  Intrusion Detection Systems (IDSs) have played a significant role in
detecting and preventing cyber-attacks within traditional computing systems. It
is not surprising that the same technology is being applied to secure Internet
of Things (IoT) networks from cyber threats. The limited computational
resources available on IoT devices make it challenging to deploy conventional
computing-based IDSs. The IDSs designed for IoT environments must also
demonstrate high classification performance, utilize low-complexity models, and
be of a small size. Despite significant progress in IoT-based intrusion
detection, developing models that both achieve high classification performance
and maintain reduced complexity remains challenging. In this study, we propose
a hybrid CNN architecture composed of a lightweight CNN and bidirectional LSTM
(BiLSTM) to enhance the performance of IDS on the UNSW-NB15 dataset. The
proposed model is specifically designed to run onboard resource-constrained IoT
devices and meet their computation capability requirements. Despite the
complexity of designing a model that fits the requirements of IoT devices and
achieves higher accuracy, our proposed model outperforms the existing research
efforts in the literature by achieving an accuracy of 97.28\% for binary
classification and 96.91\% for multiclassification.
