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研究生： 連茂棋
Lian, Mao-Ci
論文名稱： 探索類神經網路於網路流量異常偵測中的時效性需求
Exploring the timeliness requirement of artificial neural networks in network traffic anomaly detection
指導教授： 蔡瑞煌
Tsaih, Rua-Huan
學位類別： 碩士
Master
系所名稱： 商學院 - 資訊管理學系
Department of Management Information System
論文出版年： 2017
畢業學年度： 105
語文別： 英文
論文頁數： 52
中文關鍵詞： 網路流量異常偵測 、機器學習 、GPU平行運算 、類神經網絡 、張量流
外文關鍵詞： Network traffic anomaly detection, Machine learning, GPU parallel operation, Artificial neural networks, TensorFlow
相關次數： 點閱：31  下載：3 
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    • 雲端的盛行使得人們做任何事都要透過網路，但是總會有些有心人士使用一些惡意程式來創造攻擊或通過網絡連接竊取資料。為了防止這些網路惡意攻擊，我們必須不斷檢查網路流量資料，然而現在這個雲端時代，網路的資料是非常龐大且複雜，若要檢查所有網路資料不僅耗時而且非常沒有效率。
    本研究使用TensorFlow與多個圖形處理器(Graphics Processing Unit, GPU)來實作類神經網路(Artificial Neural Networks, ANN)機制，用以分析網路流量資料，並得到一個可以判斷正常與異常網路流量的偵測規則，也設計一個實驗來驗證我們提出的類神經網路機制是否符合網路流向異常偵測的時效性和有效性。
    在實驗過程中，我們發現使用更多的GPU可以減少訓練類神經網路的時間，並且在我們的實驗設計中使用三個GPU進行運算可以達到網路流量異常偵測的時效性。透過該方法得到的初步實驗結果，我們提出機制的結果優於使用反向傳播算法訓練類神經網路得到的結果。

    The prosperity of the cloud makes people do anything through the Internet, but there are people with bad intention to use some malicious programs to create attacks or steal information through the network connection. In order to prevent these cyber-attacks, we have to keep checking the network traffic information. However, in the current cloud environment, the network information is huge and complex that to check all the information is not only time-consuming but also inefficient.
    This study uses TensorFlow with multiple Graphic Processing Units (GPUs) to implement an Artificial Neural Networks (ANN) mechanism to analyze network traffic data and derive detection rules that can identify normal and malicious traffics, and we call it Network Traffic Anomaly Detection (NTAD).
    Experiments are also designed to verify the timeliness and effectiveness of the derived ANN mechanism. During the experiment, we found that using more GPUs can reduce training time, and using three GPUs to do the operation can meet the timeliness in NTAD. As a result of this method, the experiment result was better than ANN with back propagation mechanism.

    Chapter 1 Introduction 1
    1.1 Background & Motivation 1
    1.2 Purpose 3
    Chapter 2 Literature Review 5
    2.1 Cyber-Attack and Network Anomaly Detection 5
    2.2 Machine Learning & Artificial Neural network 7
    2.3 GPU Parallel Operation & Tensorflow 8
    2.3.1 The Developing of GPU Parallel Operation 8
    2.3.2 Tensorflow 9
    2.4 A Mechanism for Detecting Outlier 13
    2.4.1 Concept Drifting 13
    2.4.2 Single-Hidden Layer Feedforward Neural Networks (SLFN) 14
    2.4.3 The Resistant Learning with Envelope Module 15
    2.4.4 Moving Window 18
    Chapter 3 Experiment 20
    3.1 Network Traffic Data Set & Data Preprocessing 20
    3.2 ANN of NTAD 23
    3.3 Timeliness & Effectiveness of NTAD 24
    3.4 The derived ANN mechanism 26
    3.5 Experiment Environment 28
    Chapter 4 Experimental Results 30
    4.1 The Relationship between Training Time and Amounts of GPUs 30
    4.2 The effectiveness of the derived ANN mechanism. 34
    4.3 Comparing with other ANN mechanism 40
    Chapter 5 Conclusions and Future Works 47
    5.1 Conclusions 47
    5.2 Future Works 49
    Reference 50

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