Machine Learning Based Intrusion Detection for IoT Botnet
Sikha Bagui, Xiaojian Wang, Subhash Bagui
Abstract
In this article, we analyzed botnet traffic in an IoT environment using three machine learning classifiers: Logistic Regression, Support-Vector Machine and Random Forest. We classified each attack in each botnet for nine devices. We calculated the Accuracy, True Positive, False Positive, False Negative, True Negative, Precision, Recall, F1 score for each algorithm. We obtained impressive results (above 99%) using these three classifiers. We have a high attack detection rate. A brief analysis of the results is presented.
Topics & Concepts
BotnetComputer scienceRandom forestSupport vector machineArtificial intelligenceMachine learningLogistic regressionIntrusion detection systemInternet of ThingsFalse positive rateF1 scoreIntrusionPrecision and recallComputer securityThe InternetOperating systemGeochemistryGeologyNetwork Security and Intrusion DetectionInternet Traffic Analysis and Secure E-votingAdvanced Malware Detection Techniques