Enhancement performance of random forest algorithm via one hot encoding for IoT IDS
Enhancement performance of random forest algorithm via one hot encoding for IoT IDS
复制标题
通过 IoT IDS 的一种热编码增强随机森林算法的性能
DOI：
--
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
A. Sadiq
中科院分区：
文献类型：
--
作者：
Adil Yousef Hussein;P. Falcarin;A. Sadiq
随机森林算法是重要的监督机器学习（ML）算法之一。在本文中，通过使用One Hot Encoding方法提高了随机森林（RF）算法结果的准确性。入侵检测系统 (IDS) 可以定义为可以预测网络流量中的安全漏洞且位于网络基础设施范围之外的系统。它不会影响内置网络的效率，因为它会分析内置流量的副本，并通过发出警报将结果报告给管理员。然而，由于IDS只是一个侦听系统，它无法采取自动行动来防止检测到的攻击或安全漏洞感染系统，它提供有关开始侵入的源地址、目标地址和可疑攻击类型的信息。 IoTID20 数据集用于验证改进的算法，该数据集具有三个目标，将所提出的系统与最先进的方法进行比较，并显示出优于它们的优越性。
The random forest algorithm is one of important supervised machine learning (ML) algorithms. In the present paper, the accuracy of the results of the random forest (RF) algorithm has been improved by the use of the One Hot Encoding method. The Intrusion Detection System (IDS) can be defined as a system that can predict security vulnerabilities within network traffic and is located out of range on a network infrastructure. It does not affect the efficiency of the built-in network because it analyzes a copy of the built-in traffic flow and reports results to the administrator by giving alerts. However, since IDS is a listening system only, it cannot take automatic action to prevent an attack or security vulnerability detected from infecting the system, it provides information about the source address to start the break-in, the address of the target and the type of suspected attack. The IoTID20 dataset is used to verify the improved algorithm, where this dataset is having three targets, the proposed system is compared with the state-of-art approaches and shows superiority over them.