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Industrial cyber-physical systems (CPS) are vulnerable to cyberattacks that can compromise their operations, safety, and security. Traditional intrusion detection systems are ineffective in detecting and preventing cyberattacks in industrial CPS due to their dynamic and complex nature. In addition, there is a lack of interpretability and transparency in the decision-making process of IDS, which makes it challenging for system administrators to identify risks. Furthermore, deep learning approaches can be computationally intensive and challenging to deploy and scale in industrial CPS. This study proposes a novel intrusion detection system called TEA-EKHO-IDS that utilises trustworthy explainable artificial intelligence (XAI) and enhanced krill herd optimisation (EKHO) to detect breaches in the CPS. The proposed method uses XAI-EKHO for feature selection, which gives more robust global searching capabilities and …