search query: @author Zhu, D. / total: 2
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| Author: | Zhu, D. |
| Title: | Data Mining for Network Intrusion Detection: A Comparison of Alternative Methods |
| Journal: | Decision Sciences
2001 : FALL, VOL. 32:4, p. 635-660 |
| Index terms: | LEARNING NETWORKS COMPARATIVE RESEARCH METHOD STUDY |
| Language: | eng |
| Abstract: | Intrusion detection systems help network administrators prepare for and deal with network security attacks. These systems collect information from a variety of systems and network sources, and analyze them for signs of intrusion and misuse. A variety of techniques have been employed for analysis ranging from traditional statistical methods to new data mining approaches. In this study the performance of three data mining methods in detecting network intrusion is examined. An experimental design (3x2x2) is created to evaluate the impact of three data mining methods, two data representation formats, and two data proportion schemes on the classification accuracy of intrusion detection systems. The results indicate that data mining methods and data proportion have a significant impact on classification accuracy. |
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