01064nas a2200121   4500000000100000000000100001008004100002260002700043100001500070700001700085245006100102520077900163       2022                            d  bSpringeraNice, France1 aMert Nakip1 aErol Gelenbe00aBotnet Attack Detection with Incremental Online Learning3 a<p>In recent years, IoT devices have often been the target of Mirai Botnet attacks. This paper develops an intrusion detection method based on Auto-Associated Dense Random Neural Network with incremental online learning, targeting the detection of Mirai Botnet attacks. The proposed method is trained only on benign IoT traffic while the IoT network is online; therefore, it does not require any data collection on benign or attack traffic. Experimental results on a publicly available dataset have shown that the performance of this method is considerably high and very close to that of the same neural network model with offline training. In addition, both the training and execution times of the proposed method are highly acceptable for real-time attack detection.</p>