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.

翻译：入侵检测系统（IDS）在传统计算系统中对检测和防范网络攻击发挥了重要作用。该技术现被应用于保护物联网（IoT）网络免受网络威胁，这并不令人意外。然而，物联网设备有限的计算资源对部署基于传统计算的入侵检测系统构成了挑战。为物联网环境设计的入侵检测系统必须具备高分类性能，并采用低复杂度模型。物联网领域的入侵检测模型开发已取得显著进展，但在高分类性能与低复杂度之间实现平衡仍是一项艰巨任务。本研究提出一种有效的入侵检测系统模型，通过将轻量级卷积神经网络（CNN）与双向长短期记忆网络（BiLSTM）相结合来解决该问题。此外，我们采用特征选择技术来减少输入模型的特征数量，从而降低其复杂度。该方法使所提模型非常适合资源受限的物联网设备，确保其满足计算能力要求。创建既满足物联网设备需求又实现更高精度的模型是一项挑战性任务。然而，我们提出的模型在二元分类任务中以1.1秒的预测时间达到97.90%的显著准确率，在多分类任务中以2.10秒的预测时间达到97.09%的准确率，其性能优于文献中的已有工作。