FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4324137464> ?p ?o ?g. }

• W4324137464 abstract "Novel ways of security against attacks to critical infrastructure are becoming more important as their number and sophistication increase. In order to provide the kind of security that is necessary, one must consider both preventive measures and an understanding of the current state of affairs inside the systems being protected. Researches and institutions have brought security solutions that are both intelligent and selfsufficient, such as the necessity for sophisticated proactive monitoring capability in Intrusion Detection Systems (IDS). This study argues that critical infrastructures need automated IDS solutions and that adding Machine Learning (ML) approaches into IDS will be useful for developing dynamically adjustable algorithms that can promptly respond to changing circumstances and threats. To this end, this study assesses the several degrees of automation provided by the IDS and provide a plan for introducing preemptive automation into critical settings. Finally, this study evaluates how well the proposed method stacks up against other methods previously presented in the literature. Machine learning presents difficulties in the field of cybersecurity that must be effectively addressed using sound methodology and theory. Transfer learning, support vector machines, and Bayesian classification are just a few examples of the statistical and machine learning approaches that have been shown to be useful in reducing the effects of cyberattacks. It is crucial to establish smart security systems that can discover hidden patterns and insights from packet headers and then construct a data-driven machine-learning model to avoid these assaults. This study examines the use of machine learning methods to cybersecurity data with the goal of bolstering the safety of existing infrastructures. This article has addressed the current state of cybersecurity threats as well as the deployment of machine learning methods to combat the aforementioned challenges." @default.
• W4324137464 created "2023-03-15" @default.
• W4324137464 creator A5016771732 @default.
• W4324137464 creator A5041315010 @default.
• W4324137464 creator A5064331666 @default.
• W4324137464 creator A5073202093 @default.
• W4324137464 date "2023-01-23" @default.
• W4324137464 modified "2023-09-27" @default.
• W4324137464 title "Differentially Distributed Private Intelligence Security in Cybersecurity Infrastructures" @default.
• W4324137464 cites W1967841763 @default.
• W4324137464 cites W2018505727 @default.
• W4324137464 cites W2310347995 @default.
• W4324137464 cites W2600058599 @default.
• W4324137464 cites W2902529532 @default.
• W4324137464 cites W2905097561 @default.
• W4324137464 cites W2993999308 @default.
• W4324137464 cites W3007738605 @default.
• W4324137464 cites W3103753990 @default.
• W4324137464 cites W3110962653 @default.
• W4324137464 doi "https://doi.org/10.1109/icssit55814.2023.10061092" @default.
• W4324137464 hasPublicationYear "2023" @default.
• W4324137464 type Work @default.
• W4324137464 citedByCount "0" @default.
• W4324137464 crossrefType "proceedings-article" @default.
• W4324137464 hasAuthorship W4324137464A5016771732 @default.
• W4324137464 hasAuthorship W4324137464A5041315010 @default.
• W4324137464 hasAuthorship W4324137464A5064331666 @default.
• W4324137464 hasAuthorship W4324137464A5073202093 @default.
• W4324137464 hasConcept C115901376 @default.
• W4324137464 hasConcept C119857082 @default.
• W4324137464 hasConcept C127413603 @default.
• W4324137464 hasConcept C144024400 @default.
• W4324137464 hasConcept C154945302 @default.
• W4324137464 hasConcept C168725872 @default.
• W4324137464 hasConcept C199360897 @default.
• W4324137464 hasConcept C2780801425 @default.
• W4324137464 hasConcept C29852176 @default.
• W4324137464 hasConcept C35525427 @default.
• W4324137464 hasConcept C36289849 @default.
• W4324137464 hasConcept C38652104 @default.
• W4324137464 hasConcept C41008148 @default.
• W4324137464 hasConcept C78519656 @default.
• W4324137464 hasConcept C95713431 @default.
• W4324137464 hasConceptScore W4324137464C115901376 @default.
• W4324137464 hasConceptScore W4324137464C119857082 @default.
• W4324137464 hasConceptScore W4324137464C127413603 @default.
• W4324137464 hasConceptScore W4324137464C144024400 @default.
• W4324137464 hasConceptScore W4324137464C154945302 @default.
• W4324137464 hasConceptScore W4324137464C168725872 @default.
• W4324137464 hasConceptScore W4324137464C199360897 @default.
• W4324137464 hasConceptScore W4324137464C2780801425 @default.
• W4324137464 hasConceptScore W4324137464C29852176 @default.
• W4324137464 hasConceptScore W4324137464C35525427 @default.
• W4324137464 hasConceptScore W4324137464C36289849 @default.
• W4324137464 hasConceptScore W4324137464C38652104 @default.
• W4324137464 hasConceptScore W4324137464C41008148 @default.
• W4324137464 hasConceptScore W4324137464C78519656 @default.
• W4324137464 hasConceptScore W4324137464C95713431 @default.
• W4324137464 hasFunder F4320320736 @default.
• W4324137464 hasLocation W43241374641 @default.
• W4324137464 hasOpenAccess W4324137464 @default.
• W4324137464 hasPrimaryLocation W43241374641 @default.
• W4324137464 hasRelatedWork W15380372 @default.
• W4324137464 hasRelatedWork W2001032217 @default.
• W4324137464 hasRelatedWork W2171447151 @default.
• W4324137464 hasRelatedWork W2584408238 @default.
• W4324137464 hasRelatedWork W2779961139 @default.
• W4324137464 hasRelatedWork W2914048830 @default.
• W4324137464 hasRelatedWork W3117571503 @default.
• W4324137464 hasRelatedWork W4205568523 @default.
• W4324137464 hasRelatedWork W4285337355 @default.
• W4324137464 hasRelatedWork W772338884 @default.
• W4324137464 isParatext "false" @default.
• W4324137464 isRetracted "false" @default.
• W4324137464 workType "article" @default.