Matches in SemOpenAlex for { <https://semopenalex.org/work/W4387366720> ?p ?o ?g. }
Showing items 1 to 63 of
63
with 100 items per page.
- W4387366720 abstract "The Internet of Things (IoT) has gained significance over the past several years and is currently one of the most important technologies. The capacity to link everyday objects, such as home appliances, medical equipment, autos, and baby monitors, to the internet via embedded devices with a minimum of human interaction has made continuous communication between people, processes, and things feasible. IoT devices have established themselves in many sectors, of which electronic health is considered the most important. The IoT environment deals with many private and sensitive health data that must be kept safe from tampering or theft. If safety precautions are not implemented, these dangers and assaults against IoT devices in the health sector might completely destroy this industry. Detecting security threats to an IoT environment requires sophisticated technology; these attacks can be identified using machine learning (ML) techniques, which can also predict snooping behavior based on unidentified patterns. In this paper, it is proposed to apply five strategies to detect attacks in network traffic based on the NF-ToN-IoT dataset. The classifiers used are Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), Artificial Neural Network (ANN), and Support Vector Machine (SVM) models. These algorithms have been used instead of a centralized method to deliver compact security systems for IoT devices. The dataset was pre-processed to eliminate extraneous or missing data, and then a feature engineering approach was used to extract key features. The results obtained by applying each of the listed classifiers to a maximum classification accuracy of 98% achieved by the RF model showed our comparison to other work." @default.
- W4387366720 created "2023-10-06" @default.
- W4387366720 creator A5007292101 @default.
- W4387366720 creator A5040338622 @default.
- W4387366720 creator A5075002961 @default.
- W4387366720 date "2023-01-01" @default.
- W4387366720 modified "2023-10-16" @default.
- W4387366720 title "Securing IoT Devices in e-Health using Machine Learning Techniques" @default.
- W4387366720 doi "https://doi.org/10.14569/ijacsa.2023.0140949" @default.
- W4387366720 hasPublicationYear "2023" @default.
- W4387366720 type Work @default.
- W4387366720 citedByCount "0" @default.
- W4387366720 crossrefType "journal-article" @default.
- W4387366720 hasAuthorship W4387366720A5007292101 @default.
- W4387366720 hasAuthorship W4387366720A5040338622 @default.
- W4387366720 hasAuthorship W4387366720A5075002961 @default.
- W4387366720 hasBestOaLocation W43873667201 @default.
- W4387366720 hasConcept C119857082 @default.
- W4387366720 hasConcept C12267149 @default.
- W4387366720 hasConcept C138885662 @default.
- W4387366720 hasConcept C154945302 @default.
- W4387366720 hasConcept C169258074 @default.
- W4387366720 hasConcept C26517878 @default.
- W4387366720 hasConcept C2776401178 @default.
- W4387366720 hasConcept C38652104 @default.
- W4387366720 hasConcept C41008148 @default.
- W4387366720 hasConcept C41895202 @default.
- W4387366720 hasConcept C50644808 @default.
- W4387366720 hasConcept C52001869 @default.
- W4387366720 hasConcept C81860439 @default.
- W4387366720 hasConcept C84525736 @default.
- W4387366720 hasConceptScore W4387366720C119857082 @default.
- W4387366720 hasConceptScore W4387366720C12267149 @default.
- W4387366720 hasConceptScore W4387366720C138885662 @default.
- W4387366720 hasConceptScore W4387366720C154945302 @default.
- W4387366720 hasConceptScore W4387366720C169258074 @default.
- W4387366720 hasConceptScore W4387366720C26517878 @default.
- W4387366720 hasConceptScore W4387366720C2776401178 @default.
- W4387366720 hasConceptScore W4387366720C38652104 @default.
- W4387366720 hasConceptScore W4387366720C41008148 @default.
- W4387366720 hasConceptScore W4387366720C41895202 @default.
- W4387366720 hasConceptScore W4387366720C50644808 @default.
- W4387366720 hasConceptScore W4387366720C52001869 @default.
- W4387366720 hasConceptScore W4387366720C81860439 @default.
- W4387366720 hasConceptScore W4387366720C84525736 @default.
- W4387366720 hasIssue "9" @default.
- W4387366720 hasLocation W43873667201 @default.
- W4387366720 hasOpenAccess W4387366720 @default.
- W4387366720 hasPrimaryLocation W43873667201 @default.
- W4387366720 hasRelatedWork W2004826645 @default.
- W4387366720 hasRelatedWork W2955796858 @default.
- W4387366720 hasRelatedWork W3154045278 @default.
- W4387366720 hasRelatedWork W3210764983 @default.
- W4387366720 hasRelatedWork W4200112873 @default.
- W4387366720 hasRelatedWork W4224941037 @default.
- W4387366720 hasRelatedWork W4285162676 @default.
- W4387366720 hasRelatedWork W4367335949 @default.
- W4387366720 hasRelatedWork W4367336074 @default.
- W4387366720 hasRelatedWork W4379620016 @default.
- W4387366720 hasVolume "14" @default.
- W4387366720 isParatext "false" @default.
- W4387366720 isRetracted "false" @default.
- W4387366720 workType "article" @default.