Matches in SemOpenAlex for { <https://semopenalex.org/work/W2783227908> ?p ?o ?g. }
Showing items 1 to 98 of
98
with 100 items per page.
- W2783227908 abstract "With an exponential increase in the Internet traffic over the network, there are growing concerns of identification of legitimate users which are the bulk sources of Internet traffic generation. However, due to the occurrence of anomalies in the network traffic, normal operations or the functionalities (traffic classification, resource allocation, and service management) of network get affected. Thus, in a given time frame, there is a requirement of anomalies detection in the network. The efficiency of any anomaly detection model mainly depends on the selection of relevant features and the learning algorithms which are used for classification of the network traffic patterns. However, due to curse of dimensionality, imbalance between classes, and variations in the types of anomalies, most of the existing solutions reported in the literature fail to deal with problems that occurs while detecting anomalies in large-scale network data. So, to remove these gaps in the existing solutions, we propose a new hybrid anomaly detection scheme called as Ensemble-based Classification Model for Network Anomaly Detection (EnClass) to detect anomalies in real- world networking datasets. EnClass has three modules as (i) Hoeffding-bound based clustering to identify the optimal subset of features to be taken for classification of network traffic (ii) Eigenvalues computation module to refine the features set for removal of unnecessary attributes and (iii) Very-fast decision tree for network traffic classification. In order to validate the proposed anomaly detection model, experimental evaluation is performed using real-world Knowledge Discovery and Data Mining (KDD'99) dataset with respect to parameters such as-detection rate, false positive rate, and F-score. The comparison with existing approaches clearly demonstrates the effectiveness of the EnClass in terms of detection rate (98.58%), false positive rate (0.42%), and F-score (96.06%)." @default.
- W2783227908 created "2018-01-26" @default.
- W2783227908 creator A5005877741 @default.
- W2783227908 creator A5037688054 @default.
- W2783227908 creator A5049862622 @default.
- W2783227908 creator A5053714308 @default.
- W2783227908 creator A5056940320 @default.
- W2783227908 date "2017-12-01" @default.
- W2783227908 modified "2023-10-18" @default.
- W2783227908 title "EnClass: Ensemble-Based Classification Model for Network Anomaly Detection in Massive Datasets" @default.
- W2783227908 cites W1418477825 @default.
- W2783227908 cites W1993913559 @default.
- W2783227908 cites W2002900768 @default.
- W2783227908 cites W2012674856 @default.
- W2783227908 cites W2031366272 @default.
- W2783227908 cites W2059939031 @default.
- W2783227908 cites W2068714596 @default.
- W2783227908 cites W2073703998 @default.
- W2783227908 cites W2082860616 @default.
- W2783227908 cites W2149163997 @default.
- W2783227908 cites W2284033649 @default.
- W2783227908 cites W2342408547 @default.
- W2783227908 cites W2343117455 @default.
- W2783227908 cites W2509460041 @default.
- W2783227908 cites W2517682726 @default.
- W2783227908 cites W2547977869 @default.
- W2783227908 cites W2587703207 @default.
- W2783227908 cites W2737711166 @default.
- W2783227908 cites W4233413206 @default.
- W2783227908 cites W4247569889 @default.
- W2783227908 doi "https://doi.org/10.1109/glocom.2017.8255025" @default.
- W2783227908 hasPublicationYear "2017" @default.
- W2783227908 type Work @default.
- W2783227908 sameAs 2783227908 @default.
- W2783227908 citedByCount "22" @default.
- W2783227908 countsByYear W27832279082018 @default.
- W2783227908 countsByYear W27832279082019 @default.
- W2783227908 countsByYear W27832279082020 @default.
- W2783227908 countsByYear W27832279082021 @default.
- W2783227908 countsByYear W27832279082022 @default.
- W2783227908 countsByYear W27832279082023 @default.
- W2783227908 crossrefType "proceedings-article" @default.
- W2783227908 hasAuthorship W2783227908A5005877741 @default.
- W2783227908 hasAuthorship W2783227908A5037688054 @default.
- W2783227908 hasAuthorship W2783227908A5049862622 @default.
- W2783227908 hasAuthorship W2783227908A5053714308 @default.
- W2783227908 hasAuthorship W2783227908A5056940320 @default.
- W2783227908 hasConcept C110875604 @default.
- W2783227908 hasConcept C116834253 @default.
- W2783227908 hasConcept C119857082 @default.
- W2783227908 hasConcept C121332964 @default.
- W2783227908 hasConcept C124101348 @default.
- W2783227908 hasConcept C12997251 @default.
- W2783227908 hasConcept C136764020 @default.
- W2783227908 hasConcept C154945302 @default.
- W2783227908 hasConcept C169988225 @default.
- W2783227908 hasConcept C26873012 @default.
- W2783227908 hasConcept C41008148 @default.
- W2783227908 hasConcept C59822182 @default.
- W2783227908 hasConcept C63969886 @default.
- W2783227908 hasConcept C73555534 @default.
- W2783227908 hasConcept C739882 @default.
- W2783227908 hasConcept C84525736 @default.
- W2783227908 hasConcept C86803240 @default.
- W2783227908 hasConceptScore W2783227908C110875604 @default.
- W2783227908 hasConceptScore W2783227908C116834253 @default.
- W2783227908 hasConceptScore W2783227908C119857082 @default.
- W2783227908 hasConceptScore W2783227908C121332964 @default.
- W2783227908 hasConceptScore W2783227908C124101348 @default.
- W2783227908 hasConceptScore W2783227908C12997251 @default.
- W2783227908 hasConceptScore W2783227908C136764020 @default.
- W2783227908 hasConceptScore W2783227908C154945302 @default.
- W2783227908 hasConceptScore W2783227908C169988225 @default.
- W2783227908 hasConceptScore W2783227908C26873012 @default.
- W2783227908 hasConceptScore W2783227908C41008148 @default.
- W2783227908 hasConceptScore W2783227908C59822182 @default.
- W2783227908 hasConceptScore W2783227908C63969886 @default.
- W2783227908 hasConceptScore W2783227908C73555534 @default.
- W2783227908 hasConceptScore W2783227908C739882 @default.
- W2783227908 hasConceptScore W2783227908C84525736 @default.
- W2783227908 hasConceptScore W2783227908C86803240 @default.
- W2783227908 hasLocation W27832279081 @default.
- W2783227908 hasOpenAccess W2783227908 @default.
- W2783227908 hasPrimaryLocation W27832279081 @default.
- W2783227908 hasRelatedWork W2143820878 @default.
- W2783227908 hasRelatedWork W2667207928 @default.
- W2783227908 hasRelatedWork W2806741695 @default.
- W2783227908 hasRelatedWork W2912112202 @default.
- W2783227908 hasRelatedWork W3189286258 @default.
- W2783227908 hasRelatedWork W3207797160 @default.
- W2783227908 hasRelatedWork W3210364259 @default.
- W2783227908 hasRelatedWork W4290647774 @default.
- W2783227908 hasRelatedWork W4300558037 @default.
- W2783227908 hasRelatedWork W4377864969 @default.
- W2783227908 isParatext "false" @default.
- W2783227908 isRetracted "false" @default.
- W2783227908 magId "2783227908" @default.
- W2783227908 workType "article" @default.