FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4293238913> ?p ?o ?g. }

• W4293238913 abstract "Abstract In today's cyberspace, network traffic is more massive, complex, and multi-dimensional than ever before. In order to capture malicious network attacks, a machine learning-based network intrusion detection system (NIDS) has become the mainstream method. However, there are still high false-positive and false-negative rates, which cannot guarantee detection accuracy. On the one hand, normal behaviour dominates the Internet, and network traffic presents uneven distribution. On the other hand, the goal of machine learning algorithms is usually to obtain the highest overall accuracy without considering class-imbalanced. It is difficult for the model to learn good performance from a few attack examples. Training the model with an imbalanced data distribution often leads to severe overfitting and severely damages the model's generalization ability. To improve class-imbalanced learning in network intrusion detection, it is necessary to capture the similarities between samples in different classes and compare them with samples in other classes. Based on this, we propose ConFlow, a supervised contrastive learning method for network intrusion detection. First, we design a feature extraction encoder for bidirectional network flow, and add GELU, LayerNorm, and Skip-connection units to the MLP framework, which can enhance the representation ability of the neural network. Then, we use the dropout layer's randomness in the encoder for data augmentation, and different vector representations can be obtained by feeding the same network flow into the encoder twice. Lastly, through the weighted supervised contrastive loss and cross-entropy loss in the training phase. The ConFlow method can improve class-imbalanced learning and does not need the two stages of pre-training and fine-tuning, which can further mine maliciously attacks hidden under normal traffic. The experimental results on the ISCX-IDS2012 and CSE-CIC-IDS2017 datasets show that the ConFlow outperforms other works, and the performance improvement on few-shot learning and robustness test is more significant. The reference PyTorch code is released at https://github.com/AshinWang/ConFlow." @default.
• W4293238913 created "2022-08-27" @default.
• W4293238913 creator A5003816318 @default.
• W4293238913 creator A5034316838 @default.
• W4293238913 creator A5072860776 @default.
• W4293238913 creator A5076587936 @default.
• W4293238913 date "2022-04-28" @default.
• W4293238913 modified "2023-10-18" @default.
• W4293238913 title "ConFlow: Contrast Network Flow Improving Class-Imbalanced Learning in Network Intrusion Detection" @default.
• W4293238913 doi "https://doi.org/10.21203/rs.3.rs-1572776/v1" @default.
• W4293238913 hasPublicationYear "2022" @default.
• W4293238913 type Work @default.
• W4293238913 citedByCount "2" @default.
• W4293238913 countsByYear W42932389132022 @default.
• W4293238913 crossrefType "posted-content" @default.
• W4293238913 hasAuthorship W4293238913A5003816318 @default.
• W4293238913 hasAuthorship W4293238913A5034316838 @default.
• W4293238913 hasAuthorship W4293238913A5072860776 @default.
• W4293238913 hasAuthorship W4293238913A5076587936 @default.
• W4293238913 hasBestOaLocation W42932389131 @default.
• W4293238913 hasConcept C101738243 @default.
• W4293238913 hasConcept C119857082 @default.
• W4293238913 hasConcept C124101348 @default.
• W4293238913 hasConcept C153180895 @default.
• W4293238913 hasConcept C154945302 @default.
• W4293238913 hasConcept C167981619 @default.
• W4293238913 hasConcept C22019652 @default.
• W4293238913 hasConcept C35525427 @default.
• W4293238913 hasConcept C41008148 @default.
• W4293238913 hasConcept C50644808 @default.
• W4293238913 hasConceptScore W4293238913C101738243 @default.
• W4293238913 hasConceptScore W4293238913C119857082 @default.
• W4293238913 hasConceptScore W4293238913C124101348 @default.
• W4293238913 hasConceptScore W4293238913C153180895 @default.
• W4293238913 hasConceptScore W4293238913C154945302 @default.
• W4293238913 hasConceptScore W4293238913C167981619 @default.
• W4293238913 hasConceptScore W4293238913C22019652 @default.
• W4293238913 hasConceptScore W4293238913C35525427 @default.
• W4293238913 hasConceptScore W4293238913C41008148 @default.
• W4293238913 hasConceptScore W4293238913C50644808 @default.
• W4293238913 hasLocation W42932389131 @default.
• W4293238913 hasOpenAccess W4293238913 @default.
• W4293238913 hasPrimaryLocation W42932389131 @default.
• W4293238913 hasRelatedWork W1996541855 @default.
• W4293238913 hasRelatedWork W2766433866 @default.
• W4293238913 hasRelatedWork W2964465226 @default.
• W4293238913 hasRelatedWork W2985459377 @default.
• W4293238913 hasRelatedWork W2989932438 @default.
• W4293238913 hasRelatedWork W3011996705 @default.
• W4293238913 hasRelatedWork W3099765033 @default.
• W4293238913 hasRelatedWork W3128220493 @default.
• W4293238913 hasRelatedWork W3175189414 @default.
• W4293238913 hasRelatedWork W4210794429 @default.
• W4293238913 isParatext "false" @default.
• W4293238913 isRetracted "false" @default.
• W4293238913 workType "article" @default.