Machine learning for anomaly detection: A systematic review

AB Nassif, MA Talib, Q Nasir, FM Dakalbab - Ieee Access, 2021 - ieeexplore.ieee.org
Anomaly detection has been used for decades to identify and extract anomalous
components from data. Many techniques have been used to detect anomalies. One of the …

Machine learning and deep learning methods for intrusion detection systems: A survey

H Liu, B Lang - applied sciences, 2019 - mdpi.com
Networks play important roles in modern life, and cyber security has become a vital research
area. An intrusion detection system (IDS) which is an important cyber security technique …

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 …

Passban IDS: An intelligent anomaly-based intrusion detection system for IoT edge devices

M Eskandari, ZH Janjua, M Vecchio… - IEEE Internet of Things …, 2020 - ieeexplore.ieee.org
Cyber-threat protection is today's one of the most challenging research branches of
information technology, while the exponentially increasing number of tiny, connected …

Detecting Internet of Things attacks using distributed deep learning

GDLT Parra, P Rad, KKR Choo, N Beebe - Journal of Network and …, 2020 - Elsevier
The reliability of Internet of Things (IoT) connected devices is heavily dependent on the
security model employed to protect user data and prevent devices from engaging in …

Intrusion detection based on autoencoder and isolation forest in fog computing

K Sadaf, J Sultana - IEEE Access, 2020 - ieeexplore.ieee.org
Fog Computing has emerged as an extension to cloud computing by providing an efficient
infrastructure to support IoT. Fog computing acting as a mediator provides local processing …

Machine learning models for secure data analytics: A taxonomy and threat model

R Gupta, S Tanwar, S Tyagi, N Kumar - Computer Communications, 2020 - Elsevier
In recent years, rapid technological advancements in smart devices and their usage in a
wide range of applications exponentially increases the data generated from these devices …

[HTML][HTML] Intrusion detection models for IOT networks via deep learning approaches

B Madhu, MVG Chari, R Vankdothu, AK Silivery… - Measurement …, 2023 - Elsevier
Abstract The Internet of things (IoT) has gained more attention in recent years because of its
ubiquitous operations, connectivity, methods of communication, and intelligent decisions to …

Towards model generalization for intrusion detection: Unsupervised machine learning techniques

M Verkerken, L D'hooge, T Wauters, B Volckaert… - Journal of Network and …, 2022 - Springer
Through the ongoing digitization of the world, the number of connected devices is
continuously growing without any foreseen decline in the near future. In particular, these …

Toward explainable deep neural network based anomaly detection

K Amarasinghe, K Kenney… - 2018 11th international …, 2018 - ieeexplore.ieee.org
Anomaly detection in industrial processes is crucial for general process monitoring and
process health assessment. Deep Neural Networks (DNNs) based anomaly detection has …