Design of the network intrusion detection systems for the internet of things infrastructure using machine learning algorithms
Design of the network intrusion detection systems for the internet of things infrastructure using machine learning algorithms
复制标题
使用机器学习算法设计物联网基础设施网络入侵检测系统
DOI：
10.1117/12.2584499
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Sharifi, Safura
中科院分区：
文献类型：
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作者：
Banadaki, Yaser M.;Brook, Jalen;Sharifi, Safura
物联网（IoT）基础设施的网络入侵检测系统（NIDS）是确保网络免受恶意网络攻击的最关键工具之一。本文采用了四种机器学习算法，并评估其性能在NIDS考虑的准确率，精度，召回率和F-得分。使用CICIDS 2017数据集进行的比较分析显示，Boosted机器学习技术在检测网络攻击方面的表现优于其他算法，预测准确率达到99%以上。这种基于ML的攻击检测器还具有F1得分、精确度和召回率的最大加权度量。这些结果有助于网络工程师选择最有效的基于机器学习的NIDS，以确保当今不断增长的物联网网络流量的网络安全。
Network intrusion detection systems (NIDS) for Internet-of-Things (IoT) infrastructure are among the most critical tools to ensure the protection and security of networks against malicious cyberattacks. This paper employs four machine learning algorithms and evaluates their performance in NIDS considering the accuracy, precision, recall, and F-score. The comparative analysis conducted using the CICIDS2017 dataset reveals that the Boosted machine learning techniques perform better than the other algorithms reaching the predicted accuracy of above 99% in detecting cyberattacks. Such ML-based attack detectors also have the largest weighted metrics of F1-score, precision, and recall. The results assist the network engineers in choosing the most effective machine learning-based NIDS to ensure network security for today’s growing IoT network traffic.