G-ids: Generative adversarial networks assisted intrusion detection system
The boundaries of cyber-physical systems (CPS) and the Internet of Things (IoT) are
converging together day by day to introduce a common platform on hybrid systems …
converging together day by day to introduce a common platform on hybrid systems …
Generative adversarial networks for distributed intrusion detection in the internet of things
A Ferdowsi, W Saad - 2019 IEEE Global Communications …, 2019 - ieeexplore.ieee.org
To reap the benefits of the Internet of Things (IoT), it is imperative to secure the system
against cyber attacks in order to enable mission critical and real-time applications. To this …
against cyber attacks in order to enable mission critical and real-time applications. To this …
Addressing imbalanced data problem with generative adversarial network for intrusion detection
Machine learning techniques help to understand underlying patterns in datasets to develop
defense mechanisms against cyber attacks. Multilayer Perceptron (MLP) technique is a …
defense mechanisms against cyber attacks. Multilayer Perceptron (MLP) technique is a …
A comprehensive survey of generative adversarial networks (GANs) in cybersecurity intrusion detection
Generative Adversarial Networks (GANs) have seen significant interest since their
introduction in 2014. While originally focused primarily on image-based tasks, their capacity …
introduction in 2014. While originally focused primarily on image-based tasks, their capacity …
Model evasion attack on intrusion detection systems using adversarial machine learning
Intrusion Detection Systems (IDS) have a long history as an effective network defensive
mechanism. The systems alert defenders of suspicious and/or malicious behavior detected …
mechanism. The systems alert defenders of suspicious and/or malicious behavior detected …
Deep adversarial learning in intrusion detection: A data augmentation enhanced framework
Intrusion detection systems (IDSs) play an important role in identifying malicious attacks and
threats in networking systems. As fundamental tools of IDSs, learning based classification …
threats in networking systems. As fundamental tools of IDSs, learning based classification …
Network intrusion detection based on conditional Wasserstein generative adversarial network and cost-sensitive stacked autoencoder
G Zhang, X Wang, R Li, Y Song, J He, J Lai - IEEE access, 2020 - ieeexplore.ieee.org
In the field of intrusion detection, there is often a problem of data imbalance, and more and
more unknown types of attacks make detection difficult. To resolve above issues, this article …
more unknown types of attacks make detection difficult. To resolve above issues, this article …
Generative adversarial networks for launching and thwarting adversarial attacks on network intrusion detection systems
Intrusion detection systems (IDSs) are an essential cog of the network security suite that can
defend the network from malicious intrusions and anomalous traffic. Many machine learning …
defend the network from malicious intrusions and anomalous traffic. Many machine learning …
Unveiling the potential of graph neural networks for robust intrusion detection
D Pujol-Perich, J Suárez-Varela… - ACM SIGMETRICS …, 2022 - dl.acm.org
The last few years have seen an increasing wave of attacks with serious economic and
privacy damages, which evinces the need for accurate Network Intrusion Detection Systems …
privacy damages, which evinces the need for accurate Network Intrusion Detection Systems …
An enhanced AI-based network intrusion detection system using generative adversarial networks
C Park, J Lee, Y Kim, JG Park, H Kim… - IEEE Internet of Things …, 2022 - ieeexplore.ieee.org
As communication technology advances, various and heterogeneous data are
communicated in distributed environments through network systems. Meanwhile, along with …
communicated in distributed environments through network systems. Meanwhile, along with …