潘加宇--《软件方法》电子书

计算机软件操作技巧的高级技术指导第一章：操作系统的高级技巧在计算机软件操作中，操作系统扮演着重要的角色。

以下是一些操作系统的高级技巧，可以提升工作效率和系统性能。

1.资源管理：学会合理管理计算机的资源，包括内存、处理器和磁盘空间等。

通过优化资源的使用方式，可以提高计算机的运行速度和稳定性。

2.任务调度：掌握任务调度的技巧可以提高系统的响应速度和处理效率。

了解操作系统的优先级设置和调度算法，可以合理安排任务的执行顺序，减少等待时间。

3.命令行操作：通过熟练使用命令行工具，可以快速完成一些常见的操作。

比如，通过命令行工具执行批量操作、自动化脚本，可以提高工作效率。

第二章：办公软件的高级技巧办公软件是日常工作中必不可少的工具，在掌握基本操作的基础上，以下是一些办公软件的高级技巧。

1.Microsoft Office技巧：Microsoft Office套件是最常用的办公软件之一，掌握一些高级技巧可以提高工作效率。

-Word：学会使用快捷键、样式更改、更高级的格式设置，以及自动化功能等。

-Excel：掌握数据透视表、宏、公式和函数等高级功能，以及批处理操作等。

-PowerPoint：熟悉动画和过渡效果的设置，设计专业演示文稿等。

2.其他办公软件技巧：除了Microsoft Office，还有许多其他办公软件，如Google Docs、LibreOffice等。

学会使用这些软件的高级功能，可以提高工作的灵活性和效率。

第三章：图像处理软件的高级技巧图像处理软件在设计和编辑领域中得到广泛应用。

以下是一些图像处理软件的高级技巧。

1.Adobe Photoshop技巧：作为最流行的图像处理软件之一，掌握Adobe Photoshop的高级技巧对于设计师和摄影师来说非常重要。

-图像编辑：掌握各种复杂的选择、修饰和合成技术，对图像进行精确的调整和修复。

-图层和蒙版：熟练使用图层和蒙版功能，实现更复杂的图像合成和编辑。

2022年硕士研究生复试考试大纲学科名称： 软件工程 学科代码：[0835] 本考试科目考试时间120分钟，满分200分。

包括：面向对象设计（30分）、计算机网络（40分）、系统分析与设计（30分）、操作系统（30分）、软件过程与项目管理（40分）、算法设计与分析（30分）面向对象设计（30分）一、考试要求1. 要求考生系统地掌握面向对象的基本概念和基于面向对象技术的软件系统分析与设计技术，掌握统一建模语言（UML）及常用软件建模工具等方面的知识。

2. 针对实际问题能建立有应用价值的软件系统模型，即需求模型、分析模型、设计模型等，并进行优化。

3. 掌握对软件模型进行评价及验证的方法和过程。

二、考试内容1）面向对象与系统建模概述a:系统和软件系统，软件开辟模型和开辟各个阶段模型b:软件系统的结构化和面向对象的两种建模方法c:软件系统的建模过程2) 面向对象的建模概念与建模表示法a: 面向对象的概念b: 面向对象的建模c: 统一建模语言UML的含义、模型和结构3)基于用例技术的需求分析a: 需求与需求的活动b: 用例的建模思想和过程c:用例模型元素及其关系4)面向对象的分析技术a: 分析方法和分析原则b: 对象模型的建立，包括：确定类和对象、确定属性和操作、确定关联。

c: 动态模型的建立，包括：事件与消息，交互图、协作图，状态图、活动图。

5)面向对象设计a: 面向对象的设计方法，设计建模的原则。

b: 面向对象设计的软件体系结构c: 包图及设计包的原则，组件图、实施图三、试卷结构选择题(30分)四、参考书目1．Grady Booch, Robert A. Maksimchuk, et al. Object-Oriented Analysis and Design with Applications (3rd edition) (王海鹏，潘加宇译，人民邮电出版社2022)2．Joey George, Dinesh Batra, Joseph Valacich, Jeffrey Hoffer. Object-Oriented Systems Analysis and Design (2nd edition). (龚晓庆；张远军；陈峰译. 面向对象系统分析与设计(第2版). 清华大学出版社. 2022)《计算机网络》（40分）一、考试要求1．掌握计算机网络的基本概念、基本原理和基本方法。

基于UML的军事需求分析及建模方法研究李楠摘要：针对指挥信息系统建设规模大、集成度高、系统架构组成复杂、系统生命周期无界限等特点，本文提出一种基于UML的军事需求分析及建模方法，依次从军事能力需求、系统能力需求和技术需求三个层面对指挥信息系统的军事需求进行了分析和建模。

关键字：指挥信息系统军事需求需求建模UML引言指挥信息系统是运用以计算机为核心的各种技术设备，集指挥控制、情报侦察、预警探测、通信传输、电子对抗和其他信息保障于一体，可自动完成信息收集、传递、处理与分发，用于保障军队作战指挥和武器控制的电子信息系统。

指挥信息系统是一个复杂的人机交互系统，涉及硬件、软件和组织机构等诸多方面的学科和技术，因此指挥信息系统的综合集成建设必须以军事需求的总体规划为源头。

军事需求分析在指挥信息系统开发过程中具有方向性和决策性的意义，军事需求建模是军事需求分析过程中的一个重要阶段，一个定义良好的需求模型是保证指挥信息系统成功的前提之一。

军事需求建模通过切实有效的方法和技术对所获取军事需求中的行为、特征和相关约束进行抽取和精华，构建一个准确描述系统功能和约束的需求模型。

指挥信息系统建设通常以网络为核心，采用新旧系统共存的、开放式的、无界限的系统架构，规模不断扩大、集成度越来越高、系统架构组成越来越复杂，并且具有无限的系统生命周期，因此传统系统工程领域的需求开发过程无法适应其军事需求开发的需求。

本文提出了一种基于UML的需求分析及建模方法，以面向对象的思想依次从军事能力需求、系统能力需求和技术需求三个层面对指挥信息系统的军事需求进行梳理、分析和建模。

该方法以作战使命为需求牵引，按阶段逐步建立军事能力需求的作战任务模型，系统能力需求的指挥体制模型、作战活动模型和信息关联关系模型，技术需求的现状业务活动模型、改进业务活动模型和系统用例模型。

所提出的军事需求分析过程和建模方法能够充分满足指挥信息系统新旧共存、综合集成、无界限等复杂性特点，为系统的实际建设开发提供有益支持。

UML⽤例图说明前些时间参加了潘加宇⽼师的技术讲座，UML建模技术受益匪浅。

我也把平时的⼀些积累和上次的收获总结在这篇⽂章中，主要讲解⽤例图相关的知识。

⽤例图是软件需求分析到最终实现的第⼀步，它描述⽤户如何使⽤系统及使⽤系统什么样的功能。

⽤例图从业务⾓度上体现谁来使⽤系统、⽤户希望系统提供什么样的服务，以及⽤户需要为系统提供的服务，也便于软件开发⼈员最终实现这些功能。

⽤例图在开发中被⼴泛的应⽤，但是它最常⽤来描述系统提供了什么样的功能给什么样的⽤户使⽤。

在官⽅⽂档中⽤例图包含六个元素，分别是：执⾏者(Actor)、⽤例(Use Case)、关联关系(Association)、包含关系(Include)、扩展关系(Extend)以及泛化关系(Generalization)。

但是有些UML的绘图⼯具多提供了⼀种直接关联关系(DirectedAssociation)。

⽤例图可⼀个包含注释和约束，还可⼀个包含包，⽤于将模型中的元素组合成更⼤的模块。

有时，可以将⽤例的实例引⼊到图中。

⽤例图模型如下所⽰，执⾏者⽤⼈形图标来标识，⽤例⽤椭圆来表⽰，连线表⽰它们之间的关系。

⼀、执⾏者（Actor）1、执⾏者概念是指⽤户在系统中扮演的⾓⾊。

如图1-1是⼀个⽤户管理的⽤例图，图中的⽤户、管理员就是⽤例的执⾏者。

图1-12、从业务中找出执⾏者获取系统⽤例⾸先要找出系统的执⾏者。

我们可以通过⽤户回答⼀些问题的答案来识别执⾏者。

可以参考以下问题：1. 谁使⽤系统的主要功能（主要使⽤者）？2. 谁需要系统⽀持他们⽇常⼯作？3. 谁来维护、管理系统使其正常⼯作（辅助使⽤者）？4. 系统需要控制哪些硬件？5. 系统需要其他哪些系统交互？这⾥包含其他计算机系统或者应⽤程序。

6. 对系统产⽣结果感兴趣的是哪些⼈和哪些事物？3、执⾏者之间关系因为执⾏者是类，所以多个执⾏者之间可以具有与类相同的关系。

在⽤例图中，使⽤了泛化关系来描述多个执⾏者之间的公共⾏为。

《最后期限》书籍"这本关于治理的书读起来明白有味。

«最后期限»是一个创新性与趣味性并重的故事，每章结尾都有基于团队的项目治理方面的原那么。

"--John Aculley"……这是一部技术力作。

它涵盖了大量主题，从项目评估到度量标准，从相冲突的决定到处理模糊不清的说明…每章结尾的要点差不多足以让您这本书成为超值之选了……«最后期限»像Dilbert的漫画书一样有味，却没有那么讽刺。

更重要的是，书中包括一些深刻的聪慧，以及一些关心您实现最后期限的有用积极的建议。

我强烈举荐这本书。

"--Ed Yourdon 内容与特色：汤普金斯先生是一位体会丰富的项目经理，却也难免被炒鱿鱼的命运。

这时有人出双倍的价钱将他"请"到一个海上的小国治理六个软件项目，资金、人员、设备等所有外部条件都万事俱备，能够放手去做，还能够将自己的奇思妙想付诸实践。

本以为因祸得福，却逐步发觉情况并没有那么简单，最后期限看起来变得不可能，但他已骑虎难下……书中用一个虚构的故事阐述了真实世界中关于项目治理的一些原那么，尽管它并不能解决任何软件问题，但它绝对会让你对项目治理的重要原那么终生难忘。

它将看似高深莫测的项目治理理论以浅显易明白的方式展现出来，改变了项目治理书籍一贯枯燥乏味的形象，让你在轻松阅读小说的同时受益非浅。

书中每章都以汤普金斯先生的日记结尾，是他逐步收集的体会，有了这些，你也能够成为一个优秀的项目b经理。

本书前言在二十世纪三十年代，科罗拉多大学的物理学家乔治.伽莫夫开始撰写一系列关于"汤普金斯先生"--一位中年银行职员--的小故事。

故事中讲述的汤普金斯先生对现代科学专门感爱好。

他总是去听当地一位大学物理教授在晚上的演讲，然后在中途他必定会睡着。

当他醒来的时候，他会发觉自己身在另一个宇宙之中，在那儿某个物理常量发生了明显的变化。

《软件⽅法》潘加宇读书笔记设计源于需求却⾼于需求。

《软件⽅法》上册（五章）所表述的逻辑：愿景 ------ 业务建模 ------ 需求 ------ 分析 ------ 设计1. 愿景：明⽩软件的意义是什么，帮助或者提⾼了现有系统的那些⽅⾯，减少了那些成本。

利润 = 需求 - 设计这个公式成⽴的前提是需求都已经实现，不同的设计会花费不同的成本。

但看完上篇，我觉得应该改⼀改：利润 = 业务 - 设计。

整个软件制作的过程中，真正的价值和输出是业务，对业务有什么帮助、提⾼或者减少业务成本。

从业务的分析、愿景再到需求的分析之间有⼀定的距离和不同的理解⽅式，这⼀点也很重要。

2. 业务建模：研究业务⽤例和业务对象。

业务建模主要是研究这个组织，描述现在的业务事实，划分业务的边界，分析出各个参与⽅的现象及状况。

反过来给组织⼀个购买系统的理由，给系统赋予实际的价值。

注：业务和需求分析是两个⽅⾯，业务注重对现实的描述，需求是针对于系统的分析。

业务不⼀定和需求是⼀⼀对应的关系。

业务是现实的描述，⼀般是不会变化的；但需求可以⽤不同的⽅式实现，并且从不同的⾓度出发看业务时，看到的需求也是不⼀样的。

3. 需求：研究系统系。

统执⾏者，是指系统外与该系统发⽣功能性交互的其他系统。

系统⽤例，系统为执⾏者提供的、渉众可以接受的价值。

系统⽤例的粒度，从业务的⾓度出发去思考这个问题，⽤例就是为了给系统的执⾏者⽤。

第⼀章建模和UML1. 该章主要讲软件⽅法的价值和思路，然后确定本书的观点：建模和软件开发完美的⼯作流【业务建模-需求-分析-设计】2. 各个义务模块思考的焦点不⼀样，从上到下是有严格先后顺序的。

