Network Intrusion Detection Model Using Fused Machine Learning Technique

作     者：Fahad Mazaed Alotaibi 

作者机构：Faculty of Computing and Information Technology in Rabigh(FCITR)King Abdulaziz UniversityJeddahSaudi Arabia 

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

年 卷 期：2023年第75卷第5期

页      面：2479-2490页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 0839[工学-网络空间安全] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This project was funded(grant no.G:432-611-1443)by the Deanship of Scientific Research(DSR)at King Abdulaziz University(KAU) Jeddah Saudi Arabia 

主　　题：Cyberattack machine learning prediction solution intrusion detection 

摘      要：With the progress of advanced technology in the industrial revolution encompassing the Internet of Things(IoT)and cloud computing,cyberattacks have been increasing rapidly on a large *** rapid expansion of IoT and networks in many forms generates massive volumes of data,which are vulnerable to security *** a result,cyberattacks have become a prevalent and danger to society,including its infrastructures,economy,and citizens’privacy,and pose a national security risk ***,cyber security has become an increasingly important issue across all levels and *** progress is being made in developing more sophisticated and efficient intrusion detection and defensive *** the scale of complexity of the cyber-universe is increasing,advanced machine learning methods are the most appropriate solutions for predicting cyber *** this study,a fused machine learning-based intelligent model is proposed to detect intrusion in the early stage and thus secure networks from harmful *** results confirm the effectiveness of the proposed intrusion detection model,with 0.909 accuracy and a miss rate of 0.091.