Deep Learning-Based Hybrid Intelligent Intrusion Detection System

作     者：Muhammad Ashfaq Khan Yangwoo Kim 

作者机构：Department of Informationand Communication EngineeringDongguk UniversitySeoul100-715Korea Department of Electronics EngineeringIoT and Big-Data Research CenterIncheon National UniversityIncheonKorea 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第68卷第7期

页      面：671-687页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the MSIT(Ministry of Science,ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2020-2016-0-00465) supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation) 

主　　题：Machine learning intrusion detection system deep learning spark MLlib LSTM big data 

摘      要：Machine learning(ML)algorithms are often used to design effective intrusion detection(ID)systems for appropriate mitigation and effective detection of malicious cyber threats at the host and network ***,cybersecurity attacks are still *** ID system can play a vital role in detecting such *** ID systems are unable to detect malicious threats,primarily because they adopt approaches that are based on traditional ML techniques,which are less concerned with the accurate classication and feature ***,developing an accurate and intelligent ID system is a *** main objective of this study was to develop a hybrid intelligent intrusion detection system(HIIDS)to learn crucial features representation efciently and automatically from massive unlabeled raw network trafc *** ID datasets are publicly available to the cybersecurity research *** such,we used a spark MLlib(machine learning library)-based robust classier,such as logistic regression(LR),extreme gradient boosting(XGB)was used for anomaly detection,and a state-of-the-art DL,such as a long short-term memory autoencoder(LSTMAE)for misuse attack was used to develop an efcient and HIIDS to detect and classify unpredictable *** approach utilized LSTM to detect temporal features and an AE to more efciently detect global ***,to evaluate the efcacy of our proposed approach,experiments were conducted on a publicly existing dataset,the contemporary real-life ISCX-UNB *** simulation results demonstrate that our proposed spark MLlib and LSTMAE-based HIIDS signicantly outperformed existing ID approaches,achieving a high accuracy rate of up to 97.52%for the ISCX-UNB dataset respectively 10-fold crossvalidation *** is quite promising to use our proposed HIIDS in real-world circumstances on a large-scale.