Open AccessResearch on Intrusion Detection Method of Industrial Internet Based on Machine Learning
Author(s) -
Yanfa Xu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1802/4/042029
Subject(s) - computer science , intrusion detection system , mobile ad hoc network , machine learning , artificial intelligence , network packet , network security , attack model , support vector machine , perceptron , the internet , data mining , computer security , artificial neural network , world wide web
The mobile Adhoc network (MANET) is being used more and more widely, and the related network security issues have also begun to receive widespread attention. Researching the MANET network’s possible attack methods, the paper proposes an intrusion detection performance evaluation model based on machine learning technology and proposes a comprehensive evaluation index. It compares seven machine learning algorithms’ performance in MANET network intrusion detection, sufficient for building security. The MANET network is of great significance. Use the GloMoSim simulation tool to simulate the MANET network’s normal behavior and the three intrusions of black hole, flood, and packet loss, and analyze the performance of seven machine learning algorithms in various attack situations in various attack situations detail. Our analysis results show that the evaluation model can better reflect the performance of various machine learning algorithms. Multilayer perceptrons, logistic regression, and support vector machines have higher detection rates and lower false alarm rates.