The IoT (Internet of Things) devices extend the attack surface for cybercriminals, requiring robust IDS (Intrusion Detection Systems). In order to tackle the issues, the AI (Artificial Intelligence), especially the ML (Machine Learning) and DL (Deep Learning) is incorporated into IoT IDS to analyze large datasets, identify complex patterns, and adapt to evolving threats. Hence, the study proposes a modified CNN-LSTM model for real-time intrusion detection and classification in IoT Device Network Logs. The proposed model utilizes the CNNs (Convolutional Neural Networks) for spatial feature extraction and LSTM (Long Short-Term Memory) networks for capturing temporal dependencies, augmenting the accuracy and detection efficiency. The proposed CNN-LSTM model enhances the real-time intrusion detection and contains the ability to detect developing attacks in dynamic IoT environments, which makes it highly flexible for large-scale deployments. The IoT Device Network Logs dataset is used for evaluating the proposed modified CNN-LSTM model. The modified CNN-LSTM model is assessed using the performance metrics such as accuracy, precision, recall, F1-score, time complexity and false alarm rate. As a result, the modified CNN-LSTM model achieves superior performance and earlier detection in contrast to the conventional models deliberating its potential for enhancing IoT security.