Application of meta-learning in cyberspace security: a survey

Abstract

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.