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An effective genetic algorithm-based feature selection method for intrusion detection systems

机译：基于有效的入侵检测系统的基于遗传算法的特征选择方法

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摘要

Availability of suitable and validated data is a key issue in multiple domains for implementing machine learning methods. Higher data dimensionality has adverse effects on the learning algorithm's performance. This work aims to design a method that preserves most of the unique information related to the data with minimum number of features. Addressing the feature selection problem in the domain of network security and intrusion detection, this work contributes an enhanced Genetic Algorithm (GA)-based feature selection method, named as GA-based Feature Selection (GbFS), to increase the classifiers' accuracy. Securing a network from the cyber-attacks is a critical task and needs to be strengthened. Machine learning, due to its proven results, is widely used in developing firewalls and Intrusion Detection Systems (IDSs) to identify new kinds of attacks. Utilizing machine learning algorithms, IDSs are able to detect the intruder by analyzing the network traffic passing through it. This work presents parameter tuning for the GA-based feature selection along with a novel fitness function. The present work develops an enhanced GA-based feature selection method which is tested over three benchmark network traffic datasets, namely, CIRA-CIC-DOHBrw-2020, UNSW-NB15, and Bot-IoT. A comparison is also performed with the standard feature selection methods. Results show that the accuracies improve using GbFS by achieving a maximum accuracy of 99.80%.
机译：合适和验证数据的可用性是用于实现机器学习方法的多个域中的关键问题。更高的数据维度对学习算法的性能产生不利影响。这项工作旨在设计一种方法，该方法保留与具有最小特征数量的数据相关的唯一信息。解决网络安全性和入侵检测域中的特征选择问题，这项工作有助于增强的遗传算法（GA）基于特征选择方法，命名为基于GA的特征选择（GBF），以增加分类器的准确性。从网络攻击中保护网络是一个关键任务，需要加强。由于其经过验证的结果，机器学习广泛用于开发防火墙和入侵检测系统（IDS）来识别新的攻击。利用机器学习算法，IDS能够通过分析通过它的网络流量来检测入侵者。本工作介绍了基于GA的特征选择以及新颖的健身功能的参数调整。目前的工作开发了一种增强的GA基特征选择方法，该方法在三个基准网络流量数据集中测试，即CiC-CIC-DoHBRW-2020，UNSW-NB15和BOT-IOT。使用标准特征选择方法还执行比较。结果表明，通过达到99.80％的最大精度，使用GBF的准确性改善。

著录项

  • 来源
    《Computers & Security》 |2021年第11期|102448.1-102448.20|共20页
  • 作者

    Zahid Halim; Muhammad Nadeem Yousaf; Muhammad Waqas; Muhammad Sulaiman; Ghulam Abbas; Masroor Hussain; Iftekhar Ahmad; Muhammad Hanif;

  • 作者单位

    Machine Intelligence Research Group (MInC) Faculty of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Topi 23460 Pakistan;

    Machine Intelligence Research Group (MInC) Faculty of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Topi 23460 Pakistan;

    Telecommunications and Networking (TeleCoN) Research Lab Faculty of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Topi 23460 Pakistan Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information Technology Beijing University of Technology Beijing 100124 People's Republic of China;

    Machine Intelligence Research Group (MInC) Faculty of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Topi 23460 Pakistan Department of Computer Science Capital University of Science and Technology Islamabad Pakistan;

    Telecommunications and Networking (TeleCoN) Research Lab Faculty of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Topi 23460 Pakistan;

    Machine Intelligence Research Group (MInC) Faculty of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Topi 23460 Pakistan;

    School of Engineering Edith Cowan Uniuersity Joondalup WA 6027 Australia;

    Machine Intelligence Research Group (MInC) Faculty of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Topi 23460 Pakistan;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Feature selection; Genetic algorithm; Intrusion detection; Machine learning; Data analysis;

    机译：功能选择;遗传算法;入侵检测;机器学习;数据分析;

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