Research Area:  Machine Learning

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 the 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 respectively 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.

Keywords:  
Meta-Learning
Cyberspace Security
one-shot learning
few-shot learning
zero-shot learning
machine learning

Author(s) Name:  Aimin Yang, Chaomeng Lu, Jie Li, Xiangdong Huang, Tianhao Ji, Xichang Li, Yichao Sheng

Publisher name:  Elsevier

DOI:  10.1016/j.dcan.2022.03.007

Volume Information:  

Paper Link:   https://www.sciencedirect.com/science/article/pii/S2352864822000281