臺灣博碩士論文加值系統

智慧電網中的資訊安全問題日趨重要，特別是其資料作雙向傳輸時所產生的風險，其中最具代表的即為假數據注入攻擊(False Data Injection Attack, FDIA)。而後有人提出進階的假數據注入攻擊─不需得知此電力系統的架構。然而此方法依舊有部分條件限制，如電力系統的狀態需為非高斯(non-Gaussian)的隨機訊號，這限制使得它的攻擊層面不夠廣泛。本篇論文嘗試提出能應用在更廣泛環境下的攻擊演算法，利用主成分分析(Principal Component Analysis, PCA)對電錶量測資料(meter measurements)作分析拆解，並依照此資料的特性忽略部分元素，得到新攻擊矩陣。利用兩次投射矩陣的概念，說明新變數與原系統狀態(states)間的關係，並達到注入隨機數據攻擊的目的。文章末段的模擬實驗說明，除了在原環境下(non-Gaussian)可進行攻擊，在電力系統狀態均為高斯隨機訊號的環境下，依舊能達到不被系統偵測出的攻擊，達到攻擊層面更加廣泛的目的。

In smart grid, the security problems become more and more important owing to the vulnerability of two-way communication. Among them, the false data injection attack (FDIA) is the most common issue. Some people try to tackle these problems from protectors’ perspective, while others stand on the opposite side. Recently, advanced FDIA algorithms were proposed without needing the prior knowledge of the power system. But they still have some restrictions. For example, the states of power system need to be non-Gaussian distributed. This restriction limits advanced FDIA applications. In this thesis, we try to propose a general attack algorithm. We use principal component analysis (PCA) method to analyze meter measurements and get a matrix of the power system, which is then used to find the attack vector. Because of the characteristics of meter measurements, we can ignore partial part of the matrix of the power system. Applying the vector projection twice, we can find the relationship between power system and the attack vector by PCA. From simulations, we can observe that the new algorithm has more general applications for both Gaussian and non-Gaussian measurements.

中文摘要 ...................................................i
英文摘要 ..................................................ii
誌謝 ..................................................iii
目錄 ...................................................iv
圖目錄 ..................................................vii
符號說明 ..................................................ix
第一章 導論 ............................................1
1.1 背景知識 ...........................................1
1.2 研究動機 ...........................................3
1.3 文獻探討 ...........................................4
1.4 論文架構 ...........................................6
第二章 系統架構 ...........................................7
2.1 電力潮流 ...........................................7
2.2 狀態估測 ...........................................9
2.3 電力系統不良資料之偵測 ..........................12
第三章 假數據注入攻擊 ..................................13
3.1 傳統假數據注入攻擊 ..................................13
3.1.1 系統簡介與定義 ..................................13
3.1.2 攻擊演算法 ..................................15
3.2 有限資源下的隨機假數據注入攻擊 ..................16
第四章 進階假數據注入攻擊 ..................................18
4.1 資訊不完全下假數據注入攻擊 ..........................18
4.2 獨立成份分析介紹 ..................................19
4.2.1 獨立成份分析之概念與說明 ..........................19
4.2.2 中央極限定理 ..................................21
4.2.3 非高斯的測量值 ..................................22
4.2.4 資料前處理 ..................................26
4.2.5 FastICA演算法 ..................................26
4.3 利用ICA尋找假數據注入攻擊 ..........................28
第五章 利用主成份分析產生攻擊向量 ..........................29
5.1 主成份分析 ..................................29
5.1.1 背景介紹 ..........................................29
5.1.2 理論基礎 ..........................................30
5.1.3 投射矩陣證明推導 ..................................32
5.2 利用PCA作進階假數據注入攻擊 ..........................35
5.3 模擬與討論 ..................................39
5.3.1 非高斯訊號 ..................................39
5.3.2 高斯訊號 ..........................................47
5.3.3 混合訊號 ..........................................49
第六章 結論與未來展望 ..................................56
參考文獻 ..................................................57

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