首页|Research from University of Zagreb Broadens Understanding of Machine Learning (C omparative Analysis of Anomaly Detection Approaches in Firewall Logs: Integratin g Light-Weight Synthesis of Security Logs and Artificially Generated Attack Dete ction)
Research from University of Zagreb Broadens Understanding of Machine Learning (C omparative Analysis of Anomaly Detection Approaches in Firewall Logs: Integratin g Light-Weight Synthesis of Security Logs and Artificially Generated Attack Dete ction)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News - Researchers detail new data in artific ial intelligence. According to news reporting from Zagreb, Croatia, by NewsRx jo urnalists, research stated, “Detecting anomalies in large networks is a major ch allenge.” Funders for this research include European Union’s European Regional Development Fund, Operational Programme Competitiveness. Our news journalists obtained a quote from the research from University of Zagre b: “Nowadays, many studies rely on machine learning techniques to solve this pro blem. However, much of this research depends on synthetic or limited datasets an d tends to use specialized machine learning methods to achieve good detection re sults. This study focuses on analyzing firewall logs from a large industrial con trol network and presents a novel method for generating anomalies that simulate real attacker actions within the network without the need for a dedicated testbe d or installed security controls. To demonstrate that the proposed method is fea sible and that the constructed logs behave as one would expect real-world logs t o behave, different supervised and unsupervised learning models were compared us ing different feature subsets, feature construction methods, scaling methods, an d aggregation levels.”
University of ZagrebZagrebCroatiaE uropeCybersecurityCyborgsEmerging TechnologiesMachine LearningUnsuperv ised Learning
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