Intrusion Detection for IoT Network Security with Deep Neural Network

The number of Internet of Things devices are increasing as we are demanding more smart devices over time. These devices are well known for having weaker security measures against cyber-attacks, specifically Distributed Denial of Service attack. Distributed Denial of Service attacks has caused significant damages to many IoT networks. Hence, it is crucial to detect such attacks. In this paper, we presented a deep neural network-based intrusion detection model to detect Distributed Denial of Service attacks along with few other cyber-attacks, using the CICIDS-2017 dataset. Additionally, we explored effective deep learning models for demonstrating cybersecurity knowledge in Internet of Things networks, including DenseNet, CNN, and a hybrid model of CNN and LSTM.