Application of deep autoencoder as an one-class classifier for unsupervised network intrusion detection: a comparative evaluation.

ABSTRACT: The ever-increasing use of internet has opened a new avenue for cybercriminals, alarming the online businesses and organization to stay ahead of evolving thread landscape. To this end, intrusion detection system (IDS) is deemed as a promising defensive mechanism to ensure network security. Recently, deep learning has gained ground in the field of intrusion detection but majority of progress has been witnessed on supervised learning which requires adequate labeled data for training. In real practice, labeling the high volume of network traffic is laborious and error prone. Intuitively, unsupervised deep learning approaches has received gaining momentum. Specifically, the advances in deep learning has endowed autoencoder (AE) with greater ability for data reconstruction to learn the robust f

SUBMITTER: Vaiyapuri T 

PROVIDER: S-EPMC7924711 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature