Nesnelerin İnterneti Cihazlarına Karşı Yapılan Makine Öğrenmesi Saldırıları

Extended Abstract As the number of Internet of Things (IoT) devices increases day by day, attacks against these devices are also increasing. In this study, methods of ensuring security in IoT devices and attacks on IoT devices are discussed, and the importance of zero-trust architecture in ensuring IoT security is explained. In addition, the defense rates of padding methods against machine learning used by the attacker are shown and the defense methods used with machine learning techniques are explained. For this purpose, machine learning methods that are effective on attacks, attacks and violations that are achieved by machine learning techniques are specified. In addition, the effectiveness of machine learning techniques in classifying IoT devices in encrypted traffic is examined. The effectiveness of Random Forest and Decision Tree classification algorithms in classifying IoT devices are evaluated. Finally, experiments are carried out for commonly used attack and defense methods. For this purpose, the accuracy rates of the padded and unpadded experiments are compared by analyzing the IoT device traffic. When classifying unpadded data, 84% accuracy rate of IoT devices is achieved, while this accuracy rate has been reduced to 19% with the random padding method that aims to reduce the attacker's rate of accessing correct information.