Domenico Lofú, Andrea Pazienza, Agostino Abbatecola, E. Lella, Nicola Macchiarulo, Pietro Noviello
{"title":"Watching against the Unseen: AI-powered Approach to Detect Attacks on Critical Infrastructure","authors":"Domenico Lofú, Andrea Pazienza, Agostino Abbatecola, E. Lella, Nicola Macchiarulo, Pietro Noviello","doi":"10.23919/SpliTech58164.2023.10193130","DOIUrl":null,"url":null,"abstract":"The increasing convergence of Operational Technology (OT) networks into Information Technology (IT) communications poses critical infrastructures to new threats that may cause huge hazards. The study of protection mechanisms and the development of security systems capable of preventing such attacks is of paramount importance nowadays. Besides formally defining the model representing the intertwining of IT and OT networks of a Chemical Industry, we prove the ability to detect different types of attacks with good results experimentally by implementing an Intrusion Detection System (IDS) based on Deep Learning (DL) that achieves an accuracy of 87, 19%.","PeriodicalId":361369,"journal":{"name":"2023 8th International Conference on Smart and Sustainable Technologies (SpliTech)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 8th International Conference on Smart and Sustainable Technologies (SpliTech)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/SpliTech58164.2023.10193130","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The increasing convergence of Operational Technology (OT) networks into Information Technology (IT) communications poses critical infrastructures to new threats that may cause huge hazards. The study of protection mechanisms and the development of security systems capable of preventing such attacks is of paramount importance nowadays. Besides formally defining the model representing the intertwining of IT and OT networks of a Chemical Industry, we prove the ability to detect different types of attacks with good results experimentally by implementing an Intrusion Detection System (IDS) based on Deep Learning (DL) that achieves an accuracy of 87, 19%.