[1] 各作業系統漏洞,http://161.53.42.3/~crv/security/security.html
[2] 陳立昕,從兩岸Linux熱談其發展之迷思,MIC資訊市場情報中心,民90年7月。
[3] 郭顯鈞,本端行為與監控系統,國立台灣科技大學碩士論文,民90年6月。[4]詳細的通訊埠列表, ftp://ftp.isi.edu/in-notes/iana/assignments/port-numbers-old,南加大資科學院。
[5] 潘得龍、李序元 編譯,Maximum Linux Security之反駭客任務,第三波資訊股份有限公司,民90年6月。
[6] A. A. Cedeno and G. A. Suer, The use of a similarity coefficient-based method to perform clustering analysis to a large set of data with dissimilar papers, Computers ind. Engng, Vol. 33, Nos 1-2, pp. 225-228, 1997.
[7] A. Ghosh and A. Schwartzbard, A study in using neural networks for anomaly and misuse detection, In Proc. of the 8th USENIX Security Symposium, 1999.
[8] B. Özden, S. Ramaswamy, and A. Silberschatz, Cyclic association rules, In Proc. of the 14th Int’l Conf. on Data Engineering , pp. 412-421, 1998.
[9] D. A. Bandel, Linux security toolkit, IDG Books Worldwid, 2000.
[10] D. Anderson, T. Frivold, and A. Valdes, Next-generation intrusion detection expert system (nides): A summary, Technical Report SRI-CSL-95-07, SRI International, Menlo Park, CA, May, 1995.
[11] D. Denning, An intrusion detection model, IEEE Transactions on Software Engineering, Vol. 13(2): pp. 222-232,1987.
[12] D. E. Denning and P. G. Neumann, Requirements and model for IDES - a real-time intrusion detection system, Technical Report, SRI International, August 1985.
[13] E. Forgy, Cluster analysis of multivariate data: efficiency versus interpreability of classifications, Biomertrics, Vol. 21, p. 768. 1965.
[14] E. H. Spafford, The Internet Worm, Communication of ACM , pp. 678-687, June 1989.
[15] J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithm, Plenum Press, New York, 1981.
[16] J. S. Jang, C. T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing, Prentice Hall, New Jersey, 1997.
[17] L. A. Zadeh, Fuzzy Sets, Information Control, Vol.8, pp. 338-353, 1965.
[18] L. O. Hall, A. M. Bensaid, L. P. Clarke, R. P. Velthuizen, M. S. Sibiger, and J. C. Bezdek, A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain, IEEE Trans. Neural Networks, Vol. 3, pp. 672-682, 1992.
[19] M. F. Jiang, S. S. Tseng, C. M. Su, Two-phase clustering process for outliers detection, Pattern Recognition Letters, Vol. 22, pp. 691-700, 2001.
[20] M. Sugeno and T. Yasukawa, A fuzzy logic based approach to qualitative modeling, IEEE Trans. Fuzzy Systems, Vol. 1, pp. 7-13, 1993.
[21] M. Zait and H. Messatfa, A comparative study of clustering methods, Future Generation Computer System, Vol. 13, pp. 149-159, 1997.
[22] NetCraft:http://www.netcraft.com/
[23] R. H. Charles, Cluster analysis for researchers, 茂昌圖書有限公司, 1985.
[24] R. L. Cannon, J. Dave and J. C. Bezdek, Efficient implementation of the fuzzy c-means clustering algorithms, IEEE Trans. Pattern Anal. Machine Intelligence, Vol. 8, pp. 248-255, 1986.
[25] R. L. Cannon, V. Dave and J. C. Bezdek, Efficient implementation of the fuzzy c-means clustering algorithms, IEEE Trans. Pattern Anal. Machine Intelligence, Vol. 8, pp. 248-255, 1986.
[26] S. Forrest, S. Hofmeyr, A. Somayaji, and T. Longstaff, A sense of self for unix processes, In Proc. of IEEE Symposium on Security and Privacy, 1996.
[27] S. K. Pal and D. Majumdar, Fuzzy Mathematical Approach to Pattern Recognition, Wiley, New York, 1986.
[28] S. Ramaswamy, S. Mahajan, and A. Silberschatz, On the discovery of interesting patterns in association rules, In Proc. of the 1998 Int’l Conf. on Very Large Data Bases, pp. 368-379, 1998.
[29] T. Lane and C. E. Brodley, Approaches to online learning and concept drift for user identification in computer security, In Proc. of the 4th Int’l Conf. on Knowledge Discovery and Data Mining, pp. 259-263, 1998.
[30] T. Lane and C. E. Brodley, Temporal sequence learning and data reduction for anomaly detection, In Proc. of the 5th Conf. on Computer and Communications Security, pp. 150-158, 1998.
[31] T. P. Hong, A study of parallel processing and noise management on machine learning, Ph.D. Thesis, National Chiao Tung University, 1992.
[32] T. W. Cheng, D. B. Goldgof, and L. O. Hall, Fast fuzzy clustering, Fuzzy Sets and Systems, Vol. 93, pp. 49-56, 1998.
[33] TWNIC:http://www.twnic.com.tw/
[34] W. Lee and S. J. Stolfo, Data mining approaches for intrusion detection, In Proc. Of the 7th USENIX Security Symposium, 1998.
[35] W. Lee, S. J. Stolfo, and K. W. Mok, Mining audit data to build intrusion detection models, In Fourth Int’l Conf. On Knowledge Discovery and Data Mining, pp. 66-72, 1998.
[36] W. Lee, S. J. Stolfo, and K. W. Mok, A data mining framework for building intrusion detection models, In Proc. of the IEEE Symposium on Security and Privacy, pp. 120-132, 1999.
[37] Y. Li, N. Wu, X. S. Wang, and S. Jajodia, Enhancing profiles for anomaly detection using time granularities, Journal of Computer Security, IOS press, 2001.