银行计算机系统维护的探讨

从机房建设、通讯线路、日常维护、诊断维修等几个方面探讨了银行计算机系统维护中的重点和难点     

论银行计算机系统安全性问题

随着银行电子化建设水平的不断提高，银行计算机信息系统的安全性问题显得愈来愈重要本文论述了银行计算机网络犯罪问题的特点，作案分子的手段与人员的职业分布，并根据银行计算机网络的特点，从技术与管理两方面提出了一些防范计算机犯罪的措施。     

银行计算机系统安全运行规范研究

随着我国金融体制改革的进程不断深化发展,商业银行要想在激烈的社会竞争中占有一席之地,在自身的体制改革的过程中,应当在银行业务的创新发展过程中着力吸收并引起计算机及其相关网络信息技术的最新成果,并不断完善发展其实际应用效果。就计算机技术在银行产业中的应用前景而言,银行计算机系统安全运行是当前工作人员所应当重点解决的问题。文章通过简要介绍当前在银行计算机系统安全运行的主要威胁,针对这些威胁因素尝试探索出行之有效的计算机系统安全运行规范。     

银行计算机系统的安全与防范

近年来,我国银行计算机系统正以前所未有的速度迅速普及。银行计算机系统的安全与防范,已显得非常迫切,稍有纰漏,就会给国家和银行客户造成损害,甚至会影响整个经济的发展。１计算机系统的安全问题银行计算机系统的安全有其特殊的要求,但离不开一般计算机系统的安全问题。它的安全性主要体现在保密性、可获性、完整性、可靠性。保密性指的是防止未经授权的对物理设备和数据资源非法访问。可获性指的是对合法用户,允许他们在许可的范围之内,获得完全无阻碍地访问物理设备及数据资源的权力。完整性指的是对数据资源的任何更改,应当保…     

银行计算机系统存在的风险与防范措施

随着现代计算机系统及其高新技术的不断发展,各行各业中计算机的使用都变得普遍化和大众化,银行也不例外,计算机网络技术的广泛使用有力地促进了银行各项业务的发展,同时也带了计算机系统的风险。主要对银行计算机系统存在的风险与有效措施进行了探讨。     

实现Linux架构下的防火墙扩展技术及入侵检测

介绍了基于Linux netfilter/iptables架构实现机制和扩展技术，在此基础上提出扩展匹配选项实现防火墙的入侵检测功能，扩充后的防火墙可以像Snort一样具有入侵检测功能，从而扩展了防火墙的安全控制功能，并且可将Snort规则转化为防火墙规则实现防火墙规则集的扩充。     

Simulation of an industrial computer system—A case study

The purpose of this work was to explain long term variations in computer system performance by developing and using a simple discrete event simulation model. The system studied was an IBM 370/165 (with OS/MVT) used by a major U.S. corporation over the 3 yr period of 1972 through 1974. The model was calibrated and validated against data from the actual system and was successfully used to develop relationships between work load, hardware configuration, and performance. These results were used to establish the cause of major degradations in service levels that had been experienced on the actual system and provide insight into functional dependencies otherwise unobtainable. The study demonstrated that a reasonable match between the behavior of a complex computer system in the real and simulated worlds can be obtained by using straightforward simulation techniques and readily available performance data.     

Keybam—a system of interactive computer programs for use with the petros petrochemical data bank

KEYBAM is a system of interactive FORTRAN IV programs for accessing and operating on major-element whole-rock chemical analyses stored in data bank PETROS. KEYBAM's capabilities include subfile creation based on user-supplied criteria, normative calculations and rock classification, graphical displays including histograms, X—Y plots, triangular plots, and various statistical analyses based on the SPSS System. We have attempted to design KEYBAM so that it is machine independent and can be used for research and teaching in petrology at most modern computer installations.     

Multimicroprocessor computer system

The structure of CTL's series 8000 Computer range is described. Utilization of distributed processing techniques has allowed a high performance system to be designed. This approach benefits both designer and user.     

Soft computing system for bank performance prediction

This paper presents a soft computing based bank performance prediction system. It is an ensemble system whose constituent models are a multi-layered feed forward neural network trained with backpropagation (MLFF-BP), a probabilistic neural network (PNN) and a radial basis function neural network (RBFN), support vector machine (SVM), classification and regression trees (CART) and a fuzzy rule based classifier. Further, principal component analysis (PCA) based hybrid neural networks, viz. PCA-MLFF-BP, PCA-PNN and PCA-RBF are also included as constituents of the ensemble. Moreover, GRNN and PNN were trained with a genetic algorithm to optimize the smoothing factors. Two ensembles (i) simple majority voting based and (ii) weightage based are implemented. This system predicts the performance of a bank in the coming financial year based on its previous 2-years’ financial data. Ten-fold cross-validation is performed in the training sessions and results are validated with an independent production set. It is demonstrated that the ensemble is able to yield lower Type I and Type II errors compared to its constituent models. Further, the ensemble also outperformed an earlier study [P.G. Swicegood, Predicting poor bank profitability: a comparison of neural network, discriminant analysis and professional human judgement, Ph.D. Thesis, Department of Finance, Florida State University, 1998] that used multivariate discriminant analysis (MDA), MLFF-BP and human judgment.