Adversarial machine learning for network intrusion detection systems: A comprehensive survey
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 …
network attacks that compromise the security of the data, systems, and networks. In recent …
Analysis of cyber security attacks and its solutions for the smart grid using machine learning and blockchain methods
Smart grids are rapidly replacing conventional networks on a worldwide scale. A smart grid
has drawbacks, just like any other novel technology. A smart grid cyberattack is one of the …
has drawbacks, just like any other novel technology. A smart grid cyberattack is one of the …
Hybrid intrusion detection using mapreduce based black widow optimized convolutional long short-term memory neural networks
PR Kanna, P Santhi - Expert Systems with Applications, 2022 - Elsevier
The recent advancements in information and communication technologies have led to an
increasing number of online systems and services. These online systems can utilize …
increasing number of online systems and services. These online systems can utilize …
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 …
continue to develop new types of attacks and the size of networks continues to grow, the …
Comparative research on network intrusion detection methods based on machine learning
C Zhang, D Jia, L Wang, W Wang, F Liu, A Yang - Computers & Security, 2022 - Elsevier
Network intrusion detection system is an essential part of network security research. It
detects intrusion behaviors through active defense technology and takes emergency …
detects intrusion behaviors through active defense technology and takes emergency …
Anomaly based network intrusion detection for IoT attacks using deep learning technique
Abstract Internet of Things (IoT) applications are growing in popularity for being widely used
in many real-world services. In an IoT ecosystem, many devices are connected with each …
in many real-world services. In an IoT ecosystem, many devices are connected with each …
STL-HDL: A new hybrid network intrusion detection system for imbalanced dataset on big data environment
The ability to process large amounts of data in real time using big data analytics tools brings
many advantages that can be used in intrusion detection systems. Deep learning …
many advantages that can be used in intrusion detection systems. Deep learning …
Network abnormal traffic detection model based on semi-supervised deep reinforcement learning
The rapid development of Internet technology has brought great convenience to our
production life, and the ensuing security problems have become increasingly prominent …
production life, and the ensuing security problems have become increasingly prominent …
Explainable artificial intelligence for intrusion detection in IoT networks: A deep learning based approach
Abstract The Internet of Things (IoT) is currently seeing tremendous growth due to new
technologies and big data. Research in the field of IoT security is an emerging topic. IoT …
technologies and big data. Research in the field of IoT security is an emerging topic. IoT …
A survey of CNN-based network intrusion detection
Over the past few years, Internet applications have become more advanced and widely
used. This has increased the need for Internet networks to be secured. Intrusion detection …
used. This has increased the need for Internet networks to be secured. Intrusion detection …