A network intrusion detection method for information systems using federated learning and
improved transformer is proposed to address the problems of long detection time and low …

An efficient machine learning (ML) ensemble technique for categorizing Intrusion Detection
(ID) is proposed in this study. The tuning of the ML model's parameters is a critical topic …

Due to the extensive use of computer networks, new risks have arisen, and improving the
speed and accuracy of security mechanisms has become a critical need. Although new …

Extenuating intrusions into a network has become a great concern for network security
scholars as they pose a threat to the confidentiality, integrity and availability of the data …

Abstract Machine learning methods are widely used to implement intrusion detection models
for detecting and classifying intrusions in a network or a system. However, many challenges …

With the rapid development of Internet technology, the network scale and data have become
increasingly complex, and various forms of Internet attacks and destruction have appeared …

In this paper, we propose a novel method using ensemble learning scheme for classifying
network intrusion detection from the most renowned KDD cup dataset. We have shown that …

Network intrusion detection plays an important role as tools for managing and identifying
potential threats, which presents various challenges. Redundant features and difficult …

Network intrusion detection systems play an important role in protecting the network from
attacks. However, Existing network intrusion data is imbalanced, which makes it difficult to …

Intrusion detection technology is crucial for network security defense as it enables the
detection of network attacks. To address the problem of small sample size in intrusion …