基于貝葉斯智能分類的改進(jìn)垃圾短信攔截平臺構(gòu)建
發(fā)布時間：2018-08-27 12:28
【摘要】：移動通信網(wǎng)絡(luò)的高速發(fā)展，令手機(jī)短信成為人與人實(shí)時交流信息的重要方式之一，依附手機(jī)短信而大量傳播的垃圾信息更成為了用戶長期的困擾，雖然垃圾短信攔截技術(shù)的應(yīng)用初有成效，但是只針對單一關(guān)鍵字識別垃圾短信以及對重復(fù)出現(xiàn)的大量相同短信內(nèi)容的主叫號碼進(jìn)行過濾。然而不法分子通過廣告類、欺詐類甚至違法類等垃圾短信贏得了巨大利益，經(jīng)過統(tǒng)計發(fā)現(xiàn)，為了躲避這種實(shí)時策略，違規(guī)的垃圾短信生存和傳播形態(tài)也發(fā)生了巨大改變。雖然可以對近期出現(xiàn)的垃圾短信進(jìn)行統(tǒng)計分析，制定相應(yīng)的關(guān)鍵字組合和攔截策略，但既要付出巨大人力成本，也只能暫時攔截符合統(tǒng)計規(guī)則的垃圾短信，這成為了垃圾短信不能根治的重要原因之一。因此，結(jié)合自學(xué)習(xí)智能分類系統(tǒng)和實(shí)時垃圾短信攔截系統(tǒng)，對于長期控制和識別過濾垃圾短信有著重要的意義。 本文在充分的理論準(zhǔn)備工作前提下，對中文分詞、文本分類和關(guān)鍵的統(tǒng)計學(xué)習(xí)方法做了簡要的闡述，確定使用貝葉斯分類方法作為智能分類學(xué)習(xí)的主要算法。對改進(jìn)的垃圾短信攔截系統(tǒng)的前期系統(tǒng)需求分析和相應(yīng)的系統(tǒng)設(shè)計做了詳盡的描述，尤其在系統(tǒng)功能、系統(tǒng)處理流程和模塊設(shè)計中，表現(xiàn)出了貝葉斯智能分類系統(tǒng)和實(shí)時攔截系統(tǒng)的應(yīng)用結(jié)合的可能性和完善性，，并且系統(tǒng)最終生成的系統(tǒng)報表將作為后續(xù)垃圾短信分析處理的依據(jù)。 貝葉斯智能分類系統(tǒng)的測試結(jié)果表明，通過對短信文本內(nèi)容的預(yù)處理，使用貝葉斯分類算法可以有效的對短信進(jìn)行較為準(zhǔn)確的類別分類，和人工分類的測試結(jié)果進(jìn)行比對，智能分類系統(tǒng)在處理大批量的短信文本準(zhǔn)確率上要高于人工分類。 綜上所述，基于貝葉斯分類算法的垃圾短信攔截系統(tǒng)，改進(jìn)了系統(tǒng)自學(xué)習(xí)能力不足的問題，較大程度的降低了對人工操作的依賴性，對于將來長期跟蹤分析垃圾短信變化，以及針對垃圾短信的實(shí)時處理手段都有很大的幫助。改進(jìn)的垃圾短信攔截系統(tǒng)通過實(shí)時攔截和非實(shí)時智能分析攔截的雙重保障，能對短信進(jìn)行有效的攔截過濾操作，保證了垃圾短信攔截系統(tǒng)上線后穩(wěn)定運(yùn)行。
[Abstract]:With the rapid development of mobile communication network, SMS has become one of the most important ways for people to exchange information in real time. Although the application of spam short message interception technology has been successful, it is only used to identify spam messages by single keyword and filter the calling numbers of a large number of repeated messages with the same message content. However, through advertising, fraud and even illegal spam SMS has won huge benefits. Through statistics, it is found that in order to avoid this real-time strategy, the existence and dissemination of illegal spam SMS has changed greatly. Although it is possible to make statistical analysis of recent spam messages and formulate corresponding keyword combinations and interception strategies, they have to pay a huge human cost and only temporarily intercept spam messages that conform to the statistical rules. This has become one of the important reasons why spam messages can not be cured. Therefore, the combination of self-learning intelligent classification system and real-time spam message interception system is of great significance for long-term control and identification of filtered spam messages. On the premise of sufficient theoretical preparation, this paper gives a brief description of Chinese word segmentation, text classification and key statistical learning methods, and determines the use of Bayesian classification method as the main algorithm of intelligent classification learning. The system requirement analysis and corresponding system design of the improved spam short message interception system are described in detail, especially in the system function, system processing flow and module design. It shows the possibility and perfection of the application of Bayesian intelligent classification system and real-time interception system, and the system report will be used as the basis for the subsequent analysis and processing of spam short message. The test results of Bayesian intelligent classification system show that by preprocessing the text content of short message, using Bayesian classification algorithm can effectively classify short message more accurately, and compare the test results of manual classification. Intelligent classification system is more accurate than manual classification in dealing with a large number of text messages. To sum up, the spam short message interception system based on Bayesian classification algorithm improves the problem of insufficient self-learning ability of the system, reduces the dependence on manual operation to a large extent, and tracks and analyzes the change of spam short message for a long time in the future. And the real-time processing means for spam messages are of great help. The improved spam short message interception system can effectively intercept and filter the short message through the dual guarantee of real time interception and non real time intelligent analysis interception, which ensures that the spam short message intercepting system can run stably after it goes on line.
【學(xué)位授予單位】：上海師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP18;TP391.1
[Abstract]:With the rapid development of mobile communication network, SMS has become one of the most important ways for people to exchange information in real time. Although the application of spam short message interception technology has been successful, it is only used to identify spam messages by single keyword and filter the calling numbers of a large number of repeated messages with the same message content. However, through advertising, fraud and even illegal spam SMS has won huge benefits. Through statistics, it is found that in order to avoid this real-time strategy, the existence and dissemination of illegal spam SMS has changed greatly. Although it is possible to make statistical analysis of recent spam messages and formulate corresponding keyword combinations and interception strategies, they have to pay a huge human cost and only temporarily intercept spam messages that conform to the statistical rules. This has become one of the important reasons why spam messages can not be cured. Therefore, the combination of self-learning intelligent classification system and real-time spam message interception system is of great significance for long-term control and identification of filtered spam messages. On the premise of sufficient theoretical preparation, this paper gives a brief description of Chinese word segmentation, text classification and key statistical learning methods, and determines the use of Bayesian classification method as the main algorithm of intelligent classification learning. The system requirement analysis and corresponding system design of the improved spam short message interception system are described in detail, especially in the system function, system processing flow and module design. It shows the possibility and perfection of the application of Bayesian intelligent classification system and real-time interception system, and the system report will be used as the basis for the subsequent analysis and processing of spam short message. The test results of Bayesian intelligent classification system show that by preprocessing the text content of short message, using Bayesian classification algorithm can effectively classify short message more accurately, and compare the test results of manual classification. Intelligent classification system is more accurate than manual classification in dealing with a large number of text messages. To sum up, the spam short message interception system based on Bayesian classification algorithm improves the problem of insufficient self-learning ability of the system, reduces the dependence on manual operation to a large extent, and tracks and analyzes the change of spam short message for a long time in the future. And the real-time processing means for spam messages are of great help. The improved spam short message interception system can effectively intercept and filter the short message through the dual guarantee of real time interception and non real time intelligent analysis interception, which ensures that the spam short message intercepting system can run stably after it goes on line.
【學(xué)位授予單位】：上海師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP18;TP391.1
【參考文獻(xiàn)】
相關(guān)期刊論文 前10條
1 黃良友;;垃圾短信治理對策研究[J];重慶郵電大學(xué)學(xué)報(社會科學(xué)版);2010年03期
2 涂國章;;淺談垃圾短信及其治理[J];法制與社會;2009年17期
3 李輝;張琦;盧湖川;;基于內(nèi)容的垃圾短信過濾[J];計算機(jī)工程;2008年12期
4 婁懷東;杜旭;黃佳慶;;基于SIP的MoIP網(wǎng)關(guān)信令控制設(shè)計[J];計算機(jī)應(yīng)用;2006年01期
5 于海浩;;網(wǎng)絡(luò)不良信息檢索特征模型的研究[J];黑龍江工程學(xué)院學(xué)報(自然科學(xué)版);2012年02期
6 周茜,趙明生,扈e
本文編號：2207278
本文鏈接：http://sikaile.net/wenyilunwen/guanggaoshejilunwen/2207278.html
最近更新
教材專著
