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研究生:李家豪
研究生(外文):Lee,Jiahau
論文名稱:深度分析入侵偵測系統之效能
論文名稱(外文):Dissecting NIDS Performance with Detailed Profiling
指導教授:林柏青林柏青引用關係
指導教授(外文):Lin,Poching
口試委員:李程輝林柏青陳鵬升江為國
口試委員(外文):Lee,TsernhueiLin,PochingChen,PengshengChiang,Weikuo
口試日期:2011-06-15
學位類別:碩士
校院名稱:國立中正大學
系所名稱:資訊工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2011
畢業學年度:99
語文別:英文
論文頁數:40
中文關鍵詞:入侵偵測系統效能
外文關鍵詞:NIDSperformance
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Designing a high-speed NIDS (network intrusion detection system) has
attracted much attention over recent years due to ever-increasing amount
of network trac and ever-complicated attacks. Deeply studying the NIDS
performance is an important step toward a high-speed design. This work
studies how the NIDS performance can vary with input network traffic, in-
cluding malicious trac, and system configuration, based on detailed pro-
filing with two popular NIDSs, Snort and Bro. According to the profiling,
we find analyzing the payloads (primarily pattern matching in Snort and
executing the policy scripts in Bro) can dominate the execution time for
most of packet traces. Moreover, connection tracking and packet reassembly
can be also time-consuming if they are frequently invoked. Therefore, a ro-
bust high-speed NIDS design can focus on improving payload analysis and
preprocessing, particularly packet reassembly. We also demonstrated that
aggregating the profiling results can be used to predict the results for bulk
network traffic in a real environment. In other words, it is feasible to watch
the composing traffic types in the bulk traffic and individually analyzing the
sample of each type to extrapolate the performance for the total traffic.
1 Introduction . . . . . . . . . . . . . . . . . . . . 1
2 Background and Related Work . . . . . . . . . . . . . . . . 5
2.1 Processing Stages in Snort and Bro . . . . . . . . . . . . . . . 5
2.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3 NIDS Profiling in Snort . . . . . . . . . . . . . . . . . 11
3.1 Snort Stages in the Profiling . . . . . . . . . . . . . . . . . . . 12
3.2 Baseline Profiling with Normal Traffic . . . . . . . . . . . . . . 13
3.3 Profiling with Abnormal Network Traffic . . . . . . . . . . . . 16
3.3.1 Traffic with IP fragments and TCP segments . . . . . . 17
3.3.2 Profiling with various system configurations . . . . . . 20
3.4 Deep Observation and Testing with the Bulk Traffic . . . . . . 22
3.4.1 Analyzing the Factors in Pattern Matching . . . . . . . 22
3.4.2 Testing with the Bulk Traffic . . . . . . . . . . . . . . 24
ii4 NIDS Profiling in Bro . . . . . . . . . . . . . . . . . . . . 26
4.1 Bro Stages in the Profiling . . . . . . . . . . . . . . . . . . . . 26
4.2 Baseline Profiling in Bro . . . . . . . . . . . . . . . . . . . . . 27
4.2.1 Profiling the Execution of Policy Scripts . . . . . . . . 29
4.3 Abnormal Traffic . . . . . . . . . . . . . . . . . . . . . . . . . 32
4.4 Testing with the Bulk Traffic . . . . . . . . . . . . . . . . . . . 33
5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
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