Longfei Fu’s scientific contributions

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Publications (1)


Network Information Security Monitoring Under Artificial Intelligence Environment
  • Article
  • Full-text available

June 2024

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62 Reads

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1 Citation

Longfei Fu

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Yibin Liu

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Yanjun Zhang

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Ming Li

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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Citations (1)


... With the advancement of information technology, the network environment has become increasingly complex, leading to exacerbated network security issues such as intrusion attacks and network viruses. A network intrusion detection system (NIDS; Fu et al., 2024) is a real-time network security audit and management system that can respond to network attacks promptly and provide security protection. Network traffic is one of the core indicators of network operation, and network intrusions often cause anomalies in network traffic. ...

Reference:

A Multi-Scale Feature Extraction Method Based on Improved Transformer for Intrusion Detection
Network Information Security Monitoring Under Artificial Intelligence Environment