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 …
area. An intrusion detection system (IDS) which is an important cyber security technique …
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 …
LUCID: A practical, lightweight deep learning solution for DDoS attack detection
Distributed Denial of Service (DDoS) attacks are one of the most harmful threats in today's
Internet, disrupting the availability of essential services. The challenge of DDoS detection is …
Internet, disrupting the availability of essential services. The challenge of DDoS detection is …
A novel intrusion detection model for a massive network using convolutional neural networks
K Wu, Z Chen, W Li - Ieee Access, 2018 - ieeexplore.ieee.org
More and more network traffic data have brought great challenge to traditional intrusion
detection system. The detection performance is tightly related to selected features and …
detection system. The detection performance is tightly related to selected features and …
Deep learning in IoT intrusion detection
Abstract The Internet of Things (IoT) is the new paradigm of our times, where smart devices
and sensors from across the globe are interconnected in a global grid, and distributed …
and sensors from across the globe are interconnected in a global grid, and distributed …
An unsupervised deep learning model for early network traffic anomaly detection
Various attacks have emerged as the major threats to the success of a connected world like
the Internet of Things (IoT), in which billions of devices interact with each other to facilitate …
the Internet of Things (IoT), in which billions of devices interact with each other to facilitate …
A method of few-shot network intrusion detection based on meta-learning framework
C Xu, J Shen, X Du - IEEE Transactions on Information …, 2020 - ieeexplore.ieee.org
Conventional intrusion detection systems based on supervised learning techniques require
a large number of samples for training, while in some scenarios, such as zero-day attacks …
a large number of samples for training, while in some scenarios, such as zero-day attacks …
Machine learning for encrypted malicious traffic detection: Approaches, datasets and comparative study
As people's demand for personal privacy and data security becomes a priority, encrypted
traffic has become mainstream in the cyber world. However, traffic encryption is also …
traffic has become mainstream in the cyber world. However, traffic encryption is also …
Detection of power grid disturbances and cyber-attacks based on machine learning
Modern intelligent power grid provides an efficient way of managing energy supply and
consumption while facing numerous security threats at the same time. Both natural and man …
consumption while facing numerous security threats at the same time. Both natural and man …
Intrusion detection system for internet of things based on temporal convolution neural network and efficient feature engineering
A Derhab, A Aldweesh, AZ Emam… - … and Mobile Computing, 2020 - Wiley Online Library
In the era of the Internet of Things (IoT), connected objects produce an enormous amount of
data traffic that feed big data analytics, which could be used in discovering unseen patterns …
data traffic that feed big data analytics, which could be used in discovering unseen patterns …