Network intrusion detection algorithm based on deep neural network

Y Jia, M Wang, Y Wang - IET Information Security, 2019 - Wiley Online Library
With the rapid development of network technology, active defending of the network intrusion
is more important than before. In order to improve the intelligence and accuracy of network …

Machine learning classifiers for network intrusion detection system: comparative study

O Almomani, MA Almaiah, A Alsaaidah… - 2021 International …, 2021 - ieeexplore.ieee.org
Network security risks are increasing at an exponential rate as Internet technology
advances. Keeping the network protected is one of the most challenging of network security …

[HTML][HTML] A deep learning technique for intrusion detection system using a Recurrent Neural Networks based framework

SM Kasongo - Computer Communications, 2023 - Elsevier
In recent years, the spike in the amount of information transmitted through communication
infrastructures has increased due to the advances in technologies such as cloud computing …

A hybrid intrusion detection system based on feature selection and weighted stacking classifier

R Zhao, Y Mu, L Zou, X Wen - IEEE Access, 2022 - ieeexplore.ieee.org
Cyber-attacks occur more frequently with the rapid growth in the Internet. Intrusion detection
systems (IDS) have become an important part of protecting system security. There are still …

Efficient deep CNN-BiLSTM model for network intrusion detection

J Sinha, M Manollas - Proceedings of the 2020 3rd International …, 2020 - dl.acm.org
The need for Network Intrusion Detection systems has risen since usage of cloud
technologies has become mainstream. With the ever growing network traffic, Network …

LNNLS‐KH: A Feature Selection Method for Network Intrusion Detection

X Li, P Yi, W Wei, Y Jiang, L Tian - Security and …, 2021 - Wiley Online Library
As an important part of intrusion detection, feature selection plays a significant role in
improving the performance of intrusion detection. Krill herd (KH) algorithm is an efficient …

An optimization method for intrusion detection classification model based on deep belief network

P Wei, Y Li, Z Zhang, T Hu, Z Li, D Liu - Ieee Access, 2019 - ieeexplore.ieee.org
The rapid development and popularization of the network have brought many problems to
network security. Intrusion detection technology is often used as an effective security …

[HTML][HTML] Addressing the class imbalance problem in network intrusion detection systems using data resampling and deep learning

A Abdelkhalek, M Mashaly - The journal of Supercomputing, 2023 - Springer
Network intrusion detection systems (NIDS) are the most common tool used to detect
malicious attacks on a network. They help prevent the ever-increasing different attacks and …

An optimized ensemble prediction model using AutoML based on soft voting classifier for network intrusion detection

MA Khan, N Iqbal, H Jamil, DH Kim - Journal of Network and Computer …, 2023 - Elsevier
Traditional ML based IDS cannot handle high-speed and ever-evolving attacks.
Furthermore, these traditional IDS face several common challenges, such as processing …

M-MultiSVM: An efficient feature selection assisted network intrusion detection system using machine learning

AV Turukmane, R Devendiran - Computers & Security, 2024 - Elsevier
The intrusions are increasing daily, so there is a huge amount of privacy violations, financial
loss, illegal transferring of information, etc. Various forms of intrusion occur in networks, such …