本文選題：關(guān)鍵基礎(chǔ)設(shè)施 + 安全防范系統(tǒng)��； 參考：《西南大學(xué)》2017年碩士論文

【摘要】：關(guān)鍵基礎(chǔ)設(shè)施作為一個(gè)國(guó)家的根本。關(guān)鍵基礎(chǔ)設(shè)施是指具有任何在國(guó)家安全與運(yùn)轉(zhuǎn)體系中扮演重要角色的系統(tǒng)或者設(shè)施。它們不僅包括電力以及水處理工廠等設(shè)施,而且還涵蓋了比如化工、金融服務(wù)、信息技術(shù)等相當(dāng)廣泛的行業(yè)與設(shè)施范疇。近年來(lái),由于國(guó)際社會(huì)的持續(xù)動(dòng)蕩,特別是像恐怖主義這樣的人為因素,對(duì)關(guān)鍵基礎(chǔ)設(shè)施的安防系統(tǒng)構(gòu)成了巨大的威脅。這迫使我們需要進(jìn)一步重視相關(guān)安全防范系統(tǒng)的維護(hù)與建設(shè)。面對(duì)一個(gè)大型、復(fù)雜的安全防范系統(tǒng),我們?nèi)绾卧u(píng)估它的有效性;如何判斷系統(tǒng)中是否存在漏洞、哪些地方更加關(guān)鍵等等。這都需要我們提出一種合理、客觀的方法對(duì)一個(gè)安全防范系統(tǒng)進(jìn)行建模,分析它的有效性、脆弱性以及所面臨的風(fēng)險(xiǎn)。針對(duì)以上問(wèn)題,本文在失效模式及其影響分析模型的基礎(chǔ)上,構(gòu)建了一個(gè)評(píng)估安全防范系統(tǒng)風(fēng)險(xiǎn)的評(píng)估框架。本文的的主要流程是首先利用改進(jìn)的多目標(biāo)敵對(duì)者序列圖將一個(gè)關(guān)鍵基礎(chǔ)設(shè)施的平面圖轉(zhuǎn)化為一個(gè)安防網(wǎng)絡(luò)。再利用風(fēng)險(xiǎn)熵方法計(jì)算網(wǎng)絡(luò)中每個(gè)節(jié)點(diǎn)的防范效能,然后將節(jié)點(diǎn)上的安防效能轉(zhuǎn)化為安防網(wǎng)絡(luò)中邊的權(quán)重。接著用改進(jìn)的變形蟲(chóng)算法計(jì)算出到達(dá)每個(gè)目標(biāo)的最短路徑,這些最短路徑就是到達(dá)一個(gè)目標(biāo)的所有路徑中防范效能最弱的路徑。最后我們利用失效模式及其影響分析模型來(lái)計(jì)算每條路徑的風(fēng)險(xiǎn)值,最終確定系統(tǒng)中面臨風(fēng)險(xiǎn)值最高的目標(biāo)和路徑。在這個(gè)評(píng)估流程中,本文的主要貢獻(xiàn)有三點(diǎn):(1)在評(píng)估安防節(jié)點(diǎn)的防護(hù)效能部分,不同于多數(shù)文獻(xiàn)使用AHP方法來(lái)確定每個(gè)指標(biāo)的權(quán)重,本文提出了有序可視圖加權(quán)平均算子來(lái)得到每個(gè)指標(biāo)的權(quán)重。該方法克服了AHP方法需要專家憑經(jīng)驗(yàn)主觀輸入權(quán)重的缺點(diǎn),并且解決了當(dāng)專家對(duì)指標(biāo)間的相對(duì)權(quán)重一無(wú)所知,無(wú)法使用AHP方法得到權(quán)重的情況。相對(duì)于經(jīng)典的有序加權(quán)平均算子僅考慮輸入數(shù)據(jù)序的信息,本方法還考慮了數(shù)據(jù)值本身的信息,這使得本方法更加合理。相對(duì)于AHP方法需要專家輸入每個(gè)指標(biāo)的相對(duì)權(quán)重,本方法是數(shù)據(jù)驅(qū)動(dòng)、無(wú)參輸入的,加權(quán)方式更加客觀。此外,文中還用了一個(gè)應(yīng)用和一個(gè)例子來(lái)說(shuō)明了本方法的有效性。(2)在將設(shè)施平面圖轉(zhuǎn)化為安防網(wǎng)絡(luò)部分,本文改進(jìn)了原始的僅針對(duì)單個(gè)目標(biāo)的敵對(duì)者序列圖。在現(xiàn)實(shí)情況中,一個(gè)設(shè)施內(nèi)往往有多個(gè)目標(biāo)需要被保護(hù)。并且,當(dāng)系統(tǒng)探測(cè)到一個(gè)入侵者時(shí),有時(shí)候我們并不知道他的目標(biāo)具體是哪一個(gè)。因此,我們有必要將只針對(duì)一個(gè)目標(biāo)分析的敵對(duì)者序列圖拓展到多個(gè)目標(biāo)。同時(shí),在生成安防網(wǎng)絡(luò)邊上的權(quán)重的時(shí)候,不同于原始的敵對(duì)者序列圖將兩個(gè)區(qū)域間的距離設(shè)為默認(rèn)值,改進(jìn)的多目標(biāo)序列圖引入了條件因素。這使管理人員可以加入像路徑隱蔽性等這類其他因素,使模型的建立更加靈活。此外,因?yàn)橄到y(tǒng)中有多個(gè)目標(biāo),所以我們改進(jìn)了原始的變形蟲(chóng)算法,使之能解決單源多匯的問(wèn)題,避免了多次運(yùn)行最短路徑算法求解每個(gè)目標(biāo)最薄弱路徑的繁瑣和低效率。(3)在計(jì)算每條路徑面臨的風(fēng)險(xiǎn)值部分,本文提出了一種將專家的定性評(píng)估結(jié)果轉(zhuǎn)化為定量結(jié)果的方法。該方法的主要步驟是首先用三角模糊數(shù)將此類定性的語(yǔ)言變量量化。再用三角模糊數(shù)的距離公式,將多個(gè)專家的評(píng)估結(jié)果聚合,得到每個(gè)目標(biāo)以三角模糊數(shù)形式表示的綜合評(píng)價(jià)值。最后利用逼近最優(yōu)解的偏好順序法得到每個(gè)模式失效的后果嚴(yán)重程度的排序值,最終得到了一個(gè)清晰的值。該方法的主要優(yōu)點(diǎn)是比已有的方法更加簡(jiǎn)單。并且,本文還用了一個(gè)例子來(lái)說(shuō)明了本方法的有效性。
[Abstract]:Key infrastructure is the root of a country. Key infrastructure refers to any system or facilities that play an important role in national security and operation systems. They include not only electricity and water treatment plants, but also a wide range of industries and facilities such as chemical, financial services, and information technology. In recent years, the continuing turbulence in the international community, especially the human factors such as terrorism, poses a great threat to the security system of key infrastructure. This has forced us to pay more attention to the maintenance and construction of the related security system. How to estimate its effectiveness, how to judge whether there are loopholes in the system, which is more critical, and so on. It all requires us to put forward a reasonable and objective method to model a security