Cited By
View all- Gu LWang Z(2024)A Multi-Scale Feature Extraction Method Based on Improved Transformer for Intrusion DetectionInternational Journal of Information Security and Privacy10.4018/IJISP.35689318:1(1-20)Online publication date: 7-Nov-2024
Network Information Security Monitoring Under Artificial Intelligence Environment
Authors: 
Longfei Fu, Yibin Liu, Yanjun Zhang, Ming LiAuthors Info & Claims
Longfei Fu, Yibin Liu, Yanjun Zhang, Ming LiAuthors Info & ClaimsPages 1 - 24
Abstract
At present, network attack means emerge in endlessly. The detection technology of network attack must be constantly updated and developed. Based on this, the two stages of network attack detection (feature selection and traffic classification) are discussed. The improved bat algorithm (O-BA) and the improved random forest algorithm (O-RF) are proposed for optimization. Moreover, the NIS system is designed based on the Agent concept. Finally, the simulation experiment is carried out on the real data platform. The results showed that the detection precision, accuracy, recall, and F1 score of O-BA are significantly higher than those of references [17], [18], [19], and [20], while the false positive rate is the opposite (P < 0.05). The detection precision, accuracy, recall, and F1 score of O-RF algorithm are significantly higher than those of Apriori, ID3, SVM, NSA, and O-RF algorithm, while the false positive rate is significantly lower than that of Apriori, ID3, SVM, NSA, and O-RF algorithm (P < 0.05).
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Index Terms
- Network Information Security Monitoring Under Artificial Intelligence Environment
Index terms have been assigned to the content through auto-classification.
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Published: 21 June 2024
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View all- Gu LWang Z(2024)A Multi-Scale Feature Extraction Method Based on Improved Transformer for Intrusion DetectionInternational Journal of Information Security and Privacy10.4018/IJISP.35689318:1(1-20)Online publication date: 7-Nov-2024