  Internet of Things (IoT) has become a popular paradigm to fulfil needs of the
industry such as asset tracking, resource monitoring and automation. As
security mechanisms are often neglected during the deployment of IoT devices,
they are more easily attacked by complicated and large volume intrusion attacks
using advanced techniques. Artificial Intelligence (AI) has been used by the
cyber security community in the past decade to automatically identify such
attacks. However, deep learning methods have yet to be extensively explored for
Intrusion Detection Systems (IDS) specifically for IoT. Most recent works are
based on time sequential models like LSTM and there is short of research in
CNNs as they are not naturally suited for this problem. In this article, we
propose a novel solution to the intrusion attacks against IoT devices using
CNNs. The data is encoded as the convolutional operations to capture the
patterns from the sensors data along time that are useful for attacks detection
by CNNs. The proposed method is integrated with two classical CNNs: ResNet and
EfficientNet, where the detection performance is evaluated. The experimental
results show significant improvement in both true positive rate and false
positive rate compared to the baseline using LSTM.
