A hybrid deep random neural network for cyberattack detection in the industrial internet of things

ZE Huma, S Latif, J Ahmad, Z Idrees, A Ibrar… - IEEE …, 2021 - ieeexplore.ieee.org
The Industrial Internet of Things (IIoT) refers to the use of traditional Internet of Things (IoT)
concepts in industrial sectors and applications. IIoT has several applications in smart homes …

A novel intrusion detection system based on an optimal hybrid kernel extreme learning machine

L Lv, W Wang, Z Zhang, X Liu - Knowledge-based systems, 2020 - Elsevier
Intrusion detection is a challenging technology in the area of cyberspace security for
protecting a system from malicious attacks. A novel accurate and effective misuse intrusion …

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 …

Evaluating shallow and deep neural networks for network intrusion detection systems in cyber security

RK Vigneswaran, R Vinayakumar… - 2018 9th …, 2018 - ieeexplore.ieee.org
Intrusion detection system (IDS) has become an essential layer in all the latest ICT system
due to an urge towards cyber safety in the day-to-day world. Reasons including uncertainty …

Semi-supervised machine learning approach for DDoS detection

M Idhammad, K Afdel, M Belouch - Applied Intelligence, 2018 - Springer
Abstract Even though advanced Machine Learning (ML) techniques have been adopted for
DDoS detection, the attack remains a major threat of the Internet. Most of the existing ML …

An intrusion detection approach using ensemble support vector machine based chaos game optimization algorithm in big data platform

A Ponmalar, V Dhanakoti - Applied Soft Computing, 2022 - Elsevier
The mainstream computing technology is not efficient in managing massive data and
detecting network traffic intrusions, often including big data. The intrusions present in …

An enhanced multi-stage deep learning framework for detecting malicious activities from autonomous vehicles

IA Khan, N Moustafa, D Pi, W Haider… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Intelligent Transportation Systems (ITS), particularly Autonomous Vehicles (AVs), are
susceptible to safety and security concerns that impend people's lives. Nothing like manually …

Building an effective intrusion detection system using the modified density peak clustering algorithm and deep belief networks

Y Yang, K Zheng, C Wu, X Niu, Y Yang - Applied Sciences, 2019 - mdpi.com
Featured Application The model proposed in this paper can be deployed to the enterprise
gateway, dynamically monitor network activities, and connect with the firewall to protect the …

[HTML][HTML] Generating realistic intrusion detection system dataset based on fuzzy qualitative modeling

W Haider, J Hu, J Slay, BP Turnbull, Y Xie - Journal of Network and …, 2017 - Elsevier
Prior to deploying any intrusion detection system, it is essential to obtain a realistic
evaluation of its performance. However, the major problems currently faced by the research …

An integrated framework for privacy-preserving based anomaly detection for cyber-physical systems

M Keshk, E Sitnikova, N Moustafa… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Protecting Cyber-physical Systems (CPSs) is highly important for preserving sensitive
information and detecting cyber threats. Developing a robust privacy-preserving anomaly …