An intelligent deep representation learning with enhanced feature selection approach for cyberattack detection in internet of things enabled cloud environment.

Users of computer networks can take advantage of cloud computing (CC), a relatively new concept that provides features such as processing, in addition to storing and sharing data. Cloud computing (CC) is attracting global investment due to its services, while IoT faces rising advanced cyberattacks, making its cybersecurity crucial to protect privacy and digital assets. A significant challenge for intrusion detection systems (IDS) is detecting complex and hidden malware, as attackers use advanced evasion techniques to bypass conventional security measures. At the cutting edge of cybersecurity is artificial intelligence (AI), which is applied to develop composite models that protect systems and networks, including Internet of Things (IoT) systems. AI-based deep learning (DL) is highly effective in detecting cybersecurity threats. This paper presents an Intelligent Hybrid Deep Learning Method for Cyber Attack Detection Using an Enhanced Feature Selection Technique (IHDLM-CADEFST) approach in IoT-enabled cloud networks. The aim is to strengthen IoT cybersecurity by identifying key threats and developing effective detection and mitigation strategies. Initially, the data pre-processing phase uses the standard scaler method to convert input data into a suitable format. Furthermore, the feature selection (FS) strategy is implemented using the recursive feature elimination with information gain (RFE-IG) model to detect the most pertinent features and prevent overfitting. Finally, a hybrid Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) model is employed for attack classification, utilizing the RMSprop optimizer to enhance the performance and efficiency of the classification process. The experimentation of the IHDLM-CADEFST approach is examined under the ToN-IoT and Edge-IIoT datasets. The comparison analysis of the IHDLM-CADEFST approach yielded superior accuracy values of 99.45% and 99.19% compared to recent models on the dual dataset.