system and analyze its effectiveness, vulnerability and risk. In this paper, the failure mode and its impact analysis model are presented in this paper. The main flow of this paper is to transform the plane map of a key infrastructure into a security network first, and then use the risk entropy method to calculate the prevention efficiency of each node in the network. The security effectiveness of the point is transformed into the weight of the side in the security network. Then the shortest path to each target is calculated with the improved amoeba algorithm. These shortest paths are the weakest paths in all paths to a target. Finally, we use the failure mode and its impact analysis model to calculate each path. In this evaluation process, the main contribution of this paper is three points: (1) in evaluating the protection effectiveness part of the security node, different from the majority of the documents using the AHP method to determine the weight of each index, this paper proposes an ordered view weighted mean operator. To the weight of each index, the method overcomes the disadvantage that the AHP method requires the expert to enter the weight with the experience subjective input weight, and solves the situation that the expert can not use the AHP method to get the weight when the expert knows nothing about the relative weight between the indexes. Considering the information of the data value itself, this makes the method more reasonable. Relative to the AHP method, the relative weight of each index is required by experts. This method is data driven, no input, and the weighting method is more objective. In addition, an application and an example are used to illustrate the effectiveness of the method. (2) in the facility plan As part of the security network section, this article improves the original sequence diagram of an opponent for a single target. In reality, there are often multiple targets in a facility that need to be protected. And, when the system detects an intruder, sometimes we do not know what the target is. The sequence diagram of a target analysis is extended to multiple targets. At the same time, when the weight of the security network side is generated, the distance between the two regions is set as the default, and the improved multi target sequence diagram introduces the condition factors. This makes the manager join the path concealment. Other factors make the model more flexible. In addition, because there are multiple targets in the system, we have improved the original amoeba algorithm to solve the problem of single source and multiple sinks and avoid the complexity and low efficiency of the shortest path algorithm to solve the weakest path of each target. (3) calculating the wind faced by each path. The main step of this method is to quantify the qualitative language variables by trigonometric fuzzy numbers, and then use the distance formula of triangular fuzzy numbers to aggregate the evaluation results of several experts, and get each target in the form of triangular fuzzy numbers. Finally, a clear value is obtained by using the preference order method of approximating the optimal solution to the seriousness of the severity of the consequences of each pattern. The main advantage of this method is more simple than the existing method. Furthermore, a case is used to illustrate the effectiveness of the method.
【學(xué)位授予單位】：西南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP301.6;D631.4

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