摘要
为应对大数据技术迅猛发展带来的网络安全隐患,本文提出了一种基于人工智能技术的网络安全防御系统下文简称系统。系统设计采用分层式的架构体系,主要分为数据采集层、数据处理层、入侵检测层和防御决策层。通过深度学习模型进行入侵检测,用强化学习算法动态构建防御策略。经实验证实,本系统与传统入侵检测系统(IDS)相比,在检测比率、响应时长和误报比例方面呈现显著优势。本系统可高效辨别未知攻击,按照威胁类型及其严重性自动调整防御策略,提高了网络安全防御的综合能力。该研究为大数据环境下的智能网络安全防御提供了新的思路和方法。
To address network security challenges arising from the rapid expansion of big data technologies,this paper proposes an artificial intelligence(AI)-based defense system.The system employs a layered architecture comprising data acquisition,processing,intrusion detection,and defense decision-making layers.Deep learning models facilitate intrusion detection,while reinforcement learning algorithms dynamically formulate defense strategies.Experimental evaluations demonstrate that the proposed AI system surpasses traditional Intrusion Detection Systems(IDS)in detection rate,response time,and false positive rate.Practical verification confirms the system's capability to identify unknown attacks and autonomously adjust defense policies based on threat type and severity,thereby enhancing comprehensive network security defense.This research offers novel insights and methodologies for intelligent security defense in big data environments.
作者
关瑞佳
Guan Ruijia(The Hong Kong Polytechnic University,Hong Kong,100872,China)
出处
《北斗与空间信息应用技术》
2025年第6期13-15,共3页
Beidou and Spatial Information Application Technology
关键词
大数据
网络安全
人工智能
入侵检测
深度学习
系统设计
Big data
Network security
Artificial intelligence
Intrusion detection
Deep learning
System design
