Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/92947
標題: A Modiified Artificia Fish Swarm Algoriithm for Feature Selecttion and Parammeter Optimizzation of Suupport Vector Machine
改良式魚群演算法應用於支援向量機之特徵選取與參數最佳化
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
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摘要: 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.
拜資訊科技蓬勃發展之賜,大資料的時代來臨,因此特徵選取,也就是如何在龐大、多維度的資料當中,有效率的找出關鍵的資訊成為相當重要的一門議題。 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。 魚群演算法模擬魚群覓食的行為與群聚智慧用來解決最佳化問題,例如組合最佳化與函數最佳化,雖然在過去研究顯示,魚群演算法在函數最佳化的表現優異但仍然有許多改進的空間,因此本篇論文提出了一種改良的魚群演算法,結合了模擬內分泌調節機制的方式,依據魚群內不同的個體的優劣程度而調整搜索空間。 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
URI: http://hdl.handle.net/11455/92947
其他識別: U0005-0707201511182200
文章公開時間: 10000-01-01
Appears in Collections:資訊管理學系

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