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研究生:黃文政
研究生(外文):Wen.Cheng.Huang
論文名稱:利用迴歸理論在入侵偵測系統的特徵選取之研究
論文名稱(外文):Using Regression Theory for Feature Selection in Intrusion Detection System
指導教授:王偉德
指導教授(外文):Wai-Tak Wong
學位類別:碩士
校院名稱:中華大學
系所名稱:資訊管理學系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:中文
論文頁數:61
中文關鍵詞:特徵選取迴歸支持向量機
外文關鍵詞:Feature selectionRegressionSupport vector machine
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具我們所了解,有很多特徵影響入侵偵測系統的效能。換言之,過於太多未必要的雜訊將增加了入侵偵測的負荷,而且造成系統花過多的時間在處理不相關且不重要的資訊上,這間接的影響系統的效能。如果資訊超過入侵偵測系統的能力,偵測系統將難以提供對於環境線上即時防護作用,尤其在真實環境的異常偵測裡。隨著網路頻寬的增加,如何增進系統的處理效能已成為一個重要的研究議題。在本論文裡,我們提出特徵選取方法以迴歸理論來減少資料量以及提高偵測率。排除不必要的特徵,實驗結果的特徵可提高偵測率。我們實驗驗證利用最佳特徵集合能得到更好的效果。
As we know, the number of features affects the detection ability of an intrusion detection system. However, too much information will increase the load of the intrusion detection system and use too many resources on the unrelated information. This greatly affects the efficiency of the system. If the information to be processed comes in a rate exceeding the processing capability of the intrusion detection system, it is difficult for the system to provide on-line real-time protection to the corresponding environment. It is especially true with the anomaly intrusion detection. As the bandwidth and the traffic of network increase, this becomes an important issue to be studied. In this thesis, we propose a feature selection method based on regression theory to reduce the amount of information and increase detection rate. Unnecessary features are removed and the resulting feature set can improve the detection rate. Our experiment result shows that using the best feature model can get better performance.
ABSTRACT II
第一章 緒論 8
1.1研究背景與動機 8
1.2 研究目的 9
1.3 研究流程 10
1.4. 論文架構 10
第二章 相關研究 13
2.1 入侵偵測簡介 13
2.2 入侵偵測分類技術和特徵挑選探討 15
2.2.1 入侵偵測分類技術 15
2.2.2 特徵選取方法 17
2.3 特徵值選取相關研究 18
2.4 支持向量機研究 20
2.4.1 支持向量機與其他分類方法討論 23
2.4.2 支持向量機最佳化參數選擇問題 23
2.5 迴歸相關研究 24
2.5.1 複迴歸 24
2.5.2 邏輯迴歸 26
2.5.3 迴歸在特徵選擇 27
第三章 實驗設計 28
3.1 研究使用資料集簡介 28
3.2 研究步驟與方法 33
3.2.1 資料前置處理 36
3.2.2 迴歸選取變數研究方法 36
3.2.3 逐步複迴歸模式建立 37
3.2.4 邏輯迴歸模式建立 38
3.3 支持向量機實驗 41
3.3.1 實驗設計 41
3.3.2 資料前置處理 42
3.3.3 資料數值正規化 43
3.3.4 支持向量機模型選擇與參數測試 43
3.3.5 分類模組訓練與測試 43
第四章 實驗結果 45
第五章 結論及未來研究方向 55
5.1 結論 55
5.2 未來研究方向 56
參考文獻 57
中文文獻:
[1]賴左罕,「Information Security 資安人科技網」,入侵偵測系統的基本介紹(上),
[2]台灣電腦網路危機處理中心,中美資訊戰之分析,TWCER/CC文件
[3]鄭仁富,「2004年我國企業連網及應用程度調查分析報告」,資策會電子商務研究所FIND研究組,2004年
[4]蔡連發,「中小企業網路應用程度與競爭力關係之研究」,輔仁大學資訊管理學系在職專班碩士論文,2004年
[5]李俊偉、田筱榮、黃世昆,「入侵偵測分析方法評估與比較」,資訊安全通報,2002年
[6]簡嘉煌,「以成本效益模型評估入侵偵測系統」,中原大學資訊工程研究所碩士論文,2003年
英文文獻:
[7]CERT Coordination Center,“CERT/CC Statistics 1998-2003”,2004.
[8]SANS(System Administration, Networking and Security), http://www.sans.org/.
[9]C. Chang, J. Lin, “a library for support vector machines ”, 2003.
[10]http://kdd.ics.uci.edu/databases/kddcup99/task.html.
[11]http://www.ll.mit.edu/IST/ideval/data/data_index.html.
[12]http://www.cert.org.
[13]J. Kim, J. Lee, K. Han and M Lee, “Business as Buildings: Metrics For the Architectural Quality of Internet Businesses,” Internet Business Research Center, Yonsei University, 2002.
[14]C. Ranganathan and S. Ganapathy, “Key Dimensions of Business-to-Consumer Web Sites,” Information & Management, vo.l39, pp457-465, 2002.
[15]V.N., Vapnik, “The Nature of Statistical Learning Theory,” 1995.
[16]S. Mukkamala, A. H. Sung, “A comparative study of techniques for Intrusion
Detection,” 15th IEEE International Conference on Tools with Artificial Intelligence, pag.570, 2003.
[17]S. Mukkamala, G. Janoski, A. Sung, “Identifying Key features for Intrusion Detection Using Neural Networks,” Proceedings of the 15th international conference on Computer communication, vol.1, pp.1132-1138, 2002.
[18]J. Mill, A. Inoue, “Support vector classifiers and network intrusion detection,” Proceedings of IEEE International Conference on Fuzzy Systems, vol.1, pp.407-410, 2004.
