Recent advances in anomaly detection in Internet of Things: Status, challenges, and perspectives
This paper provides a comprehensive survey of anomaly detection for the Internet of Things
(IoT). Anomaly detection poses numerous challenges in IoT, with broad applications …
(IoT). Anomaly detection poses numerous challenges in IoT, with broad applications …
[HTML][HTML] Towards a machine learning-based framework for DDOS attack detection in software-defined IoT (SD-IoT) networks
Abstract The Internet of Things (IoT) is a complex and diverse network consisting of resource-
constrained sensors/devices/things that are vulnerable to various security threats …
constrained sensors/devices/things that are vulnerable to various security threats …
Evaluation of machine learning algorithms for intrusion detection system
Intrusion detection system (IDS) is one of the implemented solutions against harmful attacks.
Furthermore, attackers always keep changing their tools and techniques. However …
Furthermore, attackers always keep changing their tools and techniques. However …
Towards a deep learning-driven intrusion detection approach for Internet of Things
Abstract Internet of Things (IoT) as a paradigm comes with a range of benefits to humanity.
Domains of research for the IoT range from healthcare automation to energy and transport …
Domains of research for the IoT range from healthcare automation to energy and transport …
Deep learning-based intrusion detection for IoT networks
Internet of Things (IoT) has an immense potential for a plethora of applications ranging from
healthcare automation to defence networks and the power grid. The security of an IoT …
healthcare automation to defence networks and the power grid. The security of an IoT …
Cybersecurity deep: approaches, attacks dataset, and comparative study
Cyber attacks are increasing rapidly due to advanced digital technologies used by hackers.
In addition, cybercriminals are conducting cyber attacks, making cyber security a rapidly …
In addition, cybercriminals are conducting cyber attacks, making cyber security a rapidly …
[HTML][HTML] A survey on neural networks for (cyber-) security and (cyber-) security of neural networks
M Pawlicki, R Kozik, M Choraś - Neurocomputing, 2022 - Elsevier
The goal of this systematic and broad survey is to present and discuss the main challenges
that are posed by the implementation of Artificial Intelligence and Machine Learning in the …
that are posed by the implementation of Artificial Intelligence and Machine Learning in the …
Intrusion detection approach based on optimised artificial neural network
M Choraś, M Pawlicki - Neurocomputing, 2021 - Elsevier
Abstract Context and rationale Intrusion Detection, the ability to detect malware and other
attacks, is a crucial aspect to ensure cybersecurity. So is the ability to identify this myriad of …
attacks, is a crucial aspect to ensure cybersecurity. So is the ability to identify this myriad of …
[PDF][PDF] Detecting distributed denial of service attacks using data mining techniques
Users and organizations find it continuously challenging to deal with distributed denial of
service (DDoS) attacks.. The security engineer works to keep a service available at all times …
service (DDoS) attacks.. The security engineer works to keep a service available at all times …
Prediction of motion simulator signals using time-series neural networks
A motion cueing algorithm (MCA) is employed to transform the linear and angular motion
signals generated from a motion simulator without violating the physical and dynamical …
signals generated from a motion simulator without violating the physical and dynamical …