CNN-LSTM: hybrid deep neural network for network intrusion detection system

A Halbouni, TS Gunawan, MH Habaebi… - IEEE …, 2022 - ieeexplore.ieee.org
Network security becomes indispensable to our daily interactions and networks. As attackers
continue to develop new types of attacks and the size of networks continues to grow, the …

[HTML][HTML] can-train-and-test: A Curated CAN Dataset for Automotive Intrusion Detection

B Lampe, W Meng - Computers & Security, 2024 - Elsevier
When it comes to in-vehicle networks (IVNs), the controller area network (CAN) bus
dominates the market; automobiles manufactured and sold worldwide depend on the CAN …

Deep learning based network intrusion detection system for resource-constrained environments

S Rizvi, M Scanlon, J McGibney, J Sheppard - International Conference on …, 2022 - Springer
Network intrusion detection systems (IDS) examine network packets and alert system
administrators and investigators to low-level security violations. In large networks, these …

Wireless network intrusion detection based on improved convolutional neural network

H Yang, F Wang - Ieee Access, 2019 - ieeexplore.ieee.org
The diversification of wireless network traffic attack characteristics has led to the problems
what traditional intrusion detection technology with high false positive rate, low detection …

Recent advances in machine-learning driven intrusion detection in transportation: Survey

H Bangui, B Buhnova - Procedia Computer Science, 2021 - Elsevier
Abstract Rapid developments in Intelligent Transportation Systems (ITSs) have emerged as
a new research field for building sustainable smart cities. VANET (vehicular ad hoc network) …

Network intrusion detection system: A systematic study of machine learning and deep learning approaches

Z Ahmad, A Shahid Khan, C Wai Shiang… - Transactions on …, 2021 - Wiley Online Library
The rapid advances in the internet and communication fields have resulted in a huge
increase in the network size and the corresponding data. As a result, many novel attacks are …

Long short-term memory neural network-based attack detection model for in-vehicle network security

Z Khan, M Chowdhury, M Islam, CY Huang… - IEEE Sensors …, 2020 - ieeexplore.ieee.org
In this letter, we create two types of attacks to investigate in-vehicle network security: replay
attack and amplitude-shift attack. We use these two attacks to create attack datasets from two …

Network intrusion detection combined hybrid sampling with deep hierarchical network

K Jiang, W Wang, A Wang, H Wu - IEEE access, 2020 - ieeexplore.ieee.org
Intrusion detection system (IDS) plays an important role in network security by discovering
and preventing malicious activities. Due to the complex and time-varying network …

An intrusion detection model based on feature reduction and convolutional neural networks

Y Xiao, C Xing, T Zhang, Z Zhao - IEEE Access, 2019 - ieeexplore.ieee.org
With the popularity and development of network technology and the Internet, intrusion
detection systems (IDSs), which can identify attacks, have been developed. Traditional …

Deep learning approach for intelligent intrusion detection system

R Vinayakumar, M Alazab, KP Soman… - Ieee …, 2019 - ieeexplore.ieee.org
Machine learning techniques are being widely used to develop an intrusion detection
system (IDS) for detecting and classifying cyberattacks at the network-level and the host …