Big data analytics for intrusion detection system: Statistical decision-making using finite dirichlet mixture models

N Moustafa, G Creech, J Slay - Data Analytics and Decision Support for …, 2017 - Springer
An intrusion detection system has become a vital mechanism to detect a wide variety of
malicious activities in the cyber domain. However, this system still faces an important …

Network anomaly intrusion detection using a nonparametric Bayesian approach and feature selection

W Alhakami, A ALharbi, S Bourouis, R Alroobaea… - IEEE …, 2019 - ieeexplore.ieee.org
Anomaly-based intrusion detection systems (IDSs) have been deployed to monitor network
activity and to protect systems and the Internet of Things (IoT) devices from attacks (or …

Anomaly detection system using beta mixture models and outlier detection

N Moustafa, G Creech, J Slay - Progress in Computing, Analytics and …, 2018 - Springer
An intrusion detection system (IDS) plays a significant role in recognising suspicious
activities in hosts or networks, even though this system still has the challenge of producing …

Dual-IDS: A bagging-based gradient boosting decision tree model for network anomaly intrusion detection system

MHL Louk, BA Tama - Expert Systems with Applications, 2023 - Elsevier
The mission of an intrusion detection system (IDS) is to monitor network activities and
assess whether or not they are malevolent. Specifically, anomaly-based IDS can discover …

Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model

S Aljawarneh, M Aldwairi, MB Yassein - Journal of Computational Science, 2018 - Elsevier
Efficiently detecting network intrusions requires the gathering of sensitive information. This
means that one has to collect large amounts of network transactions including high details of …

Two-tier network anomaly detection model: a machine learning approach

HH Pajouh, GH Dastghaibyfard, S Hashemi - Journal of Intelligent …, 2017 - Springer
Network anomaly detection is one of the most challenging fields in cyber security. Most of
the proposed techniques have high computation complexity or based on heuristic …

[HTML][HTML] A hybrid machine learning method for increasing the performance of network intrusion detection systems

AA Megantara, T Ahmad - Journal of Big Data, 2021 - Springer
The internet has grown enormously for many years. It is not just connecting computer
networks but also a group of devices worldwide involving big data. The internet provides an …

An integrated rule based intrusion detection system: analysis on UNSW-NB15 data set and the real time online dataset

V Kumar, D Sinha, AK Das, SC Pandey, RT Goswami - Cluster Computing, 2020 - Springer
Intrusion detection system (IDS) has been developed to protect the resources in the network
from different types of threats. Existing IDS methods can be classified as either anomaly …

A survey of intrusion detection models based on NSL-KDD data set

R Thomas, D Pavithran - 2018 Fifth HCT Information …, 2018 - ieeexplore.ieee.org
An Intrusion detection system is a key component of the security management infrastructure.
Machine learning advances has benefited many domains including the security domain …

Unsupervised anomaly intrusion detection via localized bayesian feature selection

W Fan, N Bouguila, D Ziou - 2011 IEEE 11th International …, 2011 - ieeexplore.ieee.org
In recent years, an increasing number of security threats have brought a serious risk to the
internet and computer networks. Intrusion Detection System (IDS) plays a vital role in …