入侵检测系统
计算机科学
机器学习
网络安全
人工智能
互联网
入侵防御系统
入侵
算法
数据挖掘
计算机安全
万维网
地球化学
地质学
作者
Sandy Victor Amanoul,Adnan Mohsin Abdulazeez
标识
DOI:10.1109/cspa55076.2022.9782043
摘要
Due to the widespread use of the internet, computer networks are vulnerable to cyber-attacks, prompting various researchers to suggest intrusion detection systems (IDSs). Detecting intrusions is one of the important research topics in network security. As a precaution to guarantee the network's security, it aids in the detection of unwanted usage and assaults. This study summarizes significant literature reviews on machine learning (ML), and deep learning (DL) approaches for intrusion detection network analysis and includes a brief instructional overview of each ML / DL method. The papers describing each approach were indexed and summarized according to the dataset used, and the highest level of accuracy was attained.
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