The revolution of the industrial sector in the automated one has happened with the use of the Industrial Internet of things (IIoT). They are providing unprecedented possibilities for connection, and automation. Also, the ubiquitous of IIoT has brought new cyber security challenges, putting sensitive data at risk. This research paper proposes a comprehensive model for enhancing the cyber security of IIoT systems. Our model integrates various countermeasures, including a proactive assessment of security vulnerabilities, examination of identified vulnerabilities, categorizing data, delivery of comprehensive reports, and assurance of effective countermeasures based on a cost-benefit approach, aligned with industry standards and frameworks. The proposed model aims to address the need for the development of robust and resilient cyber security solutions for IIoT environments. This research work introduces the proposed model's main functions, integration, workflow, and references. With this research, we contribute to the enhancement of cyber security in the IIoT environment by proposing a model that assists with proactive assessment, effective response, and informed decision-making. We envision that the proposed model will support industrial organizations in securing their IIoT systems against cyber threats, ultimately have stability and secure industrial operations.

This study enhances the domain of cybersecurity within wireless sensor networks (WSNs) through the integration of sophisticated artificial intelligence (AI) and machine learning (ML) techniques. By conducting an exploratory data analysis (EDA), this research reveals critical insights into network behavior, facilitating the development of predictive models for anomaly detection. The application of ML algorithms decision trees (DT) and random forest (RF) demonstrated dominant performance in identifying potential security threats, as evidenced by metrics accuracy, precision, recall, and F1 scores. This work not only enhances the security framework for WSNs but also contributes to the extensive field of network security, offering a robust analytical and predictive methodology for future cybersecurity initiatives. The advanced model can be deployed in other WSN and internet of things (IoT) based applications.

Parole chiave: sanità pubblica, promozione della salute, ricerca sanitaria, stratificazione del rischio, salute della popolazione, prevenzione primaria RIASSUNTO La lotta alla cronicità è la grande sfida di tutti i Paesi industrializzati. Le strategie fino ad ora messe in campo dal Servizio Sanitario Nazionale, focalizzati sugli interventi di cura ed assistenza, non sono orientate alla riduzione dell’incidenza di malattia, il cui trend è in costante e in progressiva crescita, con conseguente sempre maggiore impegno di risorse sanitarie, economiche e sociali. Il presente articolo si propone di delineare la visione di sviluppo di una prevenzione di popolazione proattiva della malattia cronica. Attraverso una Medicina di Iniziativa, un modello assistenziale che “va incontro” al paziente, o meglio a target di popolazione, prima che le patologie insorgano o si aggravino, garantendo interventi proattivi adeguati e differenziati in rapporto al livello di rischio. Key Words: Public health, health promotion, health care research, risk stratification, population health, primary prevention SUMMARY The fight against chronic diseases is the great challenge of all industrialized countries. The strategies hitherto deployed by the National Health Service, focused on treatment and care interventions, are not oriented towards reducing the incidence of disease: The actual trend of chronic diseases is constantly and progressively increasing, resulting in an ever greater commitment of health, economic and social resourc- es. This article aims to outline the vision of developing a proactive population prevention of chronic disease. Through a Medicine of Initiative, a care model that ‘meets’ the patient, or rather the targets of population, before diseases arise or worsen, guaranteeing adequate proactive interventions differentiated according to their needs and risk level. Autore per corrispondenza: alessandra.buja@unipd.it