计算机网络入侵检测系统的多模式匹配算法

为了使网络入侵检测系统能够在高速网络环境中有效的开展工作,实现计算机网络入侵检测系统的多模式匹配算法优化设计.首先,对网络入侵检测的算法与原理进行全面分析.其次,对网络入侵检测系统多模式匹配算法的优化思想进行描述,描述多模式匹配算法,对算法进行实现,使模式匹配算法效率得到提高,以此提高系统检测能力.通过测试结果表示,优化后多模式匹配算法能够使网络检测系统的检测性能得到提高.     

一种针对网络入侵检测系统的字符串匹配算法

精确的字符串匹配算法对网络入侵检测系统的性能有重要的影响,为了提高其效率,这里设计了一个专门针对网络入侵检测系统的字符匹配算法,并在snort1.9中实现。和目前最好的替代算法相比较,试验表明此算法能提高NIDS性能10%～40%。     

入侵检测系统中模式匹配算法的改进

入侵检测系统作为防火墙的合理补充,已经发展成为网络安全体系中的一个关键性组件.网络技术的飞速发展给入侵检测系统提出新的挑战,需要通过各种途径来提高系统性能,而模式匹配算法的优劣直接影响到入侵检测系统的核心模块——规则匹配模块的运行效率.通过对原有BM算法的深入分析,从如何增大模式不匹配时的滑动距离这一点出发,对BM算法进行改进与实现,并通过实验证明该方法提高了匹配效率.     

改进的模式匹配算法在入侵检测中的应用

随着网络技术的高速发展,网络安全问题日益突出,入侵检测技术成为当今关注的焦点。模式匹配算法的性能对入侵检测系统影．响很大。在分析现有模式区配算法的基础上,提出了改进的AC_BM算法,该算法在文本与模式某次匹配失败后,跳过尽可能多的字符,实现更快的匹配过程。实验证明,改进后的算法大大提高了检测的性能。     

入侵检测系统中的快速多模式匹配算法

网络入侵检测系统常常依赖于精确的模式匹配技术，依赖于算法的选择、实现以及使用频率。这种模式匹配技术可能成为入侵检测系统的瓶颈，为了跟上快速增长的网络速度和网络流量，Snort(开放源代码的网络入侵检测系统)中采用了快速多模式匹配算法，本文描述了Snort中一种引入注目的快速多模式匹配算法及其对系统性能的改进。     

基于机器学习和NetFPGA的智能高速入侵防御系统

当前网络安全面临着日益多样化的威胁和挑战。入侵防御系统作为一种新兴的、能够动态监视并及时阻断异常网络传输行为的网络安全设备,成为目前主要的研究方向。目前主流的入侵防御系统主要通过人工预设的入侵规则集合对网络流进行匹配来发现、处理入侵,这种方法效率低下、维护困难,且存在严重的处理速度与成本的矛盾。针对上述问题,文章提出了结合基于硬件的网络数据流高速捕获过滤、经典机器学习技术以及当前人工智能领域前沿的深度学习自编码技术的入侵检测新思路,实现了基于NetFPGA的智能、高速的网络入侵防御系统,并在测试中取得了优于其他同一成本水平入侵检测系统的结果。     

基于BM算法的无需预处理的双向匹配算法的研究与实现

基于特征匹配技术的入侵检测系统的速率和效率常常依赖于模式匹配算法的精确性,而算法的效率又依赖于算法的选择和实现方式。随着网络技术的发展,匹配算法优劣有可能成为入侵检测系统的瓶颈,因此要提高入侵检测系统的性能必须对原有算法改进或提出新的算法,本文在对经典BM算法分析、研究的基础上,对该算法进行了部分改进,并给出了基于该改进的新的匹配算法。     

网络安全中入侵检测算法研究

伴随网络技术的迅猛发展,世界走向移动互联时代,网络安全的地位愈发不可小觑,作为网络安全的核心技术之一,笔者就入侵检测系统中的算法进行了一系列的研究,在文中概述了传统及新兴的检测算法,并对入侵检测系统的发展要点进行了分析。     

基于排除算法型入侵检测系统的改进

入侵检测系统能有效防御网络上多数病毒和非法入侵,但也存在效率问题,特别是在高速网络环境下对检测效率要求非常高。文章提出了一种基于排除算法的模式匹配算法,对入侵检测系统中模式匹配算法进行了改进,使得在实际应用中调用标准模式匹配算法大为减少,提高了系统效率。     

一种基于网络处理器的IDS的设计方案

入侵检测是近年来网络安全研究的重点,IXP2400是Intel公司推出的新一代网络处理器,具有较高的处理性能和较好的可扩展性。本文描述了一种基于IXP2400的入侵检测系统(IDS)的设计方案,采用了高效的特征匹配算法,使整个系统的处理性能达到高速网络环境的要求。     

Ensemble of Evolving Neural Networks in Classification

The ensemble of evolving neural networks, which employs neural networks and genetic algorithms, is developed for classification problems in data mining. This network meets data mining requirements such as smart architecture, user interaction, and performance. The evolving neural network has a smart architecture in that it is able to select inputs from the environment and controls its topology. A built-in objective function of the network offers user interaction for customized classification. The bagging technique, which uses a portion of the training set in multiple networks, is applied to the ensemble of evolving neural networks in order to improve classification performance. The ensemble of evolving neural networks is tested by various data sets and produces better performance than both classical neural networks and simple ensemble methods.     

模式匹配算法的研究与改进

随着网络的迅速发展,网络安全问题日益突出,入侵检测技术也成为当今社会关注的焦点。在基于规则的入侵检测系统中,模式匹配算法非常重要,它直接影响到系统的准确性和实时性能。介绍了BM算法和BMH算法,对BM算法的改进进行了研究,并提出一种改进的BM算法。改进后的算法运用到入侵检测系统中极大地提高了系统的检测性能。     

改进的粒子群优化算法设计FIR低通数字滤波器

粒子群优化算法(PSO)因具有参数少、易于实现等优点,在解决优化问题时表现出很好的性能。有限长单位脉冲响应(FIR)数字滤波器因具有稳定的结构、易于实现等优点,在实际中有着很广泛的应用。因此,将基于三角函数因子的改进PSO算法(TFPSO)用于对FIR低通数字滤波器性能的优化,并将其与基于折射原理反向学习(refrPSO)、基于反向学习(OPSO)的PSO算法所设计的FIR低通数字滤波器的性能进行比较。在实验中构造出一种性能较好的适应值函数,以验证这几种改进的PSO算法所设计的FIR低通数字滤波器的性能。实验结果表明,基于三角函数因子的PSO算法滤波性能较差,而基于折射原理反向学习的PSO算法性能最佳。     

Towards probabilistic footy tipping: a hybrid approach utilising genetically defined neural networks and linear programming

Using readily available data from the 1992–1995 Australian Football League season, we have developed a model that will readily predict the winner of a game, together with the probability of that win. This model has been developed using a genetically modified neural network to calculate the likely winner, combined with a linear program optimisation to determine the probability of that occurring in the context of the tipping competition scoring regime. This model has then been tested against 484 tippers in a probabilistic tipping competition for the 2002 season. We have found that the performance of the combined neural network, linear program model compared most favorably with other model based tipping programs and human tippers.     

模式匹配算法的研究与改进

随着网络的迅速发展，网络安全问题日益突出，入侵检测技术也成为当今社会关注的焦点。对于基于规则的入侵检测来说，模式匹配算法非常重要，它直接影响到系统的准确性和实时性能。本文介绍了KMP和BM算法，对BM算法的改进进行了研究，并提出一种改进的BM算法。改进后的算法运用到入侵检测系统模型中极大地提高了检测性能。     

Development of a parsimonious GA-NN ensemble model with a case study for Charpy impact energy prediction

A parsimonious genetic algorithm guided neural network ensemble modelling strategy is presented. Each neural network candidate model to participate in the ensemble model is structurally selected using a genetic algorithm. This provides an effective route to improve the performance of the individual neural network models as compared to more traditional neural network modelling approaches, whereby the neural network structure is selected through some trial-and-error methods or heuristics. The parsimonious neural network ensemble modelling strategy developed in this paper is highly efficient and requires very little extra computation for developing the ensemble model, thus overcoming one of the major known obstacles for developing an ensemble model. The key techniques behind the implementation of the ensemble model, include the formulation of the fitness function, the generation of the qualified neural network candidate models, as well as the specific definitions of the assemble strategies. A case study is presented which exploits a complex industrial data set relating to the Charpy impact energy for heat-treated steels, which was provided by Tata Steel Europe. Modelling results show a significant performance improvement over the previously developed models for the same data set.     

基于改进的贝叶斯中文垃圾邮件过滤方法研究

基于内容的垃圾邮件过滤问题是Internet安全技术研究的一个重点问题,而基于贝叶斯的分类方法在垃圾邮件处理上表现了很高的准确度,因此受到了广泛的关注。本文将一种基于模拟退火遗传算法的贝叶斯分类方法引入到中文垃圾邮件过滤中,提高了分类的精确度。实验证明,算法在邮件过滤中有更好的表现。     

Partition-based fuzzy median filter based on adaptive resonance theory

This paper presents a novel partition-based fuzzy median filter for noise removal from corrupted digital images. The proposed filter is obtained as the weighted sum of the current pixel value and the output of the median filter, where the weight is set by using fuzzy rules concerning the state of the input signal sequence to indicate to what extent the pixel is considered to be noise. Based on the adaptive resonance theory, the authors developed a neural network model and created a new weight function where the neural network model is employed to partition the observation vector. In this framework, each observation vector is mapped to one of the M blocks that form the observation vector space. The least mean square (LMS) algorithm is applied to obtain the optimal weight for each block. Experiment results have confirmed the high performance of the proposed filter in efficiently removing impulsive noise and Gaussian noise.     

A block-diagonal recurrent fuzzy neural network for system identification

A recurrent fuzzy neural network with internal feedback is suggested in this paper. The network is entitled dynamic block-diagonal fuzzy neural network (DBD-FNN), and constitutes a generalized Takagi-Sugeno-Kang fuzzy system, where the consequent parts of the fuzzy rules are small Block-Diagonal Recurrent Neural Networks. The proposed model is applied to a benchmark identification problem, where a dynamic system is to be identified. Additionally, an application of the proposed model to the problem of the analysis of lung sounds is presented. Particularly, a filter based on the DBD-FNN is developed, trained with the RENNCOM method. Extensive experimental and simulation results are given and performance comparisons with a series of other models are conducted, highlighting the modeling characteristics of DBD-FNN as an identification tool and the effectiveness of the proposed separation filter.     

Multi‐objective memetic algorithm: comparing artificial neural networks and pattern search filter method approaches

In this work, two methodologies to reduce the computation time of expensive multi‐objective optimization problems are compared. These methodologies consist of the hybridization of a multi‐objective evolutionary algorithm (MOEA) with local search procedures. First, an inverse artificial neural network proposed previously, consisting of mapping the decision variables into the multiple objectives to be optimized in order to generate improved solutions on certain generations of the MOEA, is presented. Second, a new approach based on a pattern search filter method is proposed in order to perform a local search around certain solutions selected previously from the Pareto frontier. The results obtained, by the application of both methodologies to difficult test problems, indicate a good performance of the approaches proposed.