Abstract
Three technical problems should be solved urgently in cyberspace security: the timeliness and accuracy of network attack detection, the credibility assessment and prediction of the security situation, and the effectiveness of security defense strategy optimization. Artificial intelligence (AI) algorithms have become the core means to increase the chance of security and improve the network attack and defense ability in the application of cyberspace security. Recently, the breakthrough and application of AI technology have provided a series of advanced approaches for further enhancing network defense ability. This work presents a comprehensive review of AI technology articles for cyberspace security applications, mainly from 2017 to 2022. The papers are selected from a variety of journals and conferences: 52.68% are from Elsevier, Springer, and IEEE journals and 25% are from international conferences. With a specific focus on the latest approaches in machine learning (ML), deep learning (DL), and some popular optimization algorithms, the characteristics of the algorithmic models, performance results, datasets, potential benefits, and limitations are analyzed, and some of the existing challenges are highlighted. This work is intended to provide technical guidance for researchers who would like to obtain the potential of AI technical methods for cyberspace security and to provide tips for the later resolution of specific cyberspace security issues, and a mastery of the current development trends of technology and application and hot issues in the field of network security. It also indicates certain existing challenges and gives directions for addressing them effectively.
摘要
网络空间安全急需解决的3个技术问题是: 网络攻击检测的及时性和准确性、安全态势的可信评估和预测以及安全防御策略优化的有效性。人工智能算法已成为网络安全应用增加安全机会和提高对抗能力的核心手段。近年来, 人工智能技术的突破和应用为提高网络防御能力提供了先进的技术支持。本综述对2017至2022年间人工智能技术在网络空间安全领域的最新应用进行了全面回顾。参考文献来源于各种期刊和会议, 其中52.68%的论文来自Elsevier、Springer和IEEE期刊, 25%来自国际学术会议。本综述重点介绍了机器学习、深度学习和一些流行的优化算法在该领域的最新应用进展, 对算法模型的特点、性能结果、数据集、以及潜在的优点和局限性进行了分析, 强调了现存的挑战。本工作旨在为想进一步挖掘人工智能技术在网络空间安全领域应用的潜力、解决特定网络空间安全问题的研究人员提供技术指导, 掌握当前技术和应用的发展趋势以及网络安全领域的热点问题。同时, 本综述对当前面临的挑战提供了有效应对策略和方向。
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Jie CHEN determined the whole research framework and drafted the paper. Dandan WU searched the literature and made algorithm data analysis. Ruiyun XIE reviewed the entire research framework and technology. Jie CHEN and Dandan WU revised and finalized the paper.
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Jie CHEN, Dandan WU, and Ruiyun XIE declare that they have no conflict of interest.
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List of supplementary materials
1 Machine learning
2 Deep learning
3 Swarm intelligence optimization algorithm and population search algorithm
4 Main AI algorithms and applications
Fig. S1 Convolutional neural network method
Fig. S2 Recurrent neural network structure
Table S1 Main AI algorithms and applications
Supplementary materials
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Chen, J., Wu, D. & Xie, R. Artificial intelligence algorithms for cyberspace security applications: a technological and status review. Front Inform Technol Electron Eng 24, 1117–1142 (2023). https://doi.org/10.1631/FITEE.2200314
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DOI: https://doi.org/10.1631/FITEE.2200314
Key words
- Artificial intelligence (AI)
- Machine learning (ML)
- Deep learning (DL)
- Optimization algorithm
- Hybrid algorithm
- Cyberspace security