大数据环境下入侵检测系统概述

A Survey of Intrusion Detection System in Big Data

入侵检测系统(Intrusion Detection System,IDS)为网络空间安全做出重大贡献。然而随着大数据时代的到来,IDS暴露出效率低下、理念落后等系统性不足。本文结合大数据特征及传统IDS技术的不足,针对性地概述了分布式入侵检测系统(Districted Intrusion Detection System,DIDS),并在基本概念、系统分类和性能特点等方面对其做出重点解释。最后从深度学习、广度融合等角度展望了入侵检测技术的未来发展。

Intrusion detection system has made a great contribution for cyberspace security. However, with the approach of the age of big data, IDS has exposed certain structural defects, such as inefficiency and conservative ideas. Combining with the characteristic of big data and traditional IDS techniques, this paper provides a survey of distributed intrusion detection system (DIDS) and makes detailed explanations on concepts, classifications and performance. The paper also prospects the development of IDS from the perspective of deep learning, extensive integration, etc.