DDoS Attack Detection Based on Simple ANN with SMOTE for IoT Environment

As the IoT era is rapidly developed in recent years, the attackers are mostly targeting IoT environments. They boosted the IoT devices as the bots to attack the target organization, and these devices are easily infected by IoT malware due to their resource constraint to process the powerful security mechanism on these devices. One of very dangerous IoT malware, like Mirai, launched DDoS attacks to the targeted organization via infected IoT devices. Even though many security mechanisms were implemented for IoT devices, it is still needed to get an effective detection system for IoT environments. Our detection system uses the public dataset to detect that kind of attack using machine learning technique, simple architecture with Artificial Neural Network (ANN). Although we used the modern botnet attack dataset, Bot-IoT to detect the DDoS attack, it is needed to overcome one important issue, like imbalance data problem because this dataset has a small amount of benign data and large amount attack data. We used the SMOTE (Synthetic Minority Over-sampling Technique) for solving imbalance data problem to implement a machine learning-based DDoS detection system. Our results indicate that the proposed approach can effectively detect the DDoS attack for IoT environment.