第⼆章愿景1. 回答问题，“在⽼⼤看来，为什么要引进这样的系统？”2. WHO的问题：分析清楚相关的涉⽅，涉及到的组织和系统，利益相关⽅。

3. WHY的问题：为什么要做这些，现在遇到的问题是什么，系统需要提⾼那⼀部分，或者减少那⼀部分的开销。

Distributed Intrusion Detection in Clouds Using Mobile AgentsAmir Vahid Dastjerdi The University of Melbourne Melbourne, Australia amirv@.auKamalrulnizam Abu BakarUniversiti Teknologi Malaysia (UTM)Johor Baru, Malaysiakamarul@fsksm.utm.mySayed Gholam Hassan TabatabaeiUniversiti Teknologi Malaysia (UTM)Johor Baru, Malaysiagtsayed2@siswa.utm.myAbstract—Cloud Computing extends an enterprise ability to meet the computing demands of its everyday operations, while offering flexibility, mobility and scalability. However, the reason that Chief Information Officers (CIOs) and their colleagues hesitate to let their business workloads to move from private Cloud into public Cloud is security. This work tries to offer a line of defense by applying Mobile Agents technology to provide intrusion detection for Cloud applications regardless of their locations. Therefore, CIOs feel safer to use Cloud to extend their on-premise infrastructure by adding capacity on demand.Keywords-Cloud Computing; Mobile Agent; Intrusion Detection SystemI.I NTRODUCTIONCloud Computing is becoming one of the next industry buzz words. Cloud computing builds upon advance of research in virtualization, distributed computing, grid computing and utility computing. It tries to satisfy user needs by providing infrastructure, platform and software as a service (IaaS, PaaS, SaaS). In addition, it offers on-demand services, reduced total cost of services and economy of scale. The key to the solution is an integrated framework that allows reliable, scalable (ability to scale a solution to achieve economy of scale) and reconfigurable aggregation, sharing, and allocation of software (SaaS), computational, storage and networking resources on-demand.For Cloud users it is extremely vital to feel safe and comfortable when they use Cloud resources to hold their software, data and processes. On the other hand, Mobile Agents have special characteristics which can help intrusion detection in several ways in Cloud Computing environments. The use of Mobile code and Mobile Agents computing paradigms have been proposed in several researches [2, 3, 4].In this paper, we propose a Mobile Agent based intrusion detection system (IDS) which can be applied by Cloud clients, and has been very much customized for Cloud Computing environment in order to satisfy the user’s security demands. The advantages of the proposed approach for Cloud Computing include achieving higher scalability, overcoming network latency, reducing network load and consequently lower operational cost, executing asynchronously and autonomously, adopting dynamically, operating in heterogeneous environments of Clouds, and having robust and fault-tolerant behavior.The reminder of this paper is organized as follows. The section II discusses some related works in the area of Mobile Agent-based IDSs. The next section offers design challenges in Cloud IDS. The section IV forms the core of this paper explaining the IDS design. In the next section, the prototype implementation is described to show how specific features of the Mobile Agents can increase the efficiency of the system and decrease the network load by shipping code to data instead of shipping data to code. Moreover, to illustrate how our implementation can enhance the efficiency and performance of the IDS which are discussed in section VI. We test our approach in section VII. Finally, we give conclusions in section VIII.II.R ELATED W ORKThe first generation of intrusion detection systems followed a simple two component architecture: collection component and analyzer component. While this architecture is effective for small collections of monitored hosts, centralized analysis limits the ability to scale up to handle larger collections. Therefore, subsequent generations of Mobile Agent-based intrusion detection systems, such as Autonomous Agents for Intrusion Detection (AAFID) [9], follow a hierarchical structure. Therefore, if any part of the internal nodes (or even the root node) is disabled, the functioning of that of branch of IDS will be disqualified. In addition, those architectures are not flexible, not completely distributed and are not able to respond to attacks against intrusion detection system itself. The performance of IDS using Mobile Agents is considerably relying on the produced network load by the Agents. This issue, which has been neglected by most of related works, will be the main concerns when we design it for Cloud environment. A comparative study on related works is presented in Table I.III.C LOUD IDS D ESIGN C HALLENGES According to [17], Cloud Computing refers to both the applications delivered as services over the Internet and the hardware and systems software in the datacenters that provide those services. The services themselves have long been referred to as Software as a Service (SaaS). The datacenter hardware and software is what we will call a Cloud. When a Cloud is made available in a pay-as-you-go manner to the public, we call it a Public Cloud; the service being sold is Utility Computing. Current examples of public Utility Computing include Amazon EC2 [17]which is an infrastructure as a service provider (IaaS). We use the term Private Cloud to refer to internal datacenters of a business.A simple Cloud architecture is depicted in Fig. 1. The978-0-7695-3829-7/09 $26.00 © 2009 IEEE1692009 Third International Conference on Advanced Engineering Computing and Applications in Sciences DOI 10.1109/ADVCOMP.2009.34175TABLE I. C OMPARING C HARACTERISTICS OF P REVIOUS R ELATED W ORKSarchitecture consists of one or more physical resources at the bottom which are portioned by a virtualization layer into virtual machine instances, and on top of that would be our IDS agency which will be defined later in section IV.For high availability quality of service, each private Cloud requires negotiating and consequently having an agreement with other public Cloud such as Amazon EC2 [17] to request resources when there is a high demand and not enough resources at the local side. In fact, the differentiator between Cloud architecture and other distributed computing architecture is the ability to scale-up on-demand. This is shown in Fig. 1, where public Cloud user launches new instances on a public Cloud like EC2 in order to scale-up offering services by the private Cloud. This means that for the IDS to be successfully deployed by Clouds they have to benefit from scalability.Data transfer cost [17] is a very important issue in Cloud computing. For example in Amazon Cloud, at $100 to $150 per terabyte transferred, these costs quickly add up, making costs a great concern of the administrator. For the sake of cost-effectiveness, researchers focus on areas to reduce the network bandwidth, and allow users and applications to be accessible from anywhere. Sending the Mobile Agents (MA) from the client to the server for intrusion detection purposes prevents the passage of unnecessary immediate results and information through the