基于单向函数的动态(t,n)-门限方案,它具有下述特点:(1) 系统在更新系统密钥时,无须更改每个成员的子密钥;(2) 当某个成员的子密钥泄密时,系统只需为该成员重新分配子密钥而不必更改其他成员的子密钥;(3) 当有新成员加入时,系统只需为新成员分配一个子密钥,而其他成员不受任何影响;(4) 子密钥可无限制地多次使用;(5) 只需公开n+1个信息(在需要确认欺骗者时需公开2n+1个);(6) 恢复系统密钥时,采用并行过程.     

单向散列函数的原理、实现和在密码学中的应用

简要介绍了单向散列函数的有关理论及实现情况 ,并且以密码学中广泛应用的单向散列函数MD5为例 ,详细介绍了它的原理和实现过程。最后简要介绍了单向散列函数在当前的应用 ,并且提出了一种利用单向散列函数实现的新的用户密钥管理方案     

基于FPGA的GF(2m)域求逆算法的设计研究

有限域GF(2m)上的椭圆曲线密码体制以其密钥短、安全强度高的优点获得了广泛的重视和应用,该密码体制域运算中最费时的运算是有限域上的求逆运算。论文提出一种基于多项式基乘法和平方的FPGA快速求逆设计方法,并给出了面积与速度的比较分析。     

单向壳核函数在密码学中的应用

针对密钥交换在密码学中的重要地位,该文提出一种新型的公钥密码体制——单向壳核函数。根据单向壳核函数的结构,给出了三种密钥约定方案。相对于传统的公钥密码体制,单向壳核函数具有较高的安全性,为人们提供了一种包容性更广的公钥密码体制。     

基于高斯混合模型的非平衡数据对称翻转算法

针对传统分类器对于非平衡数据的分类效果存在的问题,提出了一种基于高斯混合模型-期望最大化(GMM-EM)的对称翻转算法.该算法的核心思想是基于概率论中的"3σ法则"使数据达到平衡.首先,利用高斯混合模型和EM算法得到多数类与少数类数据的密度函数;其次,以少数类数据的均值为对称中心,根据"3σ法则"确定多数类侵入少数类的翻转边界,进行数据翻转,同时剔除与翻转区间中少数类原始数据数据重复的点;此时,若两类数据不平衡,则在翻转区域内使用概率密度增强方法使数据达到平衡.最后,从UCI、KEEL数据库中选取的14组数据使用决策树分类器对平衡后的数据进行分类,实例分析表明了该算法的有效性.     

Intelligent Selection of Instances for Prediction Functions in Lazy Learning Algorithms

Lazy learning methods for function prediction use different prediction functions. Given a set of stored instances, a similarity measure, and a novel instance, a prediction function determines the value of the novel instance. A prediction function consists of three components: a positive integer k specifying the number of instances to be selected, a method for selecting the k instances, and a method for calculating the value of the novel instance given the k selected instances. This paper introduces a novel method called k surrounding neighbor (k-SN) for intelligently selecting instances and describes a simple k-SN algorithm. Unlike k nearest neighbor (k-NN), k-SN selects k instances that surround the novel instance. We empirically compared k-SN with k-NN using the linearly weighted average and local weighted regression methods. The experimental results show that k-SN outperforms k-NN with linearly weighted average and performs slightly better than k-NN with local weighted regression for the selected datasets.     

Developing the concept of one-way functions for cryptographic security systems using achievements in chaotic dynamics

The paper shows that an “informal” interpretation of one-way functions in modern cryptography is inadequate and defines such functions in terms of information theory. This combination of complexity and information theories opens new opportunities for constructing one-way functions, whose one-way transformation is based on the ambiguity of their inverse mappings. It is shown that random mappings are promising candidates for constructing such functions. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 6, pp. 136–146, November–December 2006.     

Scheduling Functions for Spatiotemporal Mapping of d-Dimensional Algorithms with Homogeneous Dependences on (d-2)-Dimensional Parallel Architectures

A formalized method is proposed for construction of scheduling functions for spatiotemporal mapping of d-dimensional algorithms represented by systems of homogeneous recurrence equations on (d-2)-dimensional parallel architectures. Basic operations of the algorithms are scheduled separately by means of functions with rational coefficients.     

