A hybrid machine learning approach to network anomaly detection

T Shon, J Moon - Information Sciences, 2007 - Elsevier
Zero-day cyber attacks such as worms and spy-ware are becoming increasingly widespread
and dangerous. The existing signature-based intrusion detection mechanisms are often not …

A machine learning framework for network anomaly detection using SVM and GA

T Shon, Y Kim, C Lee, J Moon - Proceedings from the sixth …, 2005 - ieeexplore.ieee.org
In today's world of computer security, Internet attacks such as Dos/DDos, worms, and
spyware continue to evolve as detection techniques improve. It is not easy, however, to …

[图书][B] Network anomaly detection: A machine learning perspective

DK Bhattacharyya, JK Kalita - 2013 - books.google.com
With the rapid rise in the ubiquity and sophistication of Internet technology and the
accompanying growth in the number of network attacks, network intrusion detection has …

Supervised machine learning techniques for efficient network intrusion detection

N Aboueata, S Alrasbi, A Erbad… - 2019 28th …, 2019 - ieeexplore.ieee.org
Cloud computing is gaining significant traction and virtualized data centers are becoming
popular as a cost-effective infrastructure in telecommunication industry. Infrastructure as a …

Network anomaly detection using LSTM based autoencoder

M Said Elsayed, NA Le-Khac, S Dev… - Proceedings of the 16th …, 2020 - dl.acm.org
Anomaly detection aims to discover patterns in data that do not conform to the expected
normal behaviour. One of the significant issues for anomaly detection techniques is the …

Analysis of network traffic features for anomaly detection

F Iglesias, T Zseby - Machine Learning, 2015 - Springer
Anomaly detection in communication networks provides the basis for the uncovering of
novel attacks, misconfigurations and network failures. Resource constraints for data storage …

Intrusion detection based on machine learning techniques in computer networks

AS Dina, D Manivannan - Internet of Things, 2021 - Elsevier
Intrusions in computer networks have increased significantly in the last decade, due in part
to a profitable underground cyber-crime economy and the availability of sophisticated tools …

Evaluation of machine learning techniques for network intrusion detection

M Zaman, CH Lung - NOMS 2018-2018 IEEE/IFIP Network …, 2018 - ieeexplore.ieee.org
Network traffic anomaly may indicate a possible intrusion in the network and therefore
anomaly detection is important to detect and prevent the security attacks. The early research …

HELAD: A novel network anomaly detection model based on heterogeneous ensemble learning

Y Zhong, W Chen, Z Wang, Y Chen, K Wang, Y Li… - Computer Networks, 2020 - Elsevier
Network traffic anomaly detection is an important technique of ensuring network security.
However, there are usually three problems with existing machine learning based anomaly …

A deep learning approach for network anomaly detection based on AMF-LSTM

M Zhu, K Ye, Y Wang, CZ Xu - Network and Parallel Computing: 15th IFIP …, 2018 - Springer
The Internet and computer networks are currently suffering from different security threats.
This paper presents a new method called AMF-LSTM for abnormal traffic detection by using …