network. By doing so, bandwidth consumption is reduced in such client/server architecture [18].Moreover, we try to further reduce network load by making Each MA only responsible for detecting certain types of intrusions and as a result Mobile Agents carry less data and code. Next, we try to design all IDS components in a way to impose the least CPU load on the VM instances. That’s because for Cloud Administrators CPU load simply means money, and they are not eager to add more instances for heavy Intrusion Detection Systems.IV.IDS D ESIGNThe aim is to build up a robust distributed hybrid model for intrusion detection in Cloud which covers the flaws of the other traditional models while uses their useful features. Besides, as this area of Mobile Agent-based IDS in Cloud is very new, we will add novel features to previous works to be more customized for Clouds. We will carefully watch the related works to find out which of them help us to meet the mentioned challenges. Among the mentioned challenge, demand for scalability is the most crucial one, because it makes the IDS capable of protecting our VMs outside of our organization boundary. The proposed hybrid model design is inspired by two models namely peer to peer IDS based on Mobile Agents [5] and distributed intrusion detection using Mobile Agents (DIDMA) [8] which as described in Table I offer higher scalability. The following subsections, show how DIDMA is enhanced by adding new components (such data mining, Heart Beat, etc) and applied to each subnet of network while the peer to peer model is used to connect all subnets together.A. Components of the IDS in a SubnetThe fundamental design of our proposed hybrid model in each subnet of Virtual Machines consists of four main components namely IDS Control Center (IDS CC), Agency, Application Specific Static Agent Detectors, and Specialized Investigative Mobile Agent.As can be seen from Fig. 2, Static Agents (SA) should generate an alert whenever they detect suspicious activities, then save those activities information in a log file and send alert’s ID (like A1 in the Fig. 2) to IDS control Center. Then, IDS Control Center will send investigative task-specific Mobile Agent to every agency that sent similar alerts (which are VM 1 and VM 2 in our example).As shown in the Fig. 2, MA will visit and investigate all those VMs, collect information, correlate it and finally send or carry back the result to IDS control Center. Consequently, Alerting Console in IDS Control Center will analyze the coming information and compare and match with intrusion patterns [19] in IDS CC database.Then, it will raise the alarm if it detects an intrusion. IDS Control Center saves the information received from investigative MA into its database. Names and identifications of possibly discovered compromised VM will be black listed and sent to all VMs except the black listed VMs. When the Administrator finds out a new VM in the black list, the necessary actions should be taken. Those actions are quite different compared to actions against compromised physical machines. That’s because virtualmachines are dynamic and can be readily cloned andFigure 1. Simple Cloud architectureseamlessly moved between physical servers. That’s why vulnerabilities can be unknowingly propagated. Thus, Virtual machines which labeled as compromised are recommended to be banned from migration as migration of compromised VMs may lead to propagation of intrusion.As depicted in Fig. 2, every IDS Agency should transmit an "I'm alive" heart beat (shown as HB in Fig. 2) message to the IDSCC at regular intervals to indicate their status. In cases when these messages are not received, there is possibility of intrusion.1)IDS Agency: Mobile Agents need an environment to become alive which is called Agency. An agency is responsible for hosting and executing Agents in parallel and provides them with environment so that they can access services, communicate with each other, and migrate to other agencies. An agency also controls the execution of Agents and protects the underlying VMs from unauthorized access by malicious Agents. In addition, since virtualization creates a level of isolation, the physical machine resources can be protected by executing Agents on VE. The problem of protecting hosts from malicious Mobile Agents has been in place for a long time. However, as proved in [18], the problem could be tackled by virtualization technology.2)Application Specific Static Agent Detectors: Static Agent Detectors (SAD) act like VM monitors, generating ID events whenever traces of an attack is detected, and these events are sent in the form of structured messages to IDS Control Center [8]. SAD is capable of monitoring the VM for different classes of attacks. The SAD is responsible for parsing the log files, checking for intrusion related data pattern in log files, separating data related to the attack from the rest of the data, and formatting the data as required by the investigative MA. The architecture of our IDS allows applying components of other projects as an intrusion detection sensor.Figure 2. IDS architecture in a subnetIn that case, Static Agent Detectors will work on top of those sensors. For instance SNORT [6] network intrusion detection system and its sensors can be used to do packet filtering and looking for intrusion signatures in the packets. To impose the least CPU load on the VM a SAD should be application specific which means if for example a VM is hosting a web server only, it is not logical to have SAD monitoring all possible log files in system.3)Specialized Investigative Mobile Agent: Investigative Mobile Agents (IMA) are responsible for collecting evidences of an attack from all the attacked VM for further analysis and auditing. Then, they have to correlate and aggregate that data to detect distributed attacks. Each IMA is only responsible for detecting certain types of intrusions. This makes it easier for updating when new types of intrusion are found or new types of detection method are invented. In addition, Mobile Agents carry less data and code which save bandwidth, and consequently that decrease operational cost. The investigative MA uses List of Compromised Agency (LCA) to identify its itinerary for visiting Hosts.4)IDS Control Center:An Intrusion Detection System Control Center is (IDSCC) a central point of IDS components administration in each subnet. It includes all the components that a normal VM does and also following components:a)Databases: there should be a database of all intrusion patterns which can be used by Alerting Console to raise the alarm if patterns matched with the detected suspicious activities. All events IDs which reported by SAD are stored in another database. In addition, IDS Control Center should keep an updated status of VMs. A VM in our system can have three statuses as: normal, compromised, migrated.b)Alerting Console: this component compares the spotted suspicious activity with intrusions’ database and raises the alarm if they are matched.c)Agent generator: generate task specific Agent for detecting intrusions (SAD