An effective convolutional neural network based on SMOTE and Gaussian mixture model for intrusion detection in imbalanced dataset
H Zhang, L Huang, CQ Wu, Z Li - Computer Networks, 2020 - Elsevier
Abstract Network Intrusion Detection System (NIDS) is a key security device in modern
networks to detect malicious activities. However, the problem of imbalanced class …
networks to detect malicious activities. However, the problem of imbalanced class …
A novel multi-module integrated intrusion detection system for high-dimensional imbalanced data
J Cui, L Zong, J Xie, M Tang - Applied Intelligence, 2023 - Springer
The high dimension, complexity, and imbalance of network data are hot issues in the field of
intrusion detection. Nowadays, intrusion detection systems face some challenges in …
intrusion detection. Nowadays, intrusion detection systems face some challenges in …
Effectiveness of focal loss for minority classification in network intrusion detection systems
As the rapid development of information and communication technology systems offers
limitless access to data, the risk of malicious violations increases. A network intrusion …
limitless access to data, the risk of malicious violations increases. A network intrusion …
Improving detection accuracy for imbalanced network intrusion classification using cluster-based under-sampling with random forests
MO Miah, SS Khan, S Shatabda… - 2019 1st international …, 2019 - ieeexplore.ieee.org
Network intrusion classification int he imbalanced big data environment becomes a
significant and important issue in information and communications technology (ICT) in this …
significant and important issue in information and communications technology (ICT) in this …
A consolidated decision tree-based intrusion detection system for binary and multiclass imbalanced datasets
The widespread acceptance and increase of the Internet and mobile technologies have
revolutionized our existence. On the other hand, the world is witnessing and suffering due to …
revolutionized our existence. On the other hand, the world is witnessing and suffering due to …
Network intrusion detection model based on CNN and GRU
B Cao, C Li, Y Song, Y Qin, C Chen - Applied Sciences, 2022 - mdpi.com
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 …
recurrent unit is proposed to address the problems associated with the low accuracy of …
STL-HDL: A new hybrid network intrusion detection system for imbalanced dataset on big data environment
The ability to process large amounts of data in real time using big data analytics tools brings
many advantages that can be used in intrusion detection systems. Deep learning …
many advantages that can be used in intrusion detection systems. Deep learning …
A deep learning model for network intrusion detection with imbalanced data
Y Fu, Y Du, Z Cao, Q Li, W Xiang - Electronics, 2022 - mdpi.com
With an increase in the number and types of network attacks, traditional firewalls and data
encryption methods can no longer meet the needs of current network security. As a result …
encryption methods can no longer meet the needs of current network security. As a result …
Deep and machine learning approaches for anomaly-based intrusion detection of imbalanced network traffic
Recently, cybersecurity threats have increased dramatically, and the techniques used by the
attackers continue to evolve and become ingenious during the attack. Moreover, the …
attackers continue to evolve and become ingenious during the attack. Moreover, the …
Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction
Cybersecurity has emerged as a critical global concern. Intrusion Detection Systems (IDS)
play a critical role in protecting interconnected networks by detecting malicious actors and …
play a critical role in protecting interconnected networks by detecting malicious actors and …