Deep learning models for digital image processing: a review

R Archana, PSE Jeevaraj - Artificial Intelligence Review, 2024 - Springer
Within the domain of image processing, a wide array of methodologies is dedicated to tasks
including denoising, enhancement, segmentation, feature extraction, and classification …

Cyber risk and cybersecurity: a systematic review of data availability

F Cremer, B Sheehan, M Fortmann… - The Geneva papers …, 2022 - pmc.ncbi.nlm.nih.gov
Cybercrime is estimated to have cost the global economy just under USD 1 trillion in 2020,
indicating an increase of more than 50% since 2018. With the average cyber insurance …

[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 …

Anomaly based network intrusion detection for IoT attacks using deep learning technique

B Sharma, L Sharma, C Lal, S Roy - Computers and Electrical Engineering, 2023 - Elsevier
Abstract Internet of Things (IoT) applications are growing in popularity for being widely used
in many real-world services. In an IoT ecosystem, many devices are connected with each …

Detecting cybersecurity attacks in internet of things using artificial intelligence methods: A systematic literature review

M Abdullahi, Y Baashar, H Alhussian, A Alwadain… - Electronics, 2022 - mdpi.com
In recent years, technology has advanced to the fourth industrial revolution (Industry 4.0),
where the Internet of things (IoTs), fog computing, computer security, and cyberattacks have …

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 …, 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 …

Explainable artificial intelligence for intrusion detection in IoT networks: A deep learning based approach

B Sharma, L Sharma, C Lal, S Roy - Expert Systems with Applications, 2024 - Elsevier
Abstract The Internet of Things (IoT) is currently seeing tremendous growth due to new
technologies and big data. Research in the field of IoT security is an emerging topic. IoT …

Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction

MA Talukder, MM Islam, MA Uddin, KF Hasan… - Journal of big …, 2024 - Springer
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 …

A comprehensive review of dimensionality reduction techniques for feature selection and feature extraction

R Zebari, A Abdulazeez, D Zeebaree, D Zebari… - Journal of Applied …, 2020 - jastt.org
Due to sharp increases in data dimensions, working on every data mining or machine
learning (ML) task requires more efficient techniques to get the desired results. Therefore, in …

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 …