过度拟合
计算机科学
可靠性
入侵检测系统
人工智能
机器学习
异常检测
特征选择
数据挖掘
网络安全
管道(软件)
深度学习
人工神经网络
计算机安全
软件工程
程序设计语言
作者
Md. Alamin Talukder,Khondokar Fida Hasan,Md. Manowarul Islam,Md. Ashraf Uddin,Arnisha Akhter,Mohammad Abu Yousuf,Fares Alharbi,Mohammad Ali Moni
标识
DOI:10.1016/j.jisa.2022.103405
摘要
Network intrusion detection systems (NIDSs) play an important role in computer network security. There are several detection mechanisms where anomaly-based automated detection outperforms others significantly. Amid the sophistication and growing number of attacks, dealing with large amounts of data is a recognized issue in the development of anomaly-based NIDS. However, do current models meet the needs of today's networks in terms of required accuracy and dependability? In this research, we propose a new hybrid model that combines machine learning and deep learning to increase detection rates while securing dependability. Our proposed method ensures efficient pre-processing by combining SMOTE for data balancing and XGBoost for feature selection. We compared our developed method to various machine learning and deep learning algorithms to find a more efficient algorithm to implement in the pipeline. Furthermore, we chose the most effective model for network intrusion based on a set of benchmarked performance analysis criteria. Our method produces excellent results when tested on two datasets, KDDCUP'99 and CIC-MalMem-2022, with an accuracy of 99.99% and 100% for KDDCUP'99 and CIC-MalMem-2022, respectively, and no overfitting or Type-1 and Type-2 issues.
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