and IMA) even new ones by using knowledge that is generated by data mining inference engine or obtained from previous experiences.d)Mobile Agent dispatcher: it dispatches investigative Mobile Agents to the VMs based on the ID of event or suspicious activity received from their SADs. In addition, it determines list of compromised Agencies (LCA) for IMAs.e)Data mining inference engine: uses machine learning to deduce knowledge to detect new intrusions from System databases which contains detected intrusion and system logs and coming information from SADs. In this component we used Java Agents for Meta-Learning (JAM) project [20] at Columbia University, NY, which applies meta-learning to distributed data mining.f)Trust level manager: defines trust level for all IDS Agencies in the subnet, furthermore it keeps the trust level of the other IDS Control Centers in the same neighbourhood of networks. There are three trust level: 1-noraml 2- suspicious 3- critical. Trust level changes based on SA and MA investigation results.Trust level of all IDS Agencies in the subnet can be modified by the Trust level manager. For example, as mentioned earlier in this paper in the case that the heart beat messages are not received by IDSCC from an IDS Agency, trust manager will decrease the trust level of the Agency. When trust level of the Agency reached specific threshold, it will be identified as a compromised IDS Agency.As pointed out, all VMs which produced same suspicious activity ID will be included in the same List of Compromised Agencies (LCA). However, when a VM gets breached there is high probability that its neighbours are in risk too and consequently they have to be investigated as well. Our approach for defining the LCA is by using a simple version of the Graph based Intrusion Detection system (GrIDS) [7]. The GrIDS generates different shapes of graphs for a period of time that is an indication of large scale distributed attack. The nodes and the links of the graph represent the suspicious VMS and the connection between the VMs respectively. The further propagation of the attack to other VMs leads the way to the growth of the graph. This graph representation is then summarized to produce results that are compared with threshold values for an indication of an attack. Summarization means GrIDS uses approach of aggregating graphs, it infers and reduces the data that must be analyzed at the higher levels. For knowledge sharing between IDS Control Centers, robustness (omitting single point of failure), and detecting intrusions in IDS CCs itself neighbourhood watching scenario is presented in next section.B.Neighborhood watching scenario for detectingintrusion in IDS CCNeighbourhood watching [5] approach is inspired by the real world where neighbours cooperate with each others to achieve more secure neighbourhood. In this approach, all neighbours have the task to watch out for each others. As a result, whenever suspicious behaviours are spotted by a neighbour, all interested parties such as police will be informed.In order to apply that strategy to our IDS application, the first step is to build a virtual neighbourhood where all IDS Control Centers are peers in the same neighbourhood. When any new IDS Control Center enters into the system, it has to be assigned a virtual neighbourhood. The configuration of this neighbourhood system is not fixed and can be dynamic. The initial configuration encompasses a graph of nodes and their location in the network defines the neighbourhood. In order to get the efficient performance the number of neighbours in each neighbourhood should not exceed a predefined upper bound. This matter will be discussed later in section VII.In this neighbourhood watch approach all IDS CC are considered to be equal. Every IDS CC will perform intrusion detection for other IDS CC in its neighbourhood. In a neighbourhood, each Control Center stores data about its neighbours mainly the description of normal behaviour of the neighbours and information such as checksums of critical operating system files. For example, if VM A detects intrusion in neighbour B then it will negotiate with B’s neighbours and only if consensus is obtained then VM B will be identified as a Compromised neighbour.C.The IDS Design DifferentiatorsAs shown, attacks on IDS CC are detectable using the peer to peer model. Consequently, we successfully accomplished the ring of protection in our network. Furthermore, it tackles the single point of failure problem in the AAFID model because there are more than one IDS CC in a network. In addition, compare to AFFID approach, network load is distributed more symmetrical among the network. Moreover, using data mining and knowledge acquiring techniques our model is even capable of achieving new knowledge to detect new kind of intrusion. Outstanding scalability is another strong point of our design. When for example our VM migrates to a machine out of organization boundary (for example from our private Cloud to a public Cloud like Amazon EC2), it is still possible to perform intrusion detection as our IMA can migrate just like VMs. and this is a unique strength of our design which gives the IDS great scalability and flexibility. Therefore, we have met almost all the mentioned challenges in our design.V.P ROTOTYPE I MPLEMENTATIONThe prototype is illustrating how specific features of the Mobile Agents can increase the efficiency of the system and decrease the network load as well. TRACY Mobile Agent toolkit [1] has been used for implementation. Tracy benefited from layered architecture. It means that all basic functions for starting and managing Agent life-cycle are included in a micro kernel and all the others are added as services. Such an open architecture has a great advantage for Clouds as the Mobile Agent toolkit has a very small imperative core which can be customized to impose less load on file and processing systems.Two kinds of Agents defined in Tracy, one is static and the other is Mobile. Agents can be any java class but they should implement ng.Runnable interface. Our design is flexible enough to allow almost all kind of IDS from an integrity checker (For example, “Microsoft (R) File Checksum Integrity Verifier” can help us to verify checksums) to a network intrusion detection system (SNORT) to be deployed by SAD.According to our IDS design, static Agents are responsible for generating alerts, while Mobile Agents are doing investigation on the remote VMs. Nevertheless, a major capability of Mobile Agent is certainly mobility which means they should be able to migrate. Therefore, using migration service in Tracy will be discussed next. It will be shown how our implementation can enhance the efficiency and performance of the IDS using MA by applying Tracy’s novel migration strategies.VI.C OST S AVING U SING D YNAMIC M IGRATIONS TRATEGYOne of the most common migration strategies is the one which transmits all code of the Agent to the next destination. It is fast, however the code which is not going to be used in the next destination will be transferred as well which leads to unnecessary