A one-dimensional convolutional neural network (1D-CNN) based deep learning system for network intrusion detection

EUH Qazi, A Almorjan, T Zia - Applied Sciences, 2022 - mdpi.com
The connectivity of devices through the internet plays a remarkable role in our daily lives.
Many network-based applications are utilized in different domains, eg, health care, smart …

Transformer-based attention network for in-vehicle intrusion detection

TP Nguyen, H Nam, D Kim - IEEE Access, 2023 - ieeexplore.ieee.org
Despite the significant advantages of communication systems between electronic control
units, the controller area network (CAN) protocol is vulnerable to attacks owing to its weak …

SAIDuCANT: Specification-based automotive intrusion detection using controller area network (CAN) timing

H Olufowobi, C Young, J Zambreno… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
The proliferation of embedded devices in modern vehicles has opened the traditionally-
closed vehicular system to the risk of cybersecurity attacks through physical and remote …

[HTML][HTML] Cyber attack detection for self-driving vehicle networks using deep autoencoder algorithms

FW Alsaade, MH Al-Adhaileh - Sensors, 2023 - mdpi.com
Connected and autonomous vehicles (CAVs) present exciting opportunities for the
improvement of both the mobility of people and the efficiency of transportation systems. The …

A novel wireless network intrusion detection method based on adaptive synthetic sampling and an improved convolutional neural network

Z Hu, L Wang, L Qi, Y Li, W Yang - IEEE Access, 2020 - ieeexplore.ieee.org
The diversity of network attacks poses severe challenges to intrusion detection systems
(IDSs). Traditional attack recognition methods usually adopt mining data associations to …

A network intrusion detection method based on deep multi-scale convolutional neural network

X Wang, S Yin, H Li, J Wang, L Teng - International Journal of Wireless …, 2020 - Springer
Network intrusion detection (NID) is an important method for network system administrators
to detect various security holes. The performance of traditional NID methods can be affected …

HDLNIDS: hybrid deep-learning-based network intrusion detection system

EUH Qazi, MH Faheem, T Zia - Applied Sciences, 2023 - mdpi.com
Attacks on networks are currently the most pressing issue confronting modern society.
Network risks affect all networks, from small to large. An intrusion detection system must be …

Efficient deep CNN-BiLSTM model for network intrusion detection

J Sinha, M Manollas - Proceedings of the 2020 3rd International …, 2020 - dl.acm.org
The need for Network Intrusion Detection systems has risen since usage of cloud
technologies has become mainstream. With the ever growing network traffic, Network …

Tree-based intelligent intrusion detection system in internet of vehicles

L Yang, A Moubayed, I Hamieh… - 2019 IEEE global …, 2019 - ieeexplore.ieee.org
The use of autonomous vehicles (AVs) is a promising technology in Intelligent
Transportation Systems (ITSs) to improve safety and driving efficiency. Vehicle-to-everything …

Unsupervised deep learning approach for network intrusion detection combining convolutional autoencoder and one-class SVM

A Binbusayyis, T Vaiyapuri - Applied Intelligence, 2021 - Springer
With the rapid advancement in network technologies, the need for cybersecurity has gained
increasing momentum in recent years. As a primary defense mechanism, an intrusion …