Please use this identifier to cite or link to this item:
標題: A Modiified Artificia Fish Swarm Algoriithm for Feature Selecttion and Parammeter Optimizzation of Suupport Vector Machine
作者: 陳斯揚
Sih-Yanng Cheen
關鍵字: artificial fish swarm algorithm;support vector machine;feature selection;classification;botnet detection;swarm intelligence;魚群演算法;支援向量機;特徵選取;分類;殭屍網路;群聚智慧
引用: [1] 王闖, 薛婷, 孫林燕, '人工魚群算法的現狀與改進分析,? 大連海事大學中國, 2008. [2] 陳嘉玫, 黃銘宗, '混合型殭屍網路偵測,? 國立中山大學資訊管理學系士學位論文, 台灣, 2010. [3] 廖文華, 陳志誠, 張嘉慶, '運用資料探勘技術於偵測 P2P 機器人網路之研巳研究? 大同大學資訊經營學系碩士學位論文, 台灣, 2010. [4] 林冠成, 林泳佐, '改良貓演算法應用於特徵值選取與支援向量機參數最佳化之研究,? 國立中興大學資訊管理學系碩士學位論文, 台灣, 2013. [5] 林冠成, 簡旭佑, '運用貓群演算法於特徵值選取與支援向量機參數最佳化之研究,? 國立中興大學資訊管理學系碩士學位論文, 台灣, 2010. [6] 雷祖強, 周天穎, 萬絢, 楊龍士, 許晉嘉, '空間特徵分類器支援向量機之研究,? 航測及遙測學刊, 12 卷 2 期, 2007. [7] 林冠成, 許聖華, '植基於改良式內分泌粒子群演算法之支持向量機特徵選取與參數最佳化,? 國立中興大學資訊管理學系碩士學位論文, 台灣, 2012. [8] 林冠成, 廖振利, '倒傳遞類神經網路結合特徵選取應用於殭屍網路偵測,?國立中興大學資訊管理學系碩士學位論文, 台灣, 2013. [9] Liu, H. and H. Motoda, Feature selection for knowledge discovery and data mining. Kluwer international series in engineering and computer science. 1998, Boston: Kluwer Academic Publishers. 214. [10] Jensen, R. and Q. Shen, Computational intelligence and feature selection : rough and fuzzy approaches. IEEE Press series on computational intelligence. 2008, Oxford: Wiley. [11] Kishore, J.K., et al., Application of genetic programming for multicategory pattern classification. IEEE Transactions on Evolutionary Computation, 2000. 4(3): p. 242‐258. [12] Stevens, R., et al., A classification of tasks in bioinformatics. Bioinformatics, 2001. 17(2): p. 180‐188. [13] Sanmay Das., Filters, Wrappers and a Boosting‐Based Hybrid for Feature Selection. Proceedings of the Eighteenth International Conference on Machine Learning. Pages 74-81. 2001. [14] Quinlan, J.R., C4.5 : programs for machine learning. The Morgan Kaufmann series in machine learning. 1993, San Mateo, Calif.: Morgan Kaufmann Publishers. x, 302 [15] Furey, T.S., et al., Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics, 2000. 16(10): p. 906‐914. [16] Zhang, G.Q.P., Neural networks for classification: A survey. IEEE Transactions on Systems Man and Cybernetics Part C‐Applications and Reviews, 2000. 30(4): p. 451‐462. [17] M. Abdel Fattah, The use of MSVM and HMM for sentence alignment. Journal of Information Processing Systems, vol. 8, no.2, 2012. [18] S. Farzi, Efficient job scheduling in grid computing with modified artificial fish swarm algorithm. International Journal of Computer Theory and Engineering, vol. 1, no. 1, pp. 13–18, 2009. [19] Deb, K. et al., A fast and elitist multiobjective genetic algorithm: NSGA‐II. Evolutionary Computation, IEEE Transactions on Vol6, Issue2, 2002. [20] Holland, J.H., Adaptation in Natural and Artificial Systems. 1975: The University Michigan Press, Ann Arbor. [21] Kennedy, J. and R.C. Eberhart. Particle swarm optimization. IEEE International Conference on Neural Networks. 1995. Perth, Australia. [22] Chu, S.C. and P.W. Tsai, Computational intelligence based on the behavior of cats. International Journal of Innovative Computing Information and Control, 2007. 3(1): p. 163‐173. [23] Dorigo, M, et al., Ant system: optimization by a colony of cooperating agents. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on Vol26, Issue1, 1996. [24] X.‐L. Li, Z.‐J. Shao, and J.‐X. Qian, Optimizing methodbased on autonomous animats: fish‐swarm Algorithm. System Engineering Theory and Practice, vol. 22, no. 11, pp. 32–38, 2002. [25] H. Chen, S. Wang, J. Li, and Y. Li, A hybrid of artificialfish swarm algorithm and particle swarm optimization forfeedforward neural network training. Proceedings of the International Conference on Intelligent Systems and Knowledge Engineering, 2007. [26] W. T. Strayer, D. Lapsely, R. Walsh, and C. Livadas, Botnet detection based on network behavior. Advances in Information Security, vol. 36, pp. 1–24, 2008. [27] V. N. Vapnik, The Nature of Statistical Learning Theory. Springer, New York, NY, USA, 1995. [28] Karush, W. Minima of functions of several variables with inequalities as side constraints. Department of Mathematics, University of Chicago, 1939. [29] T. Liu, Y.