A Dependable Hybrid Machine Learning Model for Network Intrusion Detection. (arXiv:2212.04546v1 [cs.CR])

Network intrusion detection systems (NIDSs) play an important role in
computer network security. There are several detection mechanisms where
anomaly-based automated detection outperforms others significantly. Amid the
sophistication and growing number of attacks, dealing with large amounts of
data is a recognized issue in the development of anomaly-based NIDS. However,
do current models meet the needs of today's networks in terms of required
accuracy and dependability? In this research, we propose a new hybrid model
that combines machine learning and …

detection hybrid intrusion intrusion detection machine machine learning network