Aesmote: Adversarial reinforcement learning with smote for anomaly detection
X Ma, W Shi - IEEE Transactions on Network Science and …, 2020 - ieeexplore.ieee.org
Intrusion Detection Systems (IDSs) play a vital role in securing today's Data-Centric
Networks. In a dynamic environment such as the Internet of Things (IoT), which is vulnerable …
Networks. In a dynamic environment such as the Internet of Things (IoT), which is vulnerable …
Deep reinforcement learning for anomaly detection: A systematic review
Anomaly detection has been used to detect and analyze anomalous elements from data for
years. Various techniques have been developed to detect anomalies. However, the most …
years. Various techniques have been developed to detect anomalies. However, the most …
MOCCA: Multilayer one-class classification for anomaly detection
Anomalies are ubiquitous in all scientific fields and can express an unexpected event due to
incomplete knowledge about the data distribution or an unknown process that suddenly …
incomplete knowledge about the data distribution or an unknown process that suddenly …
Adversarial discriminative attention for robust anomaