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研究生:文紹安
研究生(外文):Shao-An Wen
論文名稱:基於量子啟發式禁忌搜尋演算法之入侵偵測系統
論文名稱(外文):Intrusion Detection System Based on Quantum-inspired Tabu Search Algorithm
指導教授:王行健
指導教授(外文):Sying-Jyan Wang
口試委員:郭姝妤范耀中李淑敏
口試委員(外文):Shu-Yu KuoYao-Chung FanShu-Min Li
口試日期:2023-07-31
學位類別:碩士
校院名稱:國立中興大學
系所名稱:資訊工程學系所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2023
畢業學年度:111
語文別:中文
論文頁數:30
中文關鍵詞:入侵偵測系統萬用啟發式演算法
外文關鍵詞:intrusion detection systemmeta-heuristic algorithm
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  • 被引用被引用:0
  • 點閱點閱:92
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  • 收藏至我的研究室書目清單書目收藏:0
隨著網路與相關技術飛速發展的浪潮,網路上的惡意事件也隨之如雨後春筍般不斷發生。為了抵禦相關惡意事件,最常見防範方法是部屬入侵偵測系統用以即時檢測是否有惡意入侵的行為。而入侵偵測系統必須有效地檢測出盡可能多的異常網路行為。我們在本研究中提出了基於萬用啟發式演算法的入侵偵測系統,我們使用了新穎的萬用啟發式演算法-量子啟發式禁忌搜尋演算法,做為入侵偵測系統的基本框架,並加入投票機制使我們的方法可以有效且高效地檢測網路中的異
常行為。我們的實驗結果表明,我們提出的基於量子啟發式禁忌搜尋演算法的入侵偵測系統能夠區分正常網路流量與惡意入侵行為之間的差異性,並且在兩個入侵偵測資料集上得到較高的辨識率。
With the tide of rapidly technology development, the malicious events also increased in a skyrocket speed. The common way to defense the attack from Internet is to deploy an intrusion detection system (IDS). IDS must detect the anomaly network traffic efficiently. We have proposed a meta-heuristic algorithm-based intrusion detection system. Our research uses the novel meta-heuristic algorithm, Quantum-inspired Tabu Search algorithm (QTS), as the backbone of the intrusion detection system and add the voting mechanism to detect the anomaly behaviors effectively and efficiently. Our
experimental results show that the QTS based IDS has the ability to tell the difference between the normal network traffic and the malicious behaviors and achieves high accuracy on two intrusion detection benchmarks.
摘要 i
Abstract ii
Table of Contents iii
List of Figures v
List of Tables vi
1. Introduction 1
2. Related Work 4
2.1. NSLKDD dataset [16] 5
2.2. UNSW-NB 15 dataset [17] 7
3. The proposed method 10
3.1 The encoding method 11
3.2 Quantum-inspired Tabu Search algorithm [12] 12
3.2.1. Initialization 13
3.2.2. The rule generation 13
3.2.3. Training 15
3.2.4. Fitness 15
3.2.5. Best and worst selection 16
3.2.6. Update 16
3.3 The voting mechanism 16
4. Experimental result 18
4.1 Evaluation metrics 18
4.2 QTS vs GQTS 20
4.3 Comparison 20
4.4 Performance analysis 21
5. Conclusion 26
References 27
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