Matches in SemOpenAlex for { <https://semopenalex.org/work/W4377970503> ?p ?o ?g. }
Showing items 1 to 77 of
77
with 100 items per page.
- W4377970503 abstract "The problem of network security has arisen as a key source of worry in today’s linked society. Sabotage and information extortion are among the most significant risks toan organization’s security. They include a broad spectrum of dangers such as huge intellectual property theft and software attacks, as well as sabotage and information extortion. Inappropriate usage of protocols on a network may potentially represent a security risk to a system or network. In network security, the use of data mining tools to gather, alter, and analyze enormous amounts of data is vital. With the help of a range of data mining technologies that are now accessible, it is possible to conduct analysis and forecasting of data and threats across computer networks. This study makes an effort to forecast network security dangers by using a variety of categorization methodologies. The classification techniques used in this study include the Naive Bayes Classifier, the Decision Tree Classifier and the K Nearest Neighbors Classifier. It assesses the efficacy of the categorization approaches described above in order to discover potential risks and vulnerabilities. In order to analyze a dataset and extract information, Simple Machine Learning techniques are utilized, such as in Distributed Denial of Service (DDoS) attacks (R2L), U2R attacks, and Probe Attacks (U2R). The Naive Bayes Classifier, the Decision Tree Classifier, and the K Nearest neighbours method are all examples of classification algorithms discussed in this section. For the purpose of comparing the performance of the proposed machine learning algorithm technique with other approaches, the entropy calculation and accuracy, recall, F1Score, and entropy may be employed." @default.
- W4377970503 created "2023-05-25" @default.
- W4377970503 creator A5005643121 @default.
- W4377970503 creator A5021165018 @default.
- W4377970503 creator A5071205205 @default.
- W4377970503 date "2023-01-23" @default.
- W4377970503 modified "2023-10-17" @default.
- W4377970503 title "An Improvised Machine Learning Model KNN for Malware Detection and Classification" @default.
- W4377970503 cites W2120617515 @default.
- W4377970503 cites W2169768310 @default.
- W4377970503 cites W3016094225 @default.
- W4377970503 cites W3031719555 @default.
- W4377970503 cites W3090031345 @default.
- W4377970503 doi "https://doi.org/10.1109/iccci56745.2023.10128189" @default.
- W4377970503 hasPublicationYear "2023" @default.
- W4377970503 type Work @default.
- W4377970503 citedByCount "0" @default.
- W4377970503 crossrefType "proceedings-article" @default.
- W4377970503 hasAuthorship W4377970503A5005643121 @default.
- W4377970503 hasAuthorship W4377970503A5021165018 @default.
- W4377970503 hasAuthorship W4377970503A5071205205 @default.
- W4377970503 hasConcept C110875604 @default.
- W4377970503 hasConcept C119857082 @default.
- W4377970503 hasConcept C12267149 @default.
- W4377970503 hasConcept C124101348 @default.
- W4377970503 hasConcept C136764020 @default.
- W4377970503 hasConcept C154945302 @default.
- W4377970503 hasConcept C17744445 @default.
- W4377970503 hasConcept C182590292 @default.
- W4377970503 hasConcept C199539241 @default.
- W4377970503 hasConcept C2779066997 @default.
- W4377970503 hasConcept C35525427 @default.
- W4377970503 hasConcept C38652104 @default.
- W4377970503 hasConcept C38822068 @default.
- W4377970503 hasConcept C41008148 @default.
- W4377970503 hasConcept C52001869 @default.
- W4377970503 hasConcept C541664917 @default.
- W4377970503 hasConcept C5481197 @default.
- W4377970503 hasConcept C84525736 @default.
- W4377970503 hasConcept C94124525 @default.
- W4377970503 hasConcept C95623464 @default.
- W4377970503 hasConceptScore W4377970503C110875604 @default.
- W4377970503 hasConceptScore W4377970503C119857082 @default.
- W4377970503 hasConceptScore W4377970503C12267149 @default.
- W4377970503 hasConceptScore W4377970503C124101348 @default.
- W4377970503 hasConceptScore W4377970503C136764020 @default.
- W4377970503 hasConceptScore W4377970503C154945302 @default.
- W4377970503 hasConceptScore W4377970503C17744445 @default.
- W4377970503 hasConceptScore W4377970503C182590292 @default.
- W4377970503 hasConceptScore W4377970503C199539241 @default.
- W4377970503 hasConceptScore W4377970503C2779066997 @default.
- W4377970503 hasConceptScore W4377970503C35525427 @default.
- W4377970503 hasConceptScore W4377970503C38652104 @default.
- W4377970503 hasConceptScore W4377970503C38822068 @default.
- W4377970503 hasConceptScore W4377970503C41008148 @default.
- W4377970503 hasConceptScore W4377970503C52001869 @default.
- W4377970503 hasConceptScore W4377970503C541664917 @default.
- W4377970503 hasConceptScore W4377970503C5481197 @default.
- W4377970503 hasConceptScore W4377970503C84525736 @default.
- W4377970503 hasConceptScore W4377970503C94124525 @default.
- W4377970503 hasConceptScore W4377970503C95623464 @default.
- W4377970503 hasLocation W43779705031 @default.
- W4377970503 hasOpenAccess W4377970503 @default.
- W4377970503 hasPrimaryLocation W43779705031 @default.
- W4377970503 hasRelatedWork W1525957489 @default.
- W4377970503 hasRelatedWork W2048637796 @default.
- W4377970503 hasRelatedWork W2109894153 @default.
- W4377970503 hasRelatedWork W2754051746 @default.
- W4377970503 hasRelatedWork W2767906932 @default.
- W4377970503 hasRelatedWork W2893156076 @default.
- W4377970503 hasRelatedWork W3173089840 @default.
- W4377970503 hasRelatedWork W3188003272 @default.
- W4377970503 hasRelatedWork W4313463492 @default.
- W4377970503 hasRelatedWork W3100834880 @default.
- W4377970503 isParatext "false" @default.
- W4377970503 isRetracted "false" @default.
- W4377970503 workType "article" @default.