  Protecting Internet of things (IoT) devices against cyber attacks is
imperative owing to inherent security vulnerabilities. These vulnerabilities
can include a spectrum of sophisticated attacks that pose significant damage to
both individuals and organizations. Employing robust security measures like
intrusion detection systems (IDSs) is essential to solve these problems and
protect IoT systems from such attacks. In this context, our proposed IDS model
consists on a combination of convolutional neural network (CNN) and long
short-term memory (LSTM) deep learning (DL) models. This fusion facilitates the
detection and classification of IoT traffic into binary categories, benign and
malicious activities by leveraging the spatial feature extraction capabilities
of CNN for pattern recognition and the sequential memory retention of LSTM for
discerning complex temporal dependencies in achieving enhanced accuracy and
efficiency. In assessing the performance of our proposed model, the authors
employed the new CICIoT2023 dataset for both training and final testing, while
further validating the model's performance through a conclusive testing phase
utilizing the CICIDS2017 dataset. Our proposed model achieves an accuracy rate
of 98.42%, accompanied by a minimal loss of 0.0275. False positive rate(FPR) is
equally important, reaching 9.17% with an F1-score of 98.57%. These results
demonstrate the effectiveness of our proposed CNN-LSTM IDS model in fortifying
IoT environments against potential cyber threats.
