Using WPCA and EWMA Control Chart to Construct a Network Intrusion Detection Model
Artificial intelligence algorithms and big data analysis methods are commonly employed in network intrusion detection systems. However, challenges such as unbalanced data and unknown network intrusion modes can influence the effectiveness of these methods. Moreover, the information personnel of most...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
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| Series: | IET Information Security |
| Online Access: | http://dx.doi.org/10.1049/2024/3948341 |
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| Summary: | Artificial intelligence algorithms and big data analysis methods are commonly employed in network intrusion detection systems. However, challenges such as unbalanced data and unknown network intrusion modes can influence the effectiveness of these methods. Moreover, the information personnel of most enterprises lack specialized knowledge of information security. Thus, a simple and effective model for detecting abnormal behaviors may be more practical for information personnel than attempting to identify network intrusion modes. This study develops a network intrusion detection model by integrating weighted principal component analysis into an exponentially weighted moving average control chart. The proposed method assists information personnel in easily determining whether a network intrusion event has occurred. The effectiveness of the proposed method was validated using simulated examples. |
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| ISSN: | 1751-8717 |