[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123312-en":3,"doc-seo-123312-105":30,"detail-sidebar-cat-0-en-105":91},{"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":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123312,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Sovereignty-Aware Intrusion Detection on Streaming Data - Automatic Machine Learning Pipeline and Semantic Reasoning","Intrusion Detection Systems (IDS) are essential for protecting network infrastructures from malicious attacks, yet traditional signature-based approaches often fail against novel or obfuscated threats. This work presents a sovereignty-aware IDS framework that combines machine learning with streaming data and semantic knowledge representation to improve detection accuracy and scalability. The system uses Apache Kafka for real-time processing, an automated machine learning pipeline for traffic classification, and OWL-based semantic reasoning for advanced threat detection. Evaluated on NSL-KDD and CIC-IDS-2017, results show improved detection accuracy, higher processing efficiency, reduced data storage needs, and alignment with local data laws and privacy requirements.","[Available online at www.sciencedirect.com](Available online at www.sciencedirect.com)  \nScienceDirect  \nProcedia Computer Science 254 (2025) 78–87  \nInternational Conference on Digital Sovereignty (ICDS) Sovereignty-Aware Intrusion Detection on Streaming Data:  \nAutomatic Machine Learning Pipeline and Semantic Reasoning  \nAyan Chatterjeea , Sundar Gopalakrishnanb , Ayan Mondalc  \naDept. of Digital Technology, STIFTELSEN NILU, Kjeller, 2007, Norway  \nb Dept. of R&D, Data Consultants AS, Grimstad, 4876, Norway  \nc Dept. of Electronics Engineering, KIIT, Bhubaneswar, 751024, India  \nAbstract  \nIntrusion Detection Systems (IDS) are critical in safeguarding network infrastructures against malicious attacks. Traditional IDSsoften struggle with knowledge representation, real-time detection, and accuracy, especially when dealing with high-throughput data. This paper proposes a novel IDS framework that leverages machine learning models, streaming data, and semantic knowledge representation to enhance intrusion detection accuracy and scalability. Additionally, the study incorporates the concept of Digital Sovereignty, ensuring that data control, security, and privacy are maintained according to national and regional regulations. The proposed system integrates Apache Kafka for real-time data processing, an automatic machine learning pipeline (e.g., Tree-based Pipeline Optimization Tool (TPOT)) for classifying network traffic, and OWL-based semantic reasoning for advanced threat detection. The proposed system, evaluated on NSL-KDD and CIC-IDS-2017 datasets, demonstrated qualitative outcomes such as local compliance, reduced data storage needs due to real-time processing, and improved adaptability to local data laws. Experimental results reveal significant improvements in detection accuracy, processing efficiency, and Sovereignty alignment.  \n© 2025 The Authors. Published by Elsevier B.V.  \nThis is an open access article under the CC BY-NC-ND license ([https://creativecommons.org/licenses/by-nc-nd/4.0](https://creativecommons.org/licenses/by-nc-nd/4.0))  \nPeer review under the responsibility of the scientific committee ofthe ICDS 2024 (General chairs and program committee Chairs) Keywords: Intrusion Detection; Digital Sovereignty; Machine Learning; Streaming Data; Semantic Ontology.  \n1. Introduction  \nThe rapid rise in cyber-attacks highlights the critical need for advanced Intrusion Detection Systems (IDS) in modern network defenses. Traditional IDS, which primarily use signature-based detection, struggle to identify novel or obfuscated threats. However, with the advent of machine learning and big data processing, more sophisticated IDS solutions have emerged, capable of real-time data analysis to detect anomalies and potential intrusions [1] . As nations increasingly prioritize digital sovereignty—controlling their digital infrastructure and data—the importance of robust  \n∗ Ayan Chatterjee. Tel.: +47-94719372 .  \nE-mail address: [ayan@nilu.no](ayan@nilu.no)  \n1877-0509 © 2025 The Authors. Published by Elsevier B.V.  \nThis is an open access article under the CC BY-NC-ND license ([https://creativecommons.org/licenses/by-nc-nd/4.0](https://creativecommons.org/licenses/by-nc-nd/4.0))  \nPeer review under the responsibility of the scientific committee of the ICDS 2024 (General chairs and program committee Chairs)  \n10.1016/j.procs.2025.02.066  \nAyan Chatterjee et al. / Procedia Computer Science 254 (2025) 78–87 79  \nand adaptive IDS in safeguarding network security and data privacy has become paramount [2] . IDS are a critical component of national cybersecurity strategies. With the rise of cloud services and globalized digital infrastructure, the data monitored by IDSs often crosses borders, leading to concerns over data sovereignty [2] . Ensuring that an IDS adheres to the principles of Digital Sovereignty involves guaranteeing that data is processed, stored, and managed within the legal frameworks of the region or nation in which the IDS","cbCaip0E9IITodhM","https://ap.wps.com/l/cbCaip0E9IITodhM","pdf",478616,1,10,"English","en",105,"# Introduction\n## Problem background and motivation\n## Research questions\n## Study structure","[{\"question\":\"What limitations of traditional intrusion detection does the paper address?\",\"answer\":\"Traditional IDS often rely on signature-based detection, which struggles with novel or obfuscated threats. It also highlights challenges related to knowledge representation, real-time detection, and accuracy on high-throughput data.\"},{\"question\":\"How does the proposed system process streaming network data in real time?\",\"answer\":\"It integrates Apache Kafka for real-time data processing, enabling the IDS to analyze streaming events continuously rather than relying on static datasets.\"},{\"question\":\"What role does semantic reasoning play in threat detection?\",\"answer\":\"The framework uses OWL-based semantic reasoning to provide structured, meaningful understanding of streaming data, improving contextual and adaptive threat detection beyond pure classification.\"}]","Sovereignty-Aware Intrusion Detection on Streaming Data - Automatic Machine Learning Pipeline and Semantic Reasoning | PDF",1785815874,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"sovereignty-aware-intrusion-detection-on-streaming-data-automatic-machine-learning-pipeline-and-semantic-reasoning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/sovereignty-aware-intrusion-detection-on-streaming-data-automatic-machine-learning-pipeline-and-semantic-reasoning/123312/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What limitations of traditional intrusion detection does the paper address?","Question",{"text":75,"@type":76},"Traditional IDS often rely on signature-based detection, which struggles with novel or obfuscated threats. It also highlights challenges related to knowledge representation, real-time detection, and accuracy on high-throughput data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed system process streaming network data in real time?",{"text":80,"@type":76},"It integrates Apache Kafka for real-time data processing, enabling the IDS to analyze streaming events continuously rather than relying on static datasets.",{"name":82,"@type":73,"acceptedAnswer":83},"What role does semantic reasoning play in threat detection?",{"text":84,"@type":76},"The framework uses OWL-based semantic reasoning to provide structured, meaningful understanding of streaming data, improving contextual and adaptive threat detection beyond pure classification.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]