[19]J. Han, M. Kamber, Data Mining Concepts and Techniques, Published by John Wiley & Sons, 1997.
[20]L. Wenke , J. Salvatore, Stolfo, “A Framework for Constructing Features and Models for Intrusion Detection Systems,” ACM Transactions on Information and System Security, vol.3, pp.227-261, 2000.
[21]H. Andrew, Suung, M. Srinivas, “Indentifying Important Features for Intrusion Detection Using Support Vector Machines and Neural Networks Proceedings of Symposium on Applications and the Internet,” Proceedings of Symposium on Applications and the Internet, IEEE, pp209-216, 2003.
[22]J. Chang. S. Shiuh-Pyng, “Detecting Distributed Dos/Scanning by Anomaly Distribution of Packet Fields,” Department of Computer Science and Information Engineering, National Chiao-Tung University, Hsin-Chu, Taiwan, 2002.
[23]D. Luca , G. Giorgio and R. Fabio, “Ensemble Learning for Intrusion Detection Computer Networks,” Department of Electrical and Electronic Engineering-University of Cagliari, 2002.
[24]H. Andrew. Sung, M. Srinivas, “Indentifying Important Features for Intrusion Detection Using Support Vector Machines and Neural Networks,” Proceedings of Symposium on Applications and the Internet, IEEE, 2003.
[25]S. Makkamala, A. H. Sung, “Feature ranking and selection for intrusion detection systems using support vector machines,” Proceedings of the International Conference on Information and Knowledge Engineering, Department of Computer Science, 2002.
[26]R. W. Swiniarski, S. Andrzej, “Rough set methods in feature selection and recognition,” San Diego State University, Department of Mathematical and Computer Sciences, vol.11, pp.565-582, 2003.
[27]H. Peng, Z.Dongna, W. Tiefeng, “An intrusion detection method based on roung and SVM algorithm,” International Conference on Communications, Circuits and Systems, 2004.
[28]L. H. Zhang, G. H. Zhang, Y. C. Bai, “Intrusion Detection Using Rough Set
Classification,” Department of Computer Science and Engineering, Shanghai 200030, China, 2004.
[29]Z. Pawlak, Rough sets, International Journal of Computer and Information Sciences, 1982.
[30]V. N. Vapnik, The Nature of Statistical Learning Theory, New York, 1995.
[31]S. Mukkamala, A. H. Sung “A comparative study of techniques for intrusion detection,” IEEE, 2003.
[32]S. Mukkamala, G. Janoski, A. Sung, “Intrusion detection using neural networks and support vector machines”, Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence, pp570, 2002.
[33]Y. Grandvalet, S. Canu, “Adaptive scaling for feature selection in SVMs,” Neural Information Processing System, 2002.
[34]T. Ambwani, “Multi class support vector machine implementation to intrusion detection,” Proceedings of the International Joint Conference of Neural Networks, vol.3, pp2300-2305, 2003.
[35]http://kdd.ics.uci.edu/databases/kddcup99/task.html
[36]http://www.ll.mit.edu/IST/ideval/data/data_index.html
[37]V. Cherkassky, “The nature of statistical learning theory,” IEEE Trans on Neural Networks, vol.8, pp1564-1564, 1995.
[38]S. J. Stolfo, F. Wei, L. Wenke, A. Prodromidis, P. K. Chan, “Cost-based modeling for fraud and intrusion detection: results form the JAM project,” Department of Computer Science, New Mexico Tech, Socorro, 2000.
[39]L. Pavel, D. Patrick, S. Christin and R. Konrad, “Learning intrusion detection: supervised or unsupervised?,” 13th International Conference on Image Analysis and Processing, 2005.
[40]M. Guchi, S. Goto, “Network surveillance for detecting intrusions,” Internet Workshop, IEEE, pp.99-106, 1999.
[41]M. Joseph, McAlerne and S. Stuart, A. James, Hoagland, “Practical Automated Detection of Stealthy Portscans,” Silicon Defense, vol.10, pp.105-136, 2002.
[42]J. B. D. Caberera, B. Ravichandran, M. Raman. K, “Statistical traffic modeling for network intrusion detection,” Proceedings of the 8th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, pp.466, 2000.
[43]AK. Ghosh, J. Wanken, F. Charron, “Detecting anomalous and unknown intrusions against programs,” Proceedings of the 14th Annual Computer Security Applications Conference, pp259-267, 1998.
[44]A. Phillip, Porras, “Detecting Computer and Network Misuse Through the Production-Based Expert System Toolset,” In Proceedings of the IEEE Symposium on Security and Privacy, 1999.
[45]Terrance Goan-PI, “Intelligent Correlation of Evidence for Intrusion Detection,” Technical Report #183, Stottler Henke Associate Inc.
[46]I. Koral, K. Richard A, and P. Phillip A, “A Rule-Based Intrusion Detection Approach,” IEEE Transactions on Software Engineering, pp.181-199, 1995.
[47]J.E. Dickerson, J.A. Dickerson, ”Fuzzy network profiling for intrusion detection,” Fuzzy Information Processing Society, IEEE, pp.301-306, 2000.
[48]C. Philip, J.S. Salvatore, “On the Accuracy of Meta-Learning for Scalable Data Mining,” Journal of Intelligent Information System, vol.8, pp.5-28, 1997.
[49]C. Chang, J. Lin, “ a library for support vector machines,”
http://www.csie.ntu.edu.tw/~cjlin/libsvm, 2003.
[50]W. T. Wong, and W. C. Huang,“Toward the Best Feature Model for Network Intrusion Detection Using Stepwise Regression and Support Vector Machine,” International Conference of Machine Learning and Cybernetics, 2006.
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