Network intrusion detection system: A systematic study of machine learning and deep learning approaches

Z Ahmad, A Shahid Khan, C Wai Shiang… - Transactions on …, 2021 - Wiley Online Library
The rapid advances in the internet and communication fields have resulted in a huge
increase in the network size and the corresponding data. As a result, many novel attacks are …

Internet of things applications, security challenges, attacks, intrusion detection, and future visions: A systematic review

N Mishra, S Pandya - IEEE Access, 2021 - ieeexplore.ieee.org
Internet of Things (IoT) technology is prospering and entering every part of our lives, be it
education, home, vehicles, or healthcare. With the increase in the number of connected …

[HTML][HTML] A hybrid CNN+ LSTM-based intrusion detection system for industrial IoT networks

HC Altunay, Z Albayrak - … Science and Technology, an International Journal, 2023 - Elsevier
Abstract The Internet of Things (IoT) ecosystem has proliferated based on the use of the
internet and cloud-based technologies in the industrial area. IoT technology used in the …

A survey on machine learning techniques for cyber security in the last decade

K Shaukat, S Luo, V Varadharajan, IA Hameed… - IEEE …, 2020 - ieeexplore.ieee.org
Pervasive growth and usage of the Internet and mobile applications have expanded
cyberspace. The cyberspace has become more vulnerable to automated and prolonged …

[HTML][HTML] Performance analysis of intrusion detection systems using a feature selection method on the UNSW-NB15 dataset

SM Kasongo, Y Sun - Journal of Big Data, 2020 - Springer
Computer networks intrusion detection systems (IDSs) and intrusion prevention systems
(IPSs) are critical aspects that contribute to the success of an organization. Over the past …

Machine learning methods for cyber security intrusion detection: Datasets and comparative study

IF Kilincer, F Ertam, A Sengur - Computer Networks, 2021 - Elsevier
The increase in internet usage brings security problems with it. Malicious software can affect
the operation of the systems and disrupt data confidentiality due to the security gaps in the …

[HTML][HTML] Performance analysis of machine learning models for intrusion detection system using Gini Impurity-based Weighted Random Forest (GIWRF) feature …

RA Disha, S Waheed - Cybersecurity, 2022 - Springer
To protect the network, resources, and sensitive data, the intrusion detection system (IDS)
has become a fundamental component of organizations that prevents cybercriminal …

Variational LSTM enhanced anomaly detection for industrial big data

X Zhou, Y Hu, W Liang, J Ma… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
With the increasing population of Industry 4.0, industrial big data (IBD) has become a hotly
discussed topic in digital and intelligent industry field. The security problem existing in the …

An effective intrusion detection approach using SVM with naïve Bayes feature embedding

J Gu, S Lu - Computers & Security, 2021 - Elsevier
Network security has become increasingly important in recent decades, while intrusion
detection system plays a critical role in protecting it. Various machine learning techniques …

[HTML][HTML] IGRF-RFE: a hybrid feature selection method for MLP-based network intrusion detection on UNSW-NB15 dataset

Y Yin, J Jang-Jaccard, W Xu, A Singh, J Zhu… - Journal of Big Data, 2023 - Springer
The effectiveness of machine learning models can be significantly averse to redundant and
irrelevant features present in the large dataset which can cause drastic performance …