面向多維數據的異常點檢測模型設計*
網絡安全與數據治理 7期
馬勇,,楊敏,朱琳
(1.內蒙古科技大學包頭醫(yī)學院網絡信息中心,,內蒙古包頭014040,; 2.內蒙古科技大學包頭醫(yī)學院教務處,內蒙古包頭014040)
摘要: 為了在大數據環(huán)境下快速,、精準地挖掘異常點,,保障網絡安全,提出了一種面向多維數據的異常點檢測模型設計方案,。該方案利用長短期記憶網絡(LSTM)存儲任意時間段的多維數據,,并使用圖卷積網絡提取完整數據結構,同時加入懲罰參數和均方誤差來縮小異常點出現范圍,。此外,,還利用編碼器和解碼器構建變分自編碼器函數模型,使其能夠解讀正常數據子特征,,并通過編碼重建損失函數來計算數據異常度量,,從而實現異常點檢測。經過實驗驗證,,該方法表現出較高的檢測正確率和運行效率,,具有極高的應用價值。
中圖分類號:TP995
文獻標識碼:A
DOI:10.19358/j.issn.2097-1788.2023.07.014
引用格式:馬勇,,楊敏,,朱琳.面向多維數據的異常點檢測模型設計[J].網絡安全與數據治理,2023,,42(7):85-90.
文獻標識碼:A
DOI:10.19358/j.issn.2097-1788.2023.07.014
引用格式:馬勇,,楊敏,,朱琳.面向多維數據的異常點檢測模型設計[J].網絡安全與數據治理,2023,,42(7):85-90.
Design of outlier detection model for multidimensional data
Ma Yong,Yang Min,Zhu Lin
1Network Information Center Inner Mongolia University of Science and Technology Baotou Medical College, Baotou 014040, China,; 2Dean′s Office Inner Mongolia University of Science and Technology Baotou Medical College, Baotou 014040,China)
Abstract: In order to quickly and accurately mine outliers in the big data environment and ensure network security, we propose a design scheme for multidimensional data oriented outlier detection model. In this scheme, the long short memory network (LSTM) is used to store multidimensional data in any period of time, and the graph convolution network is used to extract the complete data structure. At the same time, penalty parameters and mean square error are added to narrow the range of outliers. In addition, we also use the encoder and decoder to build a variational self encoder function model, so that it can interpret the normal data sub features, and calculate the data anomaly measurement through the coding reconstruction loss function, so as to achieve outlier detection. After experimental verification, this method exhibits high detection accuracy and operational efficiency, and has high application value.
Key words : coding loss function; variational self encoder; abnormal point detection; long and short term memory network; multidimensional data
0 引言
針對目前異常數據檢測方法占用空間內存大,且異常點漏檢率與誤檢率高問題[1],,建立一種面向多維數據異常點挖掘方法是很有必要的,,建立的方法必須要保證在實際數據異常點檢測過程中,既能夠快速響應,,又能縮小異常檢測范圍,、降低異常檢測錯誤率,這是一個很具有挑戰(zhàn)性的問題,。
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作者信息:
馬勇,,楊敏,,朱琳
(1.內蒙古科技大學包頭醫(yī)學院網絡信息中心,內蒙古包頭014040,;2.內蒙古科技大學包頭醫(yī)學院教務處,,內蒙古包頭014040)
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