Hybrid deep learning based attack detection for imbalanced data classification

HIGHLIGHTS

• who: Rasha Almarshdi from the Department of Computer, Faculty of and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia have published the Article: Hybrid Deep Learning Based Attack Detection for Imbalanced Data Classification, in the Journal: (JOURNAL)
• what: The authors implement the model on the UNSW-NB15 dataset which is a new network intrusion dataset that categorizes the network traffic into normal and attacks traffic. Despite these advantages, anomaly-based detection algorithms have a high false positive rate which is the main reason for the lack of adoption of machine_learning based-anomaly IDS. The paper . . .
  

   

  

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