基於生成對抗網路技術之惡意網路流量生成模型

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吳宗勲(Tsung-Hsun Wu) 查詢紙本館藏

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資訊工程學系

論文名稱

基於生成對抗網路技術之惡意網路流量生成模型
(MCM-GAN: Malicious Cyber Maker using Generative Adversarial Networks)

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摘要(中)

隨著網路的迅速發展，惡意攻擊事件日益增多，為了應對這種情況，使用機器學習 (Machine Learning) 或深度學習 (Deep Learning) 在入侵檢測系統 (In-trusion Detection System, IDS) 上來偵測惡意流量已成為主要趨勢。然而，由於許多資料集是公開的，並且公開資料集往往存在資料不平衡的問題，常見的解決方案是透過合成少數過採樣技術 (Synthetic Minority Over-sampling Technique, SMOTE) ，但這種方法所生成的流量缺乏真實性，如何有效地生成多樣且符合真實狀況的流量是為一大挑戰。
本論文提出了一種名為 "Malicious Cyber Maker GAN (MCM-GAN) " 的架構，旨在解決訓練不穩定性和模式崩潰 (Mode collapse) 問題，確保生成樣本多樣性。該架構採用了Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) 設計，通過引入梯度懲罰來改進原有的權重剪切方法，使生成過程更加穩定。此外，本論文還引入了條件生成對抗網路 (Conditional Generative Adversarial Network, CGAN) 和雙時間尺度更新規則 (Two Time-Scale Update Rule, TTUR) ，以進一步提高生成效果和訓練效率。在UNSW-NB15資料集上的實驗結果顯示，使用MCM-GAN生成9種網路惡意攻擊類型時，訓練時間相比使用LSTM作為生成器和鑑別器減少了21.99%，模型大小則減少了27.62%。使用原始資料結合MCM-GAN生成資料進行訓練的XGBoost、Random Forest和SVC模型，其預測F1-Score分別達到89.07%、87.04%和84.31%。相較於SVM-SMOTE、GRU-CGAN和LSTM-CGAN等生成技術，MCM-GAN能達到更高的F1-Score。此外，相較於僅使用原始資料訓練的模型，這些模型的平均F1-Score分別提升了6.83%、6.48%和9.91%。

摘要(英)

With the rapid development of the Internet, malicious attacks are becoming in-creasingly common. To address this issue, the use of machine learning or deep learn-ing technologies in intrusion detection systems (IDS) to detect malicious traffic has become a major trend. However, since many datasets are public and often have data imbalances, a common solution is to use the Synthetic Minority Over-sampling Tech-nique (SMOTE). However, the traffic generated by this method lacks authenticity. Effectively generating diverse and realistic traffic is a significant challenge.
This paper presents an architecture called "Malicious Cyber Maker GAN (MCM-GAN)", which aims to address training instability and mode collapse issues by ensuring diversity in the generated samples. This framework adopts the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) design, which improves on the original weight clipping method by incorporating a gradient penalty to stabilize the generation process. In addition, the work incorporates the Conditional Generative Adversarial Network (CGAN) and the Two Time-Scale Update Rule (TTUR) to further improve generation effectiveness and training efficiency. Experimental results on the UNSW-NB15 dataset show that when generating nine types of malicious network attacks using MCM-GAN, the training time was reduced by 21.99% compared to using LSTM as the generator and discriminator, and the model size was reduced by 27.62%. Models trained on original data combined with MCM-GAN generated data, namely XGBoost, Random Forest and SVC, achieved F1 scores of 89.07%, 87.04% and 84.31% respectively. These accuracy levels are higher compared to generation techniques such as SVM-SMOTE, GRU-CGAN and LSTM-CGAN. In addition, these models showed an average F1 score improvement of 6.83%, 6.48% and 9.91%, respectively, compared to models trained only on original data.

關鍵字(中)

★ 條件式生成對抗網路
★ 資料不平衡
★ 惡意網路流量分類
★ 入侵檢測系統

關鍵字(英)

★ Conditional Generative Adversarial Network
★ Traffic Classification
★ Malicious Network Traffic Classification
★ Intrusion Detection System

論文目次

摘要 i
Abstract iii
誌謝 v
目錄 vi
圖目錄 ix
表目錄 xi
第一章緒論 1
1.1. 概要 1
1.2. 研究動機 2
1.3. 研究目的 3
1.4. 章節架構 4
第二章背景知識與相關研究 5
2.1. 生成對抗網路 5
2.2. 閘門循環單元 6
2.3. 條件式生成 8
2.4. 雙時間尺度更新規則 9
2.4. 入侵檢測系統 10
2.5. 相關研究 12
第三章研究方法 15
3.1. 系統架構與設計 15
3.2. 系統運作流程與實作 17
3.2.1. 資料前處理 17
3.2.2. 惡意網路流量生成模型 20
3.2.3. IDS-Verification 26
3.3. 系統環境 32
第四章實驗與討論 34
4.1. 情境一：驗證使用GRU作為生成器與鑑別器於生成網路惡意流量的成效 34
4.1.1. 實驗一：GRU-GAN 與支援向量機結合合成少數過採樣技術資料分佈主成分分析之比較 36
4.1.2. 實驗二：GRU-GAN之二元交叉熵損失變化 38
4.2. 情境二：分析MCM-GAN效能並與其他模型之比較 39
4.2.1. 實驗三：MCM-GAN與不同模型生成的分布之比較 41
4.2.2. 實驗四：MCM-GAN與不同模型之參數使用量及模型大小之比較 43
4.2.3. 實驗五：MCM-GAN 與其他模型之平均訓練時間比較 45
4.3. 情境三：MCM-GAN於IDS- Verification之有效性驗證 46
4.3.1. 實驗六：比較MCM-GAN生成資料提升IDS之F1-Score成效 47
4.3.2. 實驗七：比較不同的生成技術對於IDS優化之成效 52
4.3.3. 實驗八：比較使用不同優化器於MCM-GAN之生成效果 53
第五章結論與未來研究方向 55
5.1.1. 結論 55
5.1.2. 研究限制 55
5.1.3. 未來研究 56
參考文獻 58

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指導教授

周立德(Li-Der Chou)

審核日期

2024-8-9

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