【摘要】： 電子郵件技術(shù)已經(jīng)成為一種快捷、經(jīng)濟的現(xiàn)代通信手段,幾乎每個網(wǎng)絡(luò)用戶都有自己的郵件信箱。然而電子郵件也日益成為商業(yè)廣告、病毒、木馬等內(nèi)容的重要載體,垃圾郵件的泛濫成災(zāi)給人們的正常生活帶來了極大的危害和不便,同時極壞地影響了網(wǎng)絡(luò)安全,占用了寶貴的帶寬資源,占用了郵件服務(wù)器大量的存儲空間。盡管目前已經(jīng)存在許多的垃圾郵件過濾方法,但是垃圾郵件不降反升的局面表明,已有的垃圾郵件過濾方法并未取得理想的過濾效果。所以,研究新型高效的郵件過濾系統(tǒng)仍具有特別重要的現(xiàn)實意義。在垃圾郵件過濾研究領(lǐng)域已有的算法中,都是基于規(guī)則或基于內(nèi)容的,其中基于規(guī)則的過濾算法需要用戶長期定制和維護規(guī)則,其實質(zhì)還是生硬的二值判斷,局限在二維空間內(nèi)進行處理,缺少可信度;基于內(nèi)容的過濾算法大多數(shù)是基于向量空間模型的算法,其中廣泛使用的是樸素貝葉斯算法和K最近鄰(KNN)算法。雖然樸素貝葉斯郵件過濾器計算簡便,但召回率和正確率都難以進一步提高。由于KNN算法計算復雜度太高而不適用于大規(guī)模場合和實時性要求高的場合。為此,提出郵件的合法屬性和非法屬性的概率,提出新的分類算法——基于郵件合法屬性和非法屬性的分類算法SEAFS算法。SEAFS垃圾郵件過濾算法結(jié)合KNN模型和樸素貝葉斯模型的優(yōu)點,克服了KNN模型和樸素貝葉斯模型的缺點,將普通垃圾郵件過濾方法的線性過濾轉(zhuǎn)化為非線性過濾,既提高了過濾準確度,又達到了令人滿意的過濾效率,適用于大規(guī)模場合和實時性要求高的場合,有利于大規(guī)模郵件內(nèi)容進行實時在線的垃圾郵件過濾。電子郵件的內(nèi)容是隨時間而變化的,用戶的個性化需求也在不斷改變,所以在對垃圾郵件的研究中加入了反饋學習過程,以捕捉這些變化,解決這一問題。本文設(shè)計并實現(xiàn)了一個實用、高效的垃圾郵件過濾系統(tǒng),進行了大量實驗,獲得了良好的過濾效果,實驗論證了SEAFS算法在垃圾郵件過濾中的可行性和有效性。
[Abstract]:E-mail technology has become a fast, economical modern means of communication, almost every network user has their own mail box. However, email is increasingly becoming an important carrier of commercial advertisements, viruses, Trojans, etc. The spamming of spam has brought great harm and inconvenience to people's normal lives, and at the same time, it has affected the network security very badly. It takes up valuable bandwidth resources and takes up a lot of storage space of mail server. Although there are many spam filtering methods at present, the situation that spam is not decreasing but rising shows that the existing spam filtering methods have not achieved the ideal filtering effect. Therefore, the study of new and efficient mail filtering system is still of great practical significance. Among the existing algorithms in the field of spam filtering, they are based on rules or content, in which rule-based filtering algorithms require users to customize and maintain rules for a long time. Limited in the two-dimensional space processing, the lack of credibility; Most content-based filtering algorithms are based on vector space model, among which naive Bayes algorithm and K-nearest neighbor (KNN) algorithm are widely used. Although naive Bayesian email filter is easy to calculate, it is difficult to improve recall rate and correct rate. Due to the high computational complexity of the KNN algorithm, it is not suitable for large-scale and real-time applications. For this reason, the probability of the legal and illegal attributes of mail is proposed, In this paper, a new classification algorithm, SEAFS algorithm based on legal and illegal attributes of mail, is proposed. The SEAFS spam filtering algorithm combines the advantages of KNN model and naive Bayesian model, and overcomes the shortcomings of KNN model and naive Bayesian model. The linear filtering of ordinary spam filtering method is transformed into nonlinear filtering, which not only improves the filtering accuracy, but also achieves satisfactory filtering efficiency. It is suitable for large-scale and real-time situations. Large-scale email content for real-time online spam filtering. The content of email changes with time, and the individual needs of users change constantly. So feedback learning process is added to the research of spam to capture these changes and solve this problem. In this paper, a practical and efficient spam filtering system is designed and implemented. A large number of experiments are carried out, and good filtering results are obtained. The experiment proves the feasibility and effectiveness of SEAFS algorithm in spam filtering.
【學位授予單位】：東北師范大學
【學位級別】：碩士
【學位授予年份】：2010
【分類號】：TP393.098

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本文編號：2328373