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Network Intrusion Detection Systems: A Systematic Literature Review of Hybrid Deep Learning Approaches
(2022-06)
Network Intrusion Detection Systems (NIDSs) have become standard security solutions that endeavours to discover unauthorized access to an organizational computer network by scrutinizing incoming and outgoing network traffic ...
A Comparative Study of Deep Learning and Transfer Learning in Detection of Diabetic Retinopathy
(2022)
Computer vision has gained momentum in medical imaging tasks. Deep learning and Transfer learning are some of the approaches used in computer vision. The aim of this research was to do a comparative study of deep learning ...
Evaluating Linear and Non-linear Dimensionality Reduction Approaches for Deep Learning-based Network Intrusion Detection Systems
(2023-08-08)
Dimensionality reduction is an essential ingredient of machine learning modelling that seeks to improve the performance of such models by extracting better quality features from data while removing irrelevant and redundant ...
Discriminative spatial-temporal feature learning for modeling network intrusion detection systems
(Journal of Computer Security, 2023-02)
Increasing interest and advancement of internet and communication technologies have made network security rise as a vibrant research domain. Network intrusion detection systems (NIDSs) have developed as indispensable defense ...