基于机器学习的网络入侵检测技术综述
Overview of network intrusion detection technology based on machine learning
张茜 1王晓菲 1王亚洲 1尚颖 1王芳鸣 1曾颖明1
作者信息
- 1. 北京计算机技术及应用研究所,北京 100854
- 折叠
摘要
新兴技术的发展推动了机器学习等智能化方法在网络入侵检测的广泛应用,有效提高了入侵检测的效率和准确率,然而基于机器学习的网络入侵检测领域仍然面临着大规模网络数据处理难、数据样本不平衡、未知威胁难以有效检测、模型泛化能力差等挑战.文章对基于机器学习的网络入侵检测技术进行综述和总结,对比和分析当前主流方法的优势和局限性,并总结和讨论该领域目前挑战和未来展望,以便为该领域人员了解最新研究动态提供借鉴参考.
Abstract
The development of emerging technologies has promoted the wide application of intelligent methods such as machine learning in the field of network intrusion detection,and effectively improved the efficiency and accuracy of intrusion detection.However,the field of network intrusion detection based on machine learning still faces challenges such as difficulty in processing large-scale network data,imbalance of data samples,difficulty in effectively detecting unknown threats,and poor generalization a-bility of models.This paper aims to summarize the network intrusion detection technology based on machine learning,compare and analyze the advantages and limitations of the current mainstream methods,and summarize and discuss the current challenges and future prospects in this field,so as to provide reference for people in this field to understand the latest research trends.
关键词
机器学习/入侵检测/智能化Key words
machine learning/intrusion detection/intelligence引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据