Adversarial machine learning for network intrusion detection systems: A comprehensive survey

K He, DD Kim, MR Asghar - IEEE Communications Surveys & …, 2023 - ieeexplore.ieee.org
Network-based Intrusion Detection System (NIDS) forms the frontline defence against
network attacks that compromise the security of the data, systems, and networks. In recent …

A review on machine learning and deep learning perspectives of IDS for IoT: recent updates, security issues, and challenges

A Thakkar, R Lohiya - Archives of Computational Methods in Engineering, 2021 - Springer
Abstract Internet of Things (IoT) is widely accepted technology in both industrial as well as
academic field. The objective of IoT is to combine the physical environment with the cyber …

Deep learning for anomaly detection: A survey

R Chalapathy, S Chawla - arXiv preprint arXiv:1901.03407, 2019 - arxiv.org
Anomaly detection is an important problem that has been well-studied within diverse
research areas and application domains. The aim of this survey is two-fold, firstly we present …

Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues

A Aldweesh, A Derhab, AZ Emam - Knowledge-Based Systems, 2020 - Elsevier
The massive growth of data that are transmitted through a variety of devices and
communication protocols have raised serious security concerns, which have increased the …

Internet of things (IoT) security dataset evolution: Challenges and future directions

B Kaur, S Dadkhah, F Shoeleh, ECP Neto, P Xiong… - Internet of Things, 2023 - Elsevier
The evolution of mobile technologies has introduced smarter and more connected objects
into our day-to-day lives. This trend, known as the Internet of Things (IoT), has applications …

A survey of machine and deep learning methods for internet of things (IoT) security

MA Al-Garadi, A Mohamed, AK Al-Ali… - … surveys & tutorials, 2020 - ieeexplore.ieee.org
The Internet of Things (IoT) integrates billions of smart devices that can communicate with
one another with minimal human intervention. IoT is one of the fastest developing fields in …

A survey of deep learning methods for cyber security

DS Berman, AL Buczak, JS Chavis, CL Corbett - Information, 2019 - mdpi.com
This survey paper describes a literature review of deep learning (DL) methods for cyber
security applications. A short tutorial-style description of each DL method is provided …

Deep learning in mobile and wireless networking: A survey

C Zhang, P Patras, H Haddadi - IEEE Communications surveys …, 2019 - ieeexplore.ieee.org
The rapid uptake of mobile devices and the rising popularity of mobile applications and
services pose unprecedented demands on mobile and wireless networking infrastructure …

HCRNNIDS: Hybrid convolutional recurrent neural network-based network intrusion detection system

MA Khan - Processes, 2021 - mdpi.com
Nowadays, network attacks are the most crucial problem of modern society. All networks,
from small to large, are vulnerable to network threats. An intrusion detection (ID) system is …

Kitsune: an ensemble of autoencoders for online network intrusion detection

Y Mirsky, T Doitshman, Y Elovici, A Shabtai - arXiv preprint arXiv …, 2018 - arxiv.org
Neural networks have become an increasingly popular solution for network intrusion
detection systems (NIDS). Their capability of learning complex patterns and behaviors make …