摘要
现阶段关于已知网络安全指令已经形成了较为成熟完善的检测技术,但是对于未知网络安全指令的检测尚未成熟,在一定程度上影响了网络安全检测的可靠性。因此,提出了基于机器学习的网络安全检测技术。该技术通过有效结合监督学习和非监督学习,全面且安全地检测网络安全指令,同时应用随机森林算法分析优化网络特征,在保障网络安全检测准确性的基础上提升网络安全检测效率。实验分析结果显示,设计系统对于已知和未知网络攻击均具有较高的检测精度。
At this stage, a relatively mature and perfect detection technology has been formed for known network security instructions, but the detection of unknown network security instructions is not yet mature, which affects the reliability of network security detection to a certain extent. This paper proposes a network security detection technology based on machine learning, which can realize the comprehensive security detection of network security instructions through the effective combination of supervised learning and unsupervised learning;At the same time, the technology uses random forest algorithm to analyze and optimize network characteristics, which can significantly improve the efficiency of network security detection on the basis of ensuring the accuracy of network security detection. In this paper, the experimental analysis of this technology shows that it has high detection accuracy for both known and unknown network attacks.
作者
汤亮
TANG Liang(Hunan Electronic Information Industry Institute,Changsha Hunan 430001,China)
出处
《信息与电脑》
2023年第1期226-228,241,共4页
Information & Computer
关键词
机器学习
网络安全
检测技术
machine learning
network security
detection technology
