Deep learning with leagues championship algorithm based intrusion detection on cybersecurity driven industrial IoT systems

The Internet of Things (IoT) presents significant advantages to day-to-day life across a wide range of application domains, including healthcare automation, transportation, and smart environments. However, owing to the constraints of limited resources and computation abilities, IoT networks are subject to different cyber-attacks. Incorporating IDS into the cybersecurity-driven IIoT process contains cautious deployment, planning, and progressing management. Cybersecurity is crucial for the protection of sensitive data, safeguarding the privacy of users, and securing important substructures from malicious activities attempting unauthorized access or triggering interferences. Cyberattack detection performs a vital role in this defense scheme, employing advanced technologies like deep learning (DL) for analysing digital activities in real time. With the help of recognizing and responding to possible cyber-attacks quickly, cyberattack detection not only mitigates risks but reinforces the overall flexibility of the digital ecosystem against developing security challenges. This study presents a League Championship Algorithm Feature Selection with Optimal Deep Learning based Cyberattack Detection (CLAFS-ODLCD) technique for securing the digital ecosystem. The CLAFS-ODLCD technique focuses on the recognition and classification of cyberattacks in the IoT infrastructure. To achieve this, the CLAFS-ODLCD method utilizes the linear scaling normalization (LSN) approach for data pre-processing. Furthermore, the CLAFS-ODLCD method employs the CLAFS approach to choose optimal feature subset. Moreover, the detection and classification of the cyberattacks are accomplished by implementing the stacked sparse autoencoder (SSAE) approach. Finally, the hunger games search (HGS) optimizer is employed for optimum hyperparameter selection. The empirical analysis of the CLAFS-ODLCD method is examined under the WSN-DS dataset. The comparison study of the CLAFS-ODLCD method portrayed a superior accuracy value of 99.48% over existing models.