  Intrusion Detection Systems (IDSs) have played a significant role in the
detection and prevention of cyber-attacks in traditional computing systems. It
is not surprising that this technology is now being applied to secure Internet
of Things (IoT) networks against cyber threats. However, the limited
computational resources available on IoT devices pose a challenge for deploying
conventional computing-based IDSs. IDSs designed for IoT environments must
demonstrate high classification performance, and utilize low-complexity models.
Developing intrusion detection models in the field of IoT has seen significant
advancements. However, achieving a balance between high classification
performance and reduced complexity remains a challenging endeavor. In this
research, we present an effective IDS model that addresses this issue by
combining a lightweight Convolutional Neural Network (CNN) with bidirectional
Long Short-Term Memory (BiLSTM). Additionally, we employ feature selection
techniques to minimize the number of features inputted into the model, thereby
reducing its complexity. This approach renders the proposed model highly
suitable for resource-constrained IoT devices, ensuring it meets their
computation capability requirements. Creating a model that meets the demands of
IoT devices and attains enhanced precision is a challenging task. However, our
suggested model outperforms previous works in the literature by attaining a
remarkable accuracy rate of 97.90% within a prediction time of 1.1 seconds for
binary classification. Furthermore, it achieves an accuracy rate of 97.09%
within a prediction time of 2.10 seconds for multiclassification.
