[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126875-en":3,"doc-seo-126875-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126875,1099523885336,"Violet","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",6,"Technology","Next-gen security in IIoT - integrating intrusion detection systems with machine learning for Industry 4.0 resilience","Next-gen security in Industry 4.0 relies on protecting industrial internet of things (IIoT) environments where traditional architectures expose centralized vulnerabilities and evolving cyber threats. The research presents an advanced intrusion detection approach that integrates intrusion detection systems with machine learning and deep learning for dynamic adaptation. It evaluates performance using the improved CIDDS and BoTIoT intrusion detection datasets, reporting very high accuracy and strong recall and precision, while maintaining efficient learning and detection phases.","Next-gen security in IIoT: integrating intrusion detection systems with machine learning for industry 4.0 resilience  \nLahcen Idouglid1, Said Tkatek1, Khalid Elfayq1, Azidine Guezzaz2  \n1Computer Sciences Research Laboratory, Ibn Tofail University, Kenitra, Morocco 2Computer Science and Mathematics Department, Cadi Ayyad University, Marrakech, Morocco  \nArticle history:  \nReceived Jan 19, 2024 Revised Mar 3, 2024 Accepted Mar 5, 2024  \nKeywords:  \nDeep learning  \nIndustrial internet of things Intrusion detection systems Machine learning  \nSmart factory  \nXGBoost  \nCorresponding Author:  \nIn the dynamic landscape of Industry 4.0, characterized by the integration of smart technologies and the industrial internet of things (IIoT), ensuring robust security measures is imperative. This paper explores advanced security solutions tailored for the IIoT, focusing on the integration of intrusion detection systems (IDS) with advanced machine learning (ML) and deep learning (DL) techniques. In this paper, we present a novel intrusion detection model to fortify to fortify Industry 4.0 systems against evolving cyber threats by leveraging ML an DL algorithms for dynamic adaptation. To evaluate the performances and effectiveness of our proposed model, we use the improved Coburg intrusion detection data sets (CIDDS) and BoTIoT datasets, showcasing notable performance attributes with an exceptional 99.99% accuracy, high recall, and precision scores. The model demonstrates computational efficiency, with rapid learning and detection phases. This research contributes to advancing next-gen security solutions for Industry 4.0, offering a promising approach to tackle contemporary cyber.  \nThis is an open access article under the CC BY-SA license.  \nLahcen Idouglid  \nComputer Sciences Research Laboratory, Ibn Tofail University Kenitra, Morocco  \nEmail: [lahcen.idouglid@uit.ac.ma](lahcen.idouglid@uit.ac.ma)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nThe integration of the industrial internet of things (IIoT) into industrial landscapes holds tremendous promise for optimizing efficiency through real-time monitoring and control of diverse machine activities. However, this transformative potential is met with a critical concern cybersecurity [1] . Traditional IIoT architectures, often centralized, present vulnerabilities susceptible to cyber threats including individual points of failure and bottlenecks.  \nThe ongoing Industry 4.0, often termed the digital transformation, is marked by the integration of modern digital technologies like internet of things (IoT), artificial intelligent (AI), cloud computing, and robotics, creating connected cyber-physical systems [2] . The concept of the IIoT pertains to the interconnectedness of industrial devices, encompassing sensors, controllers, machines, and robots, linking them both internally and to the broader Internet. The IIoT holds the potential to enhance the effectiveness, output, and adaptability of industrial operations while facilitating the emergence of innovative services and business model [3] . Nevertheless, the adoption of IIoT also brings about significant security challenges [4] . These challenges predominantly stem from the prolonged lifespan of components, the extensive scope of networks, and the stringent safety and reliability prerequisites inherent in industrial systems [5] .  \nAddressing the intricate security challenges encountered by the IIoT necessitates inventive methods that go beyond conventional measures. One promising avenue involves the integration of advanced security technologies, specifically intrusion detection systems (IDS) and machine learning (ML) [6], [7] . IDS can play  \na pivotal role in fortifying IIoT against vulnerabilities such as weak password protection and unauthorized access. By monitoring network and device activities, IDS can swiftly detect suspicious patterns or anomalies that may indicate security threats [8] .  \nThe integration of machine learning adds a layer of","cbCaib6i8yd373dY","https://ap.wps.com/l/cbCaib6i8yd373dY","pdf",467819,3,1,10,"English","en",105,"# Introduction\n## IIoT and Industry 4.0 security challenges\n## Role of intrusion detection systems (IDS)\n## Machine learning for adaptive defense\n## IDS classifications and detection approaches","[{\"question\":\"Why is cybersecurity critical for IIoT in Industry 4.0?\",\"answer\":\"IIoT environments face vulnerabilities from centralized architectures, long-lived components, large network scopes, and strict safety and reliability requirements. These factors make traditional protection insufficient against evolving threats.\"},{\"question\":\"How do intrusion detection systems support IIoT security?\",\"answer\":\"IDS monitor network and device activities to detect suspicious patterns or anomalies, adding a defensive layer beyond prevention mechanisms like privacy and verification. They help recognize and mitigate potential threats promptly.\"},{\"question\":\"What datasets and metrics are used to evaluate the proposed intrusion detection model?\",\"answer\":\"The model is evaluated using the improved CIDDS and BoTIoT intrusion detection datasets. Results highlight exceptional accuracy and strong recall and precision, along with computational efficiency in learning and detection.\"}]","Next-gen security in IIoT - integrating intrusion detection systems with machine learning for Industry 4.0 resilience | PDF",1785935345,25,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"next-gen-security-in-iiot-integrating-intrusion-detection-systems-with-machine-learning-for-industry-40-resilience","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/technology/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/next-gen-security-in-iiot-integrating-intrusion-detection-systems-with-machine-learning-for-industry-40-resilience/126875/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is cybersecurity critical for IIoT in Industry 4.0?","Question",{"text":76,"@type":77},"IIoT environments face vulnerabilities from centralized architectures, long-lived components, large network scopes, and strict safety and reliability requirements. These factors make traditional protection insufficient against evolving threats.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How do intrusion detection systems support IIoT security?",{"text":81,"@type":77},"IDS monitor network and device activities to detect suspicious patterns or anomalies, adding a defensive layer beyond prevention mechanisms like privacy and verification. They help recognize and mitigate potential threats promptly.",{"name":83,"@type":74,"acceptedAnswer":84},"What datasets and metrics are used to evaluate the proposed intrusion detection model?",{"text":85,"@type":77},"The model is evaluated using the improved CIDDS and BoTIoT intrusion detection datasets. 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