network load. The strategy was named as push strategy in Tracy. The second approach doesn’t transmit any code with Agent’s data transmission. After Mobile Agent received at destination Agency, it might invoke a method. Then, agency downloads all necessary class for that invocation. The strategy is called Pull strategy in Tracy. We discuss that Mobile Agents should decide dynamically, which classes to be carried to destination Agency in migration in order to decrease the network load.In cyber world Agents are autonomous and intelligent therefore Mobile Agents should decide dynamically, which classes to be carried to destination IDS Agency in migration process. Tracy offers pre-fetching technique and dynamic migration strategy to add the ability of this dynamic decision. Pre-fetching technique gives programmers ability to start the process of code downloading manually in migration when there will be an obvious demand for the class in future. This feature can enhance the IDS cost-efficiency significantly in Cloud environment by saving the bandwidth.VII.T ESTINGIn this section our IDS performance will be challenged while we are comparing it with the performance of IDSs which use client/serve approach. For testing purpose, the mathematical model used which was created and applied by peter Braun [1] in 2004. Our aim is to verify our IDS functionality and effectiveness. The IDS with Mobile Agent approach claims the less network load compare to theclient/server approach, by shipping code to data instead of shipping data to code.Fig. 3 compares the network loads of doing intrusion by client server case and Mobile Agent approach for varying number of VMs which should be visited. As a result the IDS with Mobile Agents will produce lower network load when the number of VMs to visit is less than 6 Host. It means that IDS Control Center should not add more than six VMS to the itinerary of the Mobile Agents to make the Mobile Agent’s task efficient and optimized. However, the issue of migration strategies has not been considered in the equation. As we discussed in previous section, if the system can be intelligent enough, it is not always necessary to transfer all the code to the next destination for investigation. Accordingly, less code and data will be relocated. And therefore even less network load will be produced by our approach.VIII. C ONCLUSIONMobile Agents and Virtualization -that Cloud provides- benefit from each others reciprocally. VM are idealplatforms for Agents to execute safely, based on the fact thatvirtual machine can be used to provide secure, isolated sand boxes for the Mobile Agents [18]. And with our approach Clouds and Virtualization can benefit from IDS approach which Mobile Agents make it scalable, flexible and cost-effective. Nonetheless, weaknesses are unavoidable in a new design, and many areas discussed in this paper would benefit from further efforts. For examples, works can look intoMobile Agent’s intercommunication and negotiation which can help investigative Mobile Agents to share their knowledge and therefore build up a more robust inter Cloud IDS.R EFERENCES[1] Peter Braun, Wilhelm R. Rossak, Mobile Agents: Basic Concepts,Mobility Models, and the Tracy Toolkit, published by Morgan Kaufmann (December 22, 2004), ISBN-10: 1558608176 .[2] Andreas Fuchsberger, “Intrusion Detection Systems and IntrusionPrevention Systems, “; Information Security Group, Royal Holloway,Figure 3. Evaluation of Mobile Agent versus client server [1]University of London, Egham, Surrey TW20 0EX, United Kingdom 2005.[3] J. P. Anderson. Computer Security Threat Monitoring and Surveillance. Technical Report, James P. Anderson Co., April 1980. [4]Richard A. Kemmerer and Giovanni Vigna, Intrusion detection: a brief history and overview Reliable Software Group, Computer Science Department, University of California Santa Barbara 2003. [5] Geetha Ramachandran and Delbert Hart, A P2P Intrusion Detection System based on Mobile Agents, 2004 ACM 1-58113-870-9/04/04. [6] Snort, (Oct 2005). Online. /,(March 2007).[7]S.Stainford-Chen, Steven Cheung, et.al. GrIDS-Graph Based Intrusion Detection System for Large Networks. In the Proceedings of the 19th National Information Systems Security Conference, Baltimore, MD, October 1996.[8]Pradeep Kannadiga and Mohammad Zulkernine School of Computing Queen’s University, Kingston Ontario, Canada K7L 3N, DIDMA: A Distributed Intrusion Detection System Using Mobile Agents, 2005 IEEE.[9]J.Balasubramainyan, J.O. Garcia-Fernandez, D.Isacoff, E.H. Spafford, D.Zamboni, An architecture of intrusion detection using autonomous Agents, Department of Computer Science, Purdue University coast TR 98-05, 1998.[10] Mladen Vouk, Sam Averitt, Michael Bugaev, Andy Kurth, Aaron Peeler, Henry Shaffer, Eric Sills, Sarah Stein, Josh Thompson,Powered by VCL - Using Virtual Computing Laboratory (VCL), Proc. 2nd International Conference on Virtual Computing (ICVCI), 15-16 May, 2008, RTP, NC, pp 1-10 Date Published: May 16, 2008.[11] Wayne Jansen, Peter Mell, Tom Karygiannis, Don Marks, Applying Mobile Agents to Intrusion Detection and Response, NIST Interim Report (IR) – 6416 October 1999.[12]Michael Conner, Chirag Patel, Mike Little, “Genetic Algorithm/Artificial Life Evolution of Security Vulnerability Agents,” Army Research Laboratory Federal Laboratory 3rd Annual Symposium on Advanced Telecommunications & Information Distribution Research Program (ATIRP), February 1999.[13]Barrett, Michael, W. Booth, M. Conner, D. Dumas, M. Gaughan, S, Jacobs, M. Little, “Intelligent Agents System Requirements and Architecture,” Report to ATIRP, p. 5, October 1998.[14] M.Asaka, S.Okazawa, A.Taguchi, and S.Goto, "A Method of Tracing Intruders by Use of Mobile Agents," INET'99, June 1999.[15]P. C. Chan and Victor K. Wei, Preemptive Distributed Intrusion Detection using Mobile Agents, Department of Information Engineering The Chinese University of Hong Kong Shatin, N.T., Hong Kong, Proceedings of the Eleventh IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE’02) 1080-1383/02 $17.00 © 2002 IEEE.[16] G. Helmer et al., Lightweight agents for intrusion detection, The Journal of Systems and Software 67 (2003) 109–122.[17]M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. H. Katz, A. Konwinski, G. Lee, D. A. Patterson, A. Rabkin, I. Stoica, and M. Zaharia.Above the Clouds: A Berkeley view of Cloud computing. Technical report UCB/EECS-2009-28, Electrical Eng. and Computer Sciences, University of California at Berkeley, Berkeley, USA, 2009. [18]U. Topaloglu and C. Bayrak, Secure mobile agent execution in virtual environment, Springer Netherlands, Journal of Autonomous Agents and Multi-Agent Systems Volume 16, Number 1 / February, 2008.[19]Wuu, L., Hung, C., and Chen, S. 2007. Building intrusion pattern miner for Snort network intrusion detection system. J. Syst. Softw. 80,10 (Oct. 2007), 1699-1715. DOI = /10.1016/j.jss.2006.12.546.[20]W. Lee, S.J. Stolfo, and K. Mok, “A Data Mining Framework for Building Intrusion Detection Models,” Proceedings of the IEEE Symposium on Security and Privacy, 1999. <URL: /~sal/JAM/PROJECT/>。

计算机软件的高级技术与实际操作方法第一章：软件开发生命周期软件开发生命周期是指软件开发过程中，从项目启动到软件交付使用的整个过程，包括需求分析、设计、编码、测试和维护等阶段。

在实际操作中，我们需要遵循软件开发生命周期的原则，以保证软件的质量和可靠性。

在需求分析阶段，我们需要与用户充分沟通，明确软件的功能和性能要求。

在设计阶段，我们需要根据需求分析结果进行软件架构设计和数据库设计。

在编码阶段，我们需要按照设计文档进行编码，并采用合适的编程语言和工具。

在测试阶段，我们需要进行功能测试、性能测试和安全测试，以确保软件符合用户需求和性能要求。

在维护阶段，我们需要及时修复软件中的bug，并对软件进行功能扩展和性能优化。

第二章：面向对象编程技术面向对象编程技术是一种基于对象的软件开发方法，它将现实世界中的事物抽象为对象，对象之间通过消息传递进行通信。

在面向对象编程中，我们可以通过封装、继承和多态等机制，实现代码的重用和灵活性。

在实际操作中，我们需要使用面向对象的编程语言，如Java或C++，并遵循面向对象编程的原则和设计模式。

在设计阶段，我们需要根据需求分析结果，进行对象的抽象和类的设计。

在编码阶段，我们需要按照类的设计进行实现，并进行单元测试和集成测试。

在维护阶段，我们可以通过继承和多态的方式，对现有的类进行扩展和修改，以适应新需求的变化。

第三章：软件体系结构设计软件体系结构是指软件系统中各个组件之间的关系和交互方式。

在实际操作中，我们需要进行软件体系结构的设计，以满足软件的可扩展性、可维护性和可重用性等要求。

常见的软件体系结构模式包括分层结构、客户端-服务器结构和发布-订阅结构等。

在设计阶段，我们需要根据需求分析结果进行系统的分析和设计，确定系统的各个组件及其之间的接口和关系。

在编码阶段，我们需要按照体系结构设计进行实现，并进行模块测试和系统测试。

在维护阶段，我们可以通过修改和重构软件的体系结构，以适应新需求的变化和改进软件的性能和可靠性。

探索计算机软件使用技巧的秘密方法第一章：了解软件基本操作在探索计算机软件使用技巧的秘密方法之前，我们首先需要了解软件的基本操作。

无论是操作系统、办公软件还是设计软件，掌握基本的操作方式是使用软件的关键。

这一章将向读者介绍常见软件的基本操作，包括界面布局、菜单项的作用等。

第二章：学会使用快捷键学习使用快捷键是提高计算机软件操作效率的秘密武器。

大多数软件都提供了一系列快捷键，通过快速按键操作可以实现常用功能。

这一章将介绍常见软件的常用快捷键，并教读者如何通过自定义快捷键来适应个人使用习惯。

第三章：善用搜索引擎搜索引擎是解决软件使用问题的最佳利器。

遇到不懂或不熟悉的操作，通过搜索引擎可以迅速找到答案。

除了搜索技巧，本章还介绍如何选择权威的技术论坛和社区，向其他用户请教问题并获取帮助。

第四章：利用插件和扩展功能增强软件能力现代计算机软件通常提供了插件和扩展功能，可以在默认功能的基础上进一步增强软件能力。

本章将介绍常见软件的插件和扩展功能，并提供使用方法和推荐列表，帮助读者挖掘软件更多的潜力。

第五章：定期更新软件版本软件开发商不断改进和优化软件，发布新版本以修复漏洞和提供新功能。

定期更新软件版本是使用技巧的秘密方法之一。

本章将向读者介绍软件版本更新的重要性，并提供更新软件的常见方法。

第六章：掌握调试技巧在软件使用过程中，难免会出现各种问题和错误。

掌握一些调试技巧可以帮助读者快速解决问题。

本章将介绍常见的调试技巧，包括错误代码的理解、日志文件的分析等，帮助读者更好地应对软件使用中的问题。

第七章：网络资源的利用互联网是获取软件使用技巧的宝库。

本章将介绍一些优质的网络资源，包括在线教程、视频教程、技术博客等，帮助读者通过网络获取更多关于软件使用技巧的知识。

第八章：利用社交网络获取帮助社交网络不仅是日常交流的平台，也是获取软件使用技巧帮助的渠道。

本章将介绍如何利用社交网络与其他用户交流、请教问题，并借助群组或论坛获取及时的答案和帮助。