Application of meta-learning in cyberspace security: a survey

• Published in: Digital communications and networks Vol. 9; no. 1; pp. 67 - 78
• Main Authors: Yang, Aimin, Lu, Chaomeng, Li, Jie, Huang, Xiangdong, Ji, Tianhao, Li, Xichang, Sheng, Yichao
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2023; Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes,North China University of Science and Technology,Tangshan,063000,China%Hebei Key Laboratory of Data Science and Application,North China University of Science and Technology,Tangshan,063000,China; Hebei Key Laboratory of Data Science and Application,North China University of Science and Technology,Tangshan,063000,China; The Key Laboratory of Engineering Computing in Tangshan City,North China University of Science and Technology,Tangshan,063000,China%Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes,North China University of Science and Technology,Tangshan,063000,China%The Key Laboratory of Engineering Computing in Tangshan City,North China University of Science and Technology,Tangshan,063000,China; KeAi Communications Co., Ltd
• Subjects: Cyberspace security; Few-shot learning; Machine learning; Meta-learning; Cyberspace security; Few-shot learning; Meta-learning; Machine learning
• ISSN: 2352-8648; 2352-8648
• DOI: 10.1016/j.dcan.2022.03.007

In recent years, machine learning has made great progress in intrusion detection, network protection, anomaly detection, and other issues in cyberspace. However, these traditional machine learning algorithms usually require a lot of data to learn and have a low recognition rate for unknown attacks. Among them, “one-shot learning”, “few-shot learning”, and “zero-shot learning” are challenges that cannot be ignored for traditional machine learning. The more intractable problem in cyberspace security is the changeable attack mode. When a new attack mode appears, there are few or even zero samples that can be learned. Meta-learning comes from imitating human problem-solving methods as humans can quickly learn unknown things based on their existing knowledge when learning. Its purpose is to quickly obtain a model with high accuracy and strong generalization through less data training. This article first divides the meta-learning model into five research directions based on different principles of use. They are model-based, metric-based, optimization-based, online-learning-based, or stacked ensemble-based. Then, the current problems in the field of cyberspace security are categorized into three branches: cyber security, information security, and artificial intelligence security according to different perspectives. Then, the application research results of various meta-learning models on these three branches are reviewed. At the same time, based on the characteristics of strong generalization, evolution, and scalability of meta-learning, we contrast and summarize its advantages in solving problems. Finally, the prospect of future deep application of meta-learning in the field of cyberspace security is summarized.