本文選題：進(jìn)化算法　切入點(diǎn)：基于角色的控制訪問　出處：《北京交通大學(xué)》2014年碩士論文

【摘要】：基于角色的控制訪問(RBAC, Role Based Access Control)是應(yīng)用廣泛的控制訪問技術(shù),較傳統(tǒng)意義上的訪問控制列表(ACL, Access Control List)具有很多優(yōu)勢,比如便于管理,消耗資源少等。從ACL模型轉(zhuǎn)化到RBAC模型是一個(gè)富有挑戰(zhàn)意義的過程,而解決ACL模型到RBAC模型轉(zhuǎn)換的過程就是角色挖掘。 本文首先分析了研究工作者目前提出的各種角色挖掘算法。其中,基于進(jìn)化算法的角色挖掘產(chǎn)生了很好的效果,該類算法通過啟發(fā)式搜索,尋找最小角色集合。然而這些算法在進(jìn)行角色挖掘的過程中,沒有考慮用戶和權(quán)限本身所帶的屬性,這會(huì)造成實(shí)際應(yīng)用出現(xiàn)問題,比如將醫(yī)生的權(quán)限賦予計(jì)算機(jī)專業(yè)人員。 本文從這一缺陷出發(fā),提出兩個(gè)新的角色挖掘算法,一個(gè)是基于遺傳聚類的刪除策略角色挖掘,首先根據(jù)用戶和權(quán)限的屬性產(chǎn)生候選角色集合,然后通過遺傳算法來對(duì)角色進(jìn)行刪除,生成最終RBAC模型；另一個(gè)是基于螞蟻聚類的添加策略角色挖掘,使用螞蟻算法將符合屬性策略的候選角色添加入RBAC模型。上述兩種算法,在使用進(jìn)化算法挖掘最小角色集合時(shí),充分考慮用戶和權(quán)限的屬性,產(chǎn)生有意義的角色。實(shí)驗(yàn)結(jié)果表明新算法在充分考慮用戶和權(quán)限屬性的前提下,降低了角色產(chǎn)生的規(guī)模。
[Abstract]:RBAC (Role Based Access Control) is a widely used control access technology, which has many advantages over the traditional access control list (ACLs, Access Control list), such as easy to manage, less resource consumption and so on.Transforming from ACL model to RBAC model is a challenging process, and role mining is the process of transforming ACL model to RBAC model.In this paper, the role mining algorithms proposed by researchers are analyzed.Among them, role mining based on evolutionary algorithm has a good effect. This kind of algorithm finds the minimum role set by heuristic search.However, in the process of role mining, these algorithms do not consider the attributes of users and permissions themselves, which will lead to problems in practical applications, such as giving computer professionals the authority of doctors.In this paper, we propose two new role mining algorithms. One is deletion strategy role mining based on genetic clustering. Firstly, candidate role sets are generated according to the attributes of users and permissions.Then the role is deleted by genetic algorithm and the final RBAC model is generated. The other is the role mining of adding strategy based on ant clustering. Ant algorithm is used to add the candidate role according to attribute strategy into the RBAC model.The above two algorithms take full account of the attributes of users and permissions and produce meaningful roles when using evolutionary algorithms to mine the minimum set of roles.Experimental results show that the new algorithm reduces the size of role generation on the premise of fully considering the user and privilege attributes.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.08

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本文編號(hào)：1706623