Bounded Consensus Algorithms for Multi‐Agent Systems in Directed Networks

In this paper, the consensus problem is investigated via bounded controls for the multi‐agent systems with or without communication. Based on the nested saturation method, the saturated control laws are designed to solve the consensus problem. Under the designed saturated control laws, the transient performance of the closed‐loop system can be improved by tuning the saturation level. First of all, asymptotical consensus algorithms with bounded control inputs are proposed for the multi‐agent systems with or without communication delays. Under these consensus algorithms, the states’ consensus can be achieved asymptotically. Then, based on a kind of novel nonlinear saturation functions, bounded finite‐time consensus algorithms are further developed. It is shown that the states’ consensus can be achieved in finite time. Finally, two examples are given to verify the efficiency of the proposed methods.     

Novel Neuronal Activation Functions for Feedforward Neural Networks

Feedforward neural network structures have extensively been considered in the literature. In a significant volume of research and development studies hyperbolic tangent type of a neuronal nonlinearity has been utilized. This paper dwells on the widely used neuronal activation functions as well as two new ones composed of sines and cosines, and a sinc function characterizing the firing of a neuron. The viewpoint here is to consider the hidden layer(s) as transforming blocks composed of nonlinear basis functions, which may assume different forms. This paper considers 8 different activation functions which are differentiable and utilizes Levenberg-Marquardt algorithm for parameter tuning purposes. The studies carried out have a guiding quality based on empirical results on several training data sets.     

Genetic Algorithms for Project Management

The scheduling of tasks and the allocation of resource in medium to large-scale development projects is an extremely hard problem and is one of the principal challenges of project management due to its sheer complexity. As projects evolve any solutions, either optimal or near optimal, must be continuously scrutinized in order to adjust to changing conditions. Brute force exhaustive or branch-and-bound search methods cannot cope with the complexity inherent in finding satisfactory solutions to assist project managers. Most existing project management (PM) techniques, commercial PM tools, and research prototypes fall short in their computational capabilities and only provide passive project tracking and reporting aids. Project managers must make all major decisions based on their individual insights and experience, must build the project database to record such decisions and represent them as project nets, then use the tools to track progress, perform simple consistency checks, analyze the project net for critical paths, etc., and produce reports in various formats such as Gantt or Pert charts.Our research has developed a new technique based on genetic algorithms (GA) that automatically determines, using a programmable goal function, a near-optimal allocation of resources and resulting schedule that satisfies a given task structure and resource pool. We assumed that the estimated effort for each task is known a priori and can be obtained from any known estimation method such as COCOMO. Based on the results of these algorithms, the software manager will be able to assign tasks to staff in an optimal manner and predict the corresponding future status of the project, including an extensive analysis on the time-and-cost variations in the solution space. Our experiments utilized Wall's GALib as the search engine. The algorithms operated on a richer, refined version of project management networks derived from Chao's seminal work on GA-based Software Project Management Net (SPMnet). Generalizing the results of Chao's solution, the new GA algorithms can operate on much more complex scheduling networks involving multiple projects. They also can deal with more realistic programmatic and organizational assumptions. The results of the GA algorithm were evaluated using exhaustive search for five test cases. In these tests our GA showed strong scalability and simplicity. Its orthogonal genetic form and modularized heuristic functions are well suited for complex conditional optimization problems, of which project management is a typical example.     

求解非线性方程组的蛙跳和 BFGS 混合算法

混合蛙跳算法具有算法简单、控制参数少、易于实现等优点,但缺乏良好的局部细化搜索能力,使得求解精度不高。借鉴BFGS算法强的局部搜索能力,将BFGS算法与混合蛙跳算法有机融合,形成性能更优的混合优化算法,并用来求解非线性方程组。通过3个非线性方程组的实验表明,该混合算法收敛精度较高,收敛速度较快,是一种较好的求解非线性方程组的方法。