Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/24262
標題: DDoS攻擊與SYN Flood偵測之研究
A Study of DDoS and Detection of SYN Flood
作者: 彭志翔
Peng, Chih-Hsiang
關鍵字: 分散式阻斷服務攻擊
DDoS
殭屍網路
入侵偵測系統
特徵選取
植基於質心的分類器
botnet
IDS
feature selection
centroid-based classification
出版社: 資訊管理學系所
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摘要: 然網路帶來許多便利性,但也潛藏著許多攻擊。隨著殭屍網路的發展,這些攻擊的規模也持續成長中。分散式阻斷服務攻擊是殭屍網路造成的攻擊之一,而在眾多的分散式阻斷服務攻擊中,SYN flood 較為普遍而且也嚴重造成可用性的大大降低。為了提升資訊安全,入侵偵測系統被提出來作為偵測攻擊的工具,而一個完善的入侵偵測系統則包含了特徵選取和偵測等部分。此研究的目的在於提出一個可以偵測SYN flood 的架構,首先對殭屍網路的現況作描述,接下來六個特徵值被選取當作偵測SYN flood 特徵值,並使用相關分析對每個特徵值作分析。最後,一個可以偵測SYN flood 的架構被提出,這個架構使用植基於質心的分類器來對封包資料作分群。此架構整體而言有高的效能,擁有97.6% 的偵測率,97.2%的準確率和2.3% 的誤判率。
With the rapid growth of technology, Internet has become a tool that can solve many problems in life. Although the usage of Internet is practical and it can also enhance overall efficiency, it exists several kinds of attacks in Internet. Distributed denial of service is one of the attacks that are caused by botnet. In several kinds of Distributed denial of service, SYN flood happens more often and reduces availability. To enhance information security, intrusion detection system is proposed to detect attacks from Internet. In a complete intrusion detection system, feature selection and detection are two topics that will influence overall performance. The goal of this study is to propose a framework that can detect SYN flood effectively. To design a complete framework, the information of current botnet is needed, which includes the architecture of botnet, attacks, the methodology of detecting botnet and the technique. Before designing an intrusion detection system, feature selection is needed, and it is completed in a statistic method called correlation analysis. Finally, a framework that is used to detect SYN flood is proposed, which centroid-based classification is applied in detection phase. With the proposed framework, it can detect SYN flood with high performance, which the detection rate is 97.6 percent, the accuracy rate is 97.2 percent and the false alarm rate is 2.3 percent.
URI: http://hdl.handle.net/11455/24262
其他識別: U0005-2406201315350700
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-2406201315350700
Appears in Collections:資訊管理學系

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