本文選題：手機支付 + 安全監(jiān)測�。� 參考：《北京交通大學》2014年碩士論文

【摘要】：摘要：近年來,手機移動支付因其便利性和快捷性具有了廣大的市場,但因其應用層的安全機制和技術還不夠完善,外加手機病毒的出現(xiàn)、移動終端與銀行接口以及系統(tǒng)的開放性等方面存在的漏洞,使得手機支付的安全性成為當前的研究熱點之一。 本論文主要以研究手機支付中惡意行為帶來的網(wǎng)絡流量異常變化的安全威脅問題為切入點,以網(wǎng)絡流量的分析提取為基礎,以信息熵為基本度量方法,建立了基于多層前饋神經(jīng)網(wǎng)絡(Back Propagation Neural Network,簡稱BP神經(jīng)網(wǎng)絡)的手機安全支付異常流量監(jiān)測模型。系統(tǒng)通過對網(wǎng)絡流量變化過程中異常行為的提取和對比,給出了一個完整的從異常發(fā)現(xiàn)到異常分析及判斷異常指標動態(tài)調(diào)整的工作流程。本文所作的工作主要體現(xiàn)在以下幾個方面： 1.通過對手機移動支付過程的分析,討論了其安全威脅的主要來源,并選取了特征參數(shù)對其流量進行監(jiān)測從而達到保證其安全性的目的。其中,通過分析手機支付中能夠充分體現(xiàn)其流量特征的參數(shù),提取出四個特征參數(shù)并計算熵值。 2.詳細設計了基于信息熵和BP神經(jīng)網(wǎng)絡的異常流量監(jiān)測系統(tǒng)的結構。利用BP神經(jīng)網(wǎng)絡最突出的再學習機制,將初步監(jiān)測結果再次送入神經(jīng)網(wǎng)絡內(nèi)部,達到動態(tài)分析和調(diào)整的目的。 3.提出了動態(tài)調(diào)整網(wǎng)絡流量閾值區(qū)間的算法,通過該算法可以實時更新流量監(jiān)測指標,做到適應當前網(wǎng)絡狀況的要求。此外,考慮到監(jiān)測任務中監(jiān)測結果的效率問題,設計該算法時只選取變化顯著的特征參數(shù)參與調(diào)整,從而減少了網(wǎng)內(nèi)數(shù)據(jù)的存儲量和計算量。 本論文最后利用matlab實現(xiàn)了本文提出的異常流量監(jiān)測模型,并對系統(tǒng)進行了測試和分析。結果表明：該模型較為成熟有效,能夠起到一定的抵制網(wǎng)絡惡意攻擊的作用,適用于手機安全支付系統(tǒng)異常流量監(jiān)測。
[Abstract]:Absrtact: in recent years, mobile payment of mobile phone has a broad market because of its convenience and quickness, but the security mechanism and technology of its application layer are not perfect enough, and the emergence of mobile phone virus. The security of mobile payment has become one of the current research hotspots due to the vulnerabilities in the interface between mobile terminal and bank and the openness of the system. In this paper, we focus on the research of the security threat caused by malicious behavior in mobile phone payment, based on the analysis and extraction of network traffic, and based on the information entropy as the basic measurement method. A mobile phone security payment abnormal traffic monitoring model based on the multilayer feedforward neural network back Propagation Neural network (BP neural network) is established. Through the extraction and comparison of abnormal behavior in the process of network traffic change, a complete workflow from anomaly detection to anomaly analysis and dynamic adjustment of abnormal index is presented. The work done in this paper is mainly reflected in the following aspects: 1. By analyzing the mobile payment process of mobile phone, the main sources of security threat are discussed, and the characteristic parameters are selected to monitor the traffic to ensure the security of mobile phone. Among them, by analyzing the parameters of mobile phone payment which can fully reflect the characteristics of its flow, four feature parameters are extracted and entropy is calculated. 2. The structure of abnormal flow monitoring system based on information entropy and BP neural network is designed in detail. Using the most outstanding relearning mechanism of BP neural network, the preliminary monitoring results are sent into the neural network again to achieve the purpose of dynamic analysis and adjustment. 3. An algorithm for dynamically adjusting the threshold interval of network traffic is proposed, through which the traffic monitoring index can be updated in real time to meet the requirements of current network conditions. In addition, considering the efficiency of the monitoring results in the monitoring task, only the characteristic parameters with significant changes are selected in the design of the algorithm, which reduces the storage and computation of the data in the network. In the end of this paper, the matlab is used to realize the model of abnormal flow monitoring, and the system is tested and analyzed. The results show that the model is more mature and effective and can resist network malicious attacks to some extent. It is suitable for mobile phone security payment system to monitor abnormal traffic.
【學位授予單位】：北京交通大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN929.53

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本文編號：1801665