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2025年下半年信息安全工程師《綜合知識》考試真題及答案解析正在更新中，具體如下：

1、網(wǎng)絡(luò)安全法第二十一條規(guī)定，國家實(shí)行網(wǎng)絡(luò)安全等級保護(hù)制度。網(wǎng)絡(luò)安全等級保護(hù)的主要工作中，等級保護(hù)測評機(jī)構(gòu)依據(jù)相應(yīng)等級要求，對定級的保護(hù)對象進(jìn)行測評，并出具相應(yīng)的等級保護(hù)測評證書的過程被稱為（ ）。

A.備案

B.定級

C.等級測評

D.運(yùn)營維護(hù)

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2、國產(chǎn)密碼算法是由國家密碼相關(guān)研究機(jī)構(gòu)自主研發(fā)，具有相關(guān)知識產(chǎn)權(quán)的商用密碼算法。目前已經(jīng)公布的國產(chǎn)密碼算法主要有：SM1算法、SM2算法、SM3雜湊算法、SM4算法、SM9標(biāo)識密碼算法和ZUC算法等。其中SM1是一種對稱的分組密碼算法，其分組長度和密鑰長度分別是（ ）。

A.256比特和128比特

B.64比特和128比特

C.256比特和256比特

D.128比特和128比特

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3、SSH協(xié)議是基于公鑰的安全應(yīng)用協(xié)議，可以實(shí)現(xiàn)加密、認(rèn)證、完整性檢查等多種安全服務(wù)。在SSH協(xié)議的子協(xié)議中，（ ）提供算法協(xié)商和密鑰交換，并實(shí)現(xiàn)服務(wù)器認(rèn)證，形成一個加密的安全連接，提供完整性、保密性和壓縮選項(xiàng)服務(wù)。

A.SSH傳輸層協(xié)議

B.SSH連接協(xié)議

C.SSH端口轉(zhuǎn)發(fā)技術(shù)

D.SSH用戶認(rèn)證協(xié)議

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4、訪問控制是指對資源對象的訪問者授權(quán)、控制的方法及運(yùn)行機(jī)制。以下屬于控制的是（ ）。

A.接收郵件

B.讀寫

C.刪除

D.禁止訪問

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5、防火墻和網(wǎng)閘都是保障內(nèi)網(wǎng)安全的網(wǎng)絡(luò)安全技術(shù)。以下關(guān)于防火墻和網(wǎng)閘的說法中，錯誤的是（ ）。

A.防火墻依賴規(guī)則庫防御已知威脅

B.網(wǎng)閘對外提供開放端口

C.網(wǎng)閘可實(shí)現(xiàn)協(xié)議層面的完全隔離

D.防火墻支持雙向通信與會話保持

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6~10、Biometric authentication is a system which is capable to identifying a （01） based on the inherent physical or behavioral traits associated with that person. In recent years the application of biometrics in authentication systems, increase gradually, one of the main reasons for its popularity is that biometric traits like a fingerprint, face and iris feature of a person are （02） during the whole life time and it is not easily guessed, forgotten and misplaced. Biometric based system is more secure and accurate compared to the traditional system which is based on user personal identification number （PIN） and the user set PIN can be easily guessed by the third party. Unimodal system which is based on single modality has several inherent problems like intra-class variation, spoofing attacks and failure-to-enroll rate. To overcome this limitation multibiometric is a good option where we can use （03） one modality at a time to improve the performance and accuracy level of the systems. A fusion process plays an important role in multibiometric where the features of more than one modality are combined together. The whole fusion process can be classified as sensor level, feature level （combining features from different biometrics）, score level （combining the genuine and imposter score）, decision level （combining the decisions） and rank level （combining the ranks）. Among all the fusion methods score level fusion is very popular and simple; lots of research has been done in the area of score level fusion. Researchers prefer to use score level fusion due to ease of combining matching score. Further score level fusion technique can be categorized as a） transformation-based score fusion like sum rule, weighted sum rule and the product rule. b） Classifier based like support vector machine （SVM） and c） density based fusion like likelihood ratio test. It is very （04） to deal with the feature level fusion because at this level the feature sets generated from multiple modalities are different in nature as in the case of fusion of fingerprint and face. Researchers preferred （05） use score level fusion due to ease of combining matching score. Due to lack of information it is very difficult to select a decision level, as a fusion strategy in a multibiometric system. In feature level fusion due to the large dimensionality of the feature set, it may affect the performance of the system, so some feature selection technique is used to identify and remove the irrelevant and redundant information and may allow learning algorithms to operate faster and more effectively.

（6）

A.person

B.server

C.device

D.computer

（7）

A.diferernt

B.safe

C.same

D.complete

（8）

A.of

B.more than

C.on

D.as

（9）

A.difficult

B.easy

C.relaxed

D.fast

（10）

A.with

B.to

C.for

D.by

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