Mert NakipErol GelenbeNakip, MertGelenbe, Erol2025-10-062021978-1-7281-8104-297817281810422334-09832576-681310.1109/GLOBECOM46510.2021.9685306http://dx.doi.org/10.1109/GLOBECOM46510.2021.9685306https://gcris.yasar.edu.tr/handle/123456789/7394https://doi.org/10.1109/GLOBECOM46510.2021.9685306Internet connected IoT devices have often been particularly vulnerable to Botnet attacks of the Mirai family in recent years. Thus we develop an attack detection scheme for Mirai Botnets using the Auto-Associative Dense Random Neural Network that has recently been successful for other attacks such as the SYN attack. The resulting method is trained with normal traffic and tested with attack traffic and shown to result in high accuracy detection of attacks with low false alarms. The approach is compared on the same data set with two other common Machine learning methods (Lasso and KNN) and shown to have higher accuracy and much lower computation times than KNN and slightly higher (but comparable) computation times with respect to Lasso.Englishinfo:eu-repo/semantics/openAccessMirai Botnet Attacks, Attack Detection, Auto-Associative Dense Random Neural Networks, Machine LearningVIDEO QUALITYAttack DetectionAuto-Associative Dense Random Neural NetworksMirai Botnet AttacksMachine LearningConference Object