The rapid growth of Internet of Things (IoT) networks, fuelled by advancements in Low-Power Wide-Area Networks (LPWANs) and 5G technologies, has transformed industries such as healthcare, smart cities, and manufacturing. However, this expansion has also exposed IoT systems to cybersecurity vulnerabilities, making them prime targets for network intrusions and cyberattacks. Addressing these threats requires effective Intrusion Detection Systems (IDS) capable of identifying and classifying malicious traffic patterns. This paper proposes a hybrid IDS framework that integrates a 1D Convolutional Neural Network (1D CNN) and a Bidirectional Long Short-Term Memory (BiLSTM) model. The 1D CNN serves as a feature extractor, capturing spatial patterns in network traffic, while the BiLSTM leverages temporal dependencies in both forward and backward directions to enhance classification accuracy. Experiments assess the model’s performance in both binary and multi-class classification tasks. The results demonstrate that the 1D CNN+BiLSTM outperforms traditional methods, including SVM, XGBoost, and CNN+LSTM, achieving the highest accuracy (95.03%), recall (94.80%), and F1-score (94.90%). These findings highlight the model’s ability to minimize false positives and false negatives, making it highly suitable for real-time intrusion detection in IoT environments.