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The popularity of wireless sensor networks (WSNs) increases as the usage areas and the number of integrated systems increase, and this situation attracts the attention of attackers. Attackers carry out attacks aimed at infiltrating, capturing, and manipulating the network. These attacks are implemented differently according to the layers. After these attacks on sensor networks, network traffic data is examined and malicious traffic and node behaviors are analyzed to prevent possible future attacks. The raw data received from the network are made usable by learning models by some preprocessing. The data analyzed with the models are categorized according to the network traffic types and the attacks carried out on the network are detected. In WSN, attack detections made with learning models are performed with high-accuracy percentages compared to classical detection methods. In this study, blackhole, flooding …