‐B. Hou, A.‐L. Qi, and X.‐T. Chang, Feature optimization based on Artificial Fish‐swarm Algorithm in intrusion detections. Proceedings of the International Conference on Networks Security, Wireless Communications and Trusted Com‐puting (NSWCTC '09), pp. 542–545, April 2009. [30] O. Avila‐garcía and Lola Cañamero, Using Hormonal Feedback to Modulate Action Selection in a Competitive Scenario. the proceedings of the 8th International Conference of Adaptive Behavior, pp. 243‐252, 2004. [31] L.Y. Zhuang, J.Q. Jiang, Artificial Fish Swarm Optimization Algorithm Based on Mixed Crossover Strategy. Advances in Neural Networks‐ISNN 2013, Vol. 7952, pp. 367‐374, 2013. [32] T.Fei, et al., The Artificial Fish Swarm Algorithmto Solve Traveling Salesman Problem. Advances in Intelligent Systems and Computing. Vol. 255, pp.679‐685, 2014. [33] K.Lenin, B.Ravindranath Reddy, M.Surya Kalavathi, Reduction of Real Power Loss by Using Enhanced Artificial Fish Swarm Algorithm. International Journal of Research in Electronics and Communication Technology (IJRECT 2014), Vol. 1, Issue. 2, April‐June 2014. [34] H.Men, et al., Application of Electronic Tongue in Edible Oil Detection with Cluster Algorithm based on Artificial Fish Swarm Improvement. Advance Journal of Food Science and Technology 5(4): pp.469‐473, 2013. [35] J.Bai, L.Yang, X.Zhang, Parameter Optimization and Application of Support Vector Machine Based on Parallel Artificial Fish Swarm Algorithm. Journal of Software, Vol. 8, No. 3, March 2013. [36] C. C. Chang and C. J. Lin, LIBSVM: A Library for Support Vector Machines. Available in May, 2014. [37] UC Irvine Machine Learning Repository Available in May, 2014. [38] 2013 Internet Security Threat Report, Volume 18, Symantec Corporation, Available at: Available in May, 2014.
With the advances in information technology, the age of Big Data has coming; and feature selection, which is how to find critical information among big data, has become
crucial topic. Therefore, in this research we propose a method of Modified Artificial Fish Swarm algorithms combines Support Vector Machine (SVM) for feature selection.
We also use this proposed model in Botnet detection and find the critical feature of Botnet virus.
The main idea of AFSA is simulate the behaviors of fish swarm and its swarm intelligence to solve optimization problems, such as schedule management and function
optimization. Although the prior research shows AFSA has great performance in function optimization, but AFSA still has a lots defect. Therefore, this research propose a modified AFSA, which is MAFSA, its combine the mechanism of endocrine and give appropriate search space for each fish.
To verify the effectiveness of this model, out experimental used famous datasets in machine learning, University of California, Irvine (UCI). The experimental result shows MAFSA has better classification rate and also find better the optimal feature subset.
Furthermore, this research also simulates a LAN environment which was infected by Botnet virus. The packet data of network flow in this LAN has been collected; and we use the propose model to find the critical feature of Botnet virus.

本篇研究提出改良式魚群演算法(Modified Artificial Fish Swarm Algorithm)結合支援向量機(Support Vector Machine)之方法應用於特徵選取,並利用此方法實作於殭屍網路之偵測,找出能判別殭屍網路攻擊的重要特徵。
本研究的實驗使用機器學習領域著名的 UCI(University of California, Irvine)資料集來測試演算法的效能,結果顯示改良式魚群演算法能有更好的分類正確率與較少的最佳化特徵子集合。另外本研究也模擬了一個受到殭屍網路攻擊之網路環境並收集網路封包數據,利用特徵選取之方法找出殭屍網路攻擊的重要特徵
其他識別: U0005-0707201511182200
Rights: 不同意授權瀏覽/列印電子全文服務
Appears in Collections:資訊管理學系

Files in This Item:
File Description SizeFormat Existing users please Login
nchu-103-7101029017-1.pdf2.23 MBAdobe PDFThis file is only available in the university internal network    Request a copy
Show full item record

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.