As computer networks and interconnected systems continue to gain widespread adoption,
ensuring cybersecurity has become a prominent concern for organizations, regardless of …

The detection technique for IP packet header modifications associated with store-and-
forward operation pertains to a methodology or mechanism utilized for the identification and …

This paper presents the FlowTransformer framework, a novel approach for implementing
transformer-based Network Intrusion Detection Systems (NIDSs). FlowTransformer …

Network intrusion detection plays a very important role in network security. Although current
deep learning-based intrusion detection algorithms have achieved good detection …

Machine Learning (ML) has become a valuable asset to solve many real-world tasks. For
Network Intrusion Detection (NID), however, scientific advances in ML are still seen with …

In ML for network security, traditional workflows rely on high-quality labeled data and
manual feature engineering, but limited datasets and human expertise hinder feature …

Intrusion detection is a form of anomalous activity detection in communication network traffic.
Continual learning (CL) approaches to the intrusion detection task accumulate old …

Synthetically generated benchmark datasets are vitally important for machine learning and
network intrusion research. When producing intrusion datasets for research, providers make …

The number of papers on network intrusion detection based on machine and deep learning
is growing at an unprecedented rate. Most of these papers follow a well-consolidated …

The rapid development of cloud–fog–edge computing and mobile devices has led to
massive amounts of data being generated. Also, artificial intelligence technology, like …