Imbalanced datasets greatly affect the analysis capability of intrusion detection models,
biasing their classification results toward normal behavior and leading to high false‐positive …

To solve the problem of low accuracy and high false‐alarm rate of existing intrusion
detection models for multiple classifications of intrusion behaviors, a network intrusion …

A network intrusion detection model that fuses a convolutional neural network and a gated
recurrent unit is proposed to address the problems associated with the low accuracy of …

In the field of intrusion detection, there is often a problem of data imbalance, and more and
more unknown types of attacks make detection difficult. To resolve above issues, this article …

Intrusion detection system (IDS) plays an important role in network security by discovering
and preventing malicious activities. Due to the complex and time-varying network …

To address the problems of attack category omission and poor generalization ability of
traditional Intrusion Detection System (IDS) when processing unbalanced input data, an …

To address the issue of low detection accuracy and high false positive rate in existing
network intrusion detection methods, this paper proposes an intrusion detection model …

Internet of Things (IoT) devices are large in number, widely distributed, weak in protection
ability, and vulnerable to various malicious attacks. Intrusion detection technology can …

There is a class-imbalance problem that the number of minority class samples is significantly
lower than that of majority class samples in common network traffic datasets. Class …

In this paper, a high-performance network intrusion detection system based on deep
learning is proposed for situations in which there are significant imbalances between normal …