Ahmadian, A.Yadav, A.K.Ferrara, M.2025-10-152025-10-152025978807378002997896665448999788024810256978807378171297899863427489782954494807978956236198980248102559788024823911807378002X1613-00732-s2.0-105017609253https://hdl.handle.net/20.500.14517/8497Associazione Italiana per l'Intelligenza Artificiale (AIxIA); Associazione per la Matematica Applicata alle Scienze Economiche e Sociali (A.M.A.S.E.S.); Master's Program in Management of Small and Medium Enterprises; NODES (Nord Ovest Digitale E Sostenibile) project - Spoke 3 Industria del turismo e culturaThe widespread implementation of innovative healthcare systems brings about notable security risks, especially in cyber-physical systems (CPS). Ensuring patient safety and system performance is crucial in CPS, particularly when detecting and preventing attacks. This paper discusses smart healthcare systems and presents a modified deep neural network (DNN) model that can effectively classify various types of attacks on CPS. In addition, we present a modified Ant Lion Optimization (ALO) algorithm that enhances the model’s accuracy and reliability when combined with ensemble methods. By incorporating multiple feature selection techniques, the voting-based ensemble selection method improves the ability to detect attacks by leveraging the importance of the rankings of each feature assessed in those approaches. This enhances the recovery of vital data while minimizing the number of characteristics utilized for identification. Our optimized DNN model outperforms traditional approaches regarding real-time attack detection in smart healthcare system networks. From a theoretical standpoint, the methods outlined in the paper have the potential to enhance the security measures implemented in the construction of CPS and significantly bolster the resilience of smart healthcare systems against the latest cyber threats. The optimized DNN, which was further optimized with the help of the modified ALO algorithm, returned excellent results, with a carpet accuracy of 99.5%, a precision of 99.3%, a recall of 99.4%, an F1-score of 99.35%, and an ROCAUC of 0.995. Such metrics illustrate the model’s effectiveness in detecting and classifying different cyberattack forms with a high accuracy rate. © 2025 Elsevier B.V., All rights reserved.eninfo:eu-repo/semantics/closedAccessAttack DetectionCyber-Physical Systems (CPS)Deep Neural Networks (DNN)Ensemble Feature SelectionModified Ant Lion Optimization (ALO)Smart HealthcareConference ObjectN/AQ440314152