Matches in SemOpenAlex for { <https://semopenalex.org/work/W4324138045> ?p ?o ?g. }
Showing items 1 to 79 of
79
with 100 items per page.
- W4324138045 abstract "The most popular technique for identifying and blocking malicious network requests is the intrusion detection system, or IDS for short. They are positioned carefully to keep an eye on network traffic going to and coming from every device. Most networking devices can employ an IDS with the use of virtual machines and sophisticated switches. While having good accuracy, the classic SIDS (Signature-Based Intrusion Detection System) cannot identify many modern incursions, such as zero-day attacks, as it relies on a pattern matching technique. Instead, the majority of recently launched attacks can be detected using machine learning, statistical, and knowledge-based methods. An anomaly is defined as any significant difference between the observed behavior and the model.The training phase and the testing phase make up the two stages of the development of these models. During the training phase, a model of typical behavior is learned using the average traffic profile. The system's ability to generalize to as-yet-undiscovered intrusions is then determined during the testing step using a fresh data set. In order to identify network traffic anomalies, we have used an unsupervised machine-learning approach called Isolation Forest in this paper. Using the anomaly score, the algorithm finds the outliers. The KDD data set, a well-known benchmark in the study of Intrusion Detection methods, has been used for training and testing. Keywords: anomaly detection; machine learning; network security" @default.
- W4324138045 created "2023-03-15" @default.
- W4324138045 creator A5020470567 @default.
- W4324138045 creator A5040079565 @default.
- W4324138045 creator A5049632796 @default.
- W4324138045 creator A5054621486 @default.
- W4324138045 creator A5073096779 @default.
- W4324138045 date "2023-03-14" @default.
- W4324138045 modified "2023-10-03" @default.
- W4324138045 title "Machine Learning for the Identification of Network Anomalies" @default.
- W4324138045 doi "https://doi.org/10.55041/ijsrem18082" @default.
- W4324138045 hasPublicationYear "2023" @default.
- W4324138045 type Work @default.
- W4324138045 citedByCount "0" @default.
- W4324138045 crossrefType "journal-article" @default.
- W4324138045 hasAuthorship W4324138045A5020470567 @default.
- W4324138045 hasAuthorship W4324138045A5040079565 @default.
- W4324138045 hasAuthorship W4324138045A5049632796 @default.
- W4324138045 hasAuthorship W4324138045A5054621486 @default.
- W4324138045 hasAuthorship W4324138045A5073096779 @default.
- W4324138045 hasBestOaLocation W43241380451 @default.
- W4324138045 hasConcept C116834253 @default.
- W4324138045 hasConcept C119857082 @default.
- W4324138045 hasConcept C121332964 @default.
- W4324138045 hasConcept C124101348 @default.
- W4324138045 hasConcept C12997251 @default.
- W4324138045 hasConcept C13280743 @default.
- W4324138045 hasConcept C137524506 @default.
- W4324138045 hasConcept C154945302 @default.
- W4324138045 hasConcept C177264268 @default.
- W4324138045 hasConcept C185798385 @default.
- W4324138045 hasConcept C199360897 @default.
- W4324138045 hasConcept C205649164 @default.
- W4324138045 hasConcept C26873012 @default.
- W4324138045 hasConcept C2778579508 @default.
- W4324138045 hasConcept C35525427 @default.
- W4324138045 hasConcept C41008148 @default.
- W4324138045 hasConcept C59822182 @default.
- W4324138045 hasConcept C739882 @default.
- W4324138045 hasConcept C79337645 @default.
- W4324138045 hasConcept C86803240 @default.
- W4324138045 hasConceptScore W4324138045C116834253 @default.
- W4324138045 hasConceptScore W4324138045C119857082 @default.
- W4324138045 hasConceptScore W4324138045C121332964 @default.
- W4324138045 hasConceptScore W4324138045C124101348 @default.
- W4324138045 hasConceptScore W4324138045C12997251 @default.
- W4324138045 hasConceptScore W4324138045C13280743 @default.
- W4324138045 hasConceptScore W4324138045C137524506 @default.
- W4324138045 hasConceptScore W4324138045C154945302 @default.
- W4324138045 hasConceptScore W4324138045C177264268 @default.
- W4324138045 hasConceptScore W4324138045C185798385 @default.
- W4324138045 hasConceptScore W4324138045C199360897 @default.
- W4324138045 hasConceptScore W4324138045C205649164 @default.
- W4324138045 hasConceptScore W4324138045C26873012 @default.
- W4324138045 hasConceptScore W4324138045C2778579508 @default.
- W4324138045 hasConceptScore W4324138045C35525427 @default.
- W4324138045 hasConceptScore W4324138045C41008148 @default.
- W4324138045 hasConceptScore W4324138045C59822182 @default.
- W4324138045 hasConceptScore W4324138045C739882 @default.
- W4324138045 hasConceptScore W4324138045C79337645 @default.
- W4324138045 hasConceptScore W4324138045C86803240 @default.
- W4324138045 hasIssue "03" @default.
- W4324138045 hasLocation W43241380451 @default.
- W4324138045 hasOpenAccess W4324138045 @default.
- W4324138045 hasPrimaryLocation W43241380451 @default.
- W4324138045 hasRelatedWork W11100131 @default.
- W4324138045 hasRelatedWork W1516902003 @default.
- W4324138045 hasRelatedWork W1521770704 @default.
- W4324138045 hasRelatedWork W1969635302 @default.
- W4324138045 hasRelatedWork W2127961541 @default.
- W4324138045 hasRelatedWork W2355809385 @default.
- W4324138045 hasRelatedWork W2377356555 @default.
- W4324138045 hasRelatedWork W2383127772 @default.
- W4324138045 hasRelatedWork W2541628903 @default.
- W4324138045 hasRelatedWork W2736040673 @default.
- W4324138045 hasVolume "07" @default.
- W4324138045 isParatext "false" @default.
- W4324138045 isRetracted "false" @default.
- W4324138045 workType "article" @default.