發(fā)布時(shí)間：2018-04-25 14:15

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

【摘要】：摘要：近年來,手機(jī)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(yīng)用層的安全機(jī)制和技術(shù)還不夠完善,外加手機(jī)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(jī)支付的安全性成為當(dāng)前的研究熱點(diǎn)之一。 本論文主要以研究手機(jī)支付中惡意行為帶來的網(wǎng)絡(luò)流量異常變化的安全威脅問題為切入點(diǎn),以網(wǎng)絡(luò)流量的分析提取為基礎(chǔ),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(luò)(Back Propagation Neural Network,簡稱BP神經(jīng)網(wǎng)絡(luò))的手機(jī)安全支付異常流量監(jiān)測模型。系統(tǒng)通過對網(wǎng)絡(luò)流量變化過程中異常行為的提取和對比,給出了一個(gè)完整的從異常發(fā)現(xiàn)到異常分析及判斷異常指標(biāo)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(gè)方面： 1.通過對手機(jī)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(jìn)行監(jiān)測從而達(dá)到保證其安全性的目的。其中,通過分析手機(jī)支付中能夠充分體現(xiàn)其流量特征的參數(shù),提取出四個(gè)特征參數(shù)并計(jì)算熵值。 2.詳細(xì)設(shè)計(jì)了基于信息熵和BP神經(jīng)網(wǎng)絡(luò)的異常流量監(jiān)測系統(tǒng)的結(jié)構(gòu)。利用BP神經(jīng)網(wǎng)絡(luò)最突出的再學(xué)習(xí)機(jī)制,將初步監(jiān)測結(jié)果再次送入神經(jīng)網(wǎng)絡(luò)內(nèi)部,達(dá)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(luò)流量閾值區(qū)間的算法,通過該算法可以實(shí)時(shí)更新流量監(jiān)測指標(biāo),做到適應(yīng)當(dāng)前網(wǎng)絡(luò)狀況的要求。此外,考慮到監(jiān)測任務(wù)中監(jiān)測結(jié)果的效率問題,設(shè)計(jì)該算法時(shí)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(jì)算量。 本論文最后利用matlab實(shí)現(xiàn)了本文提出的異常流量監(jiān)測模型,并對系統(tǒng)進(jìn)行了測試和分析。結(jié)果表明：該模型較為成熟有效,能夠起到一定的抵制網(wǎng)絡(luò)惡意攻擊的作用,適用于手機(jī)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(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN929.53

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本文編號：1801665