頁籤選單縮合
題 名 | Detection of Anomalous Mailing Behavior Using Novel Data Mining Approaches |
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作 者 | Lin, Da-wei; Chen, Yi-ming; | 書刊名 | 資訊、科技與社會學報 |
卷 期 | 6:1=10 民95.06 |
頁 次 | 頁49-73 |
分類號 | 312.1 |
關鍵詞 | Anomalous behavior detection; Mailing behavior; Data mining; Grouping; |
語 文 | 英文(English) |
英文摘要 | The paper presents a novel method for detecting anomalous mailing behavior based on data mining approaches. Known or unknown email viruses may cause anomalous behaviors. Such behavior can be measured by deviations from a user’s normal behavior. Grouping and association analysis are used to establish a normal user profile. The building process is divided into two stages - first, group relation analysis and second, dependence relation analysis. Only group relationship analysis or both analyses may be selected, depending on the amount of data available to solve real problems. Bulk amounts of SENDMAIL log data are analyzed and virus behavior simulated. Empirical results indicate that this method of detecting anomalous mailing behavior, based on data mining, is highly accurate. A prototype system has also been designed and constructed. |
本系統中英文摘要資訊取自各篇刊載內容。