臺灣博碩士論文加值系統

現代科技進步，網路上的應用和發展速度也隨著越來越快，智慧電網的相關應用也跟著越來越多，很多系統都可以利用遠端操控來進行處理，因此也有許多不法份子想利用其中的漏洞，進行一些網路上的攻擊、竊取資料等等的非法行為來達到自己的利益，在電力系統上攻擊者們利用許多干擾方式來進行攻擊，而其中有些正常事件也包含在當中，我們要如何去判斷受到的干擾類型為何，入侵檢測系統的工作就是幫使用者分辨這些案件，如何的把各種類型干擾案件正確的分類出來。
而現在要處理的資料量也比以前大上許多，大數據的時代來臨，延伸出許多的方法，深度學習就是其中之一，深度學習可以計算大量的資料，從這大量的資料中分析判斷出我們所需要的資訊，因此在這篇論文中我們將利用深度學習中的遞歸神經網路演算法，搭配著另外兩種其變形的模組來把資料做訓練，進行入侵檢測，將訓練出來的模型對事件進行預測，達到很好的效果。

With the advancement of modern science and technology, numerous applications of the Internet of Things are developing faster and faster. Smart grid is one of the examples which provides full communication, monitor, and control abilities to the components in the power systems in order to meet the increasing demands of reliable energy. In such systems, many components can be monitored and controlled remotely. As a result, they could be vulnerable to malicious cyber-attacks if there exist exploitable loopholes. In the power system, the disturbances caused by cyber-attacks are mixed with those caused by natural events. It is crucial for the intrusion detection systems in the smart grid to classify the types of disturbances and pinpoint the attacks with high accuracy.
The amount of information in a smart grid system is much larger than before, and the amount of computation of the big data increases accordingly. Many analyzing techniques have been proposed to extract useful information in these data and deep learning is one of them. It can be applied to “learn” a model from a large set of training data and classify unknown events from subsequent data. In this paper, we apply the methods of recurrent neural network (RNN) algorithm as well as two other variants to train models for intrusion detection in smart grid. Our experiment results showed that RNN can achieves high accuracy and precision on a set of real data collected from an experimental power system network.

誌謝 i
摘要 ii
Abstract iii
目錄 iv
圖目錄 vi
表目錄 vii
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 1
1.3 論文架構 2
第二章 文獻回顧與相關研究 3
2.1 基於主機的入侵檢測 3
2.2 基於網路的入侵檢測 4
2.3 基於數據挖掘的檢測 5
2.4 基於機器學習的偵測 6
第三章 研究方法 7
3.1 研究方法 7
3.2 資料前處理 8
3.3 建立模型 10
3.3.1 遞歸神經網路(RNN) 11
3.3.2 長短期記憶(LSTM) 13
3.3.3 Gated Recurrent Unit(GRU) 15
第四章 實驗結果 17
4.1 實驗環境 17
4.2 實驗資料集 18
4.3 準確度與效能評估 20
4.3.1 模型準確度 21
4.3.2 模型的效能評估 24
4.3.3 訓練模型使用的時間 27
第五章 結論與未來研究方向 28
5.1 結論 28
5.2 未來研究 28
參考文獻 29

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