IMAGE-BASED IOT MALWARE DETECTION USING CHI-SQUARE AND CNN

The Internet of Things (IoT) constitutes an expanding network of interconnected gadgets that enable intelligent systems to gather, analyze, and disseminate data. However, this rapid growth raises cyber-attack risks due to poor configurations and outdated systems. Malware, which exploits system vulnerabilities, represents a significant threat to the information security of IoT systems. Thus, malware detection in IoT systems is a critical concern. Therefore, this research paper presents an IoT malware detection method based on an image dataset and the Chi-square method as well as applying the Convolutional Neural Network (CNN) deep learning model to detect the IoT malware. This study attempts to investigate the impact of the chi-square Feature Selection (FS) method on the effectiveness of CNNs for identifying IoT malware, by directly applying feature selection to the images to discern the most informative ones from the dataset before passing them to the CNN deep learning model, demonstrating robust outcomes and validating the efficacy and robustness of the suggested approach for identifying IoT malware. An experimental comparison was carried out between the suggested method that Involved training the CNN on the feature-selected dataset (FS+CNN Model) and the (CNN Model) that was trained on the full dataset and was also evaluated using the presented state-of-the-art to add to the method’s reliability. The accuracy of the (Fs + CNN Model) reached 98.19% while its precision, recall, and F1-score were 99.52%, 95.90 %, and 97.68 %, respectively, outperforming the CNN Model’s accuracy with 94.75 %, precision with 93.00 %, recall with 91.43 % and f1-score with 90.43 %. It also outperformed the state-of-the-art evaluation with an accuracy value of 97.93 %, a precision value of 98.64 %, a recall value of 88.73 %, and an f1-score value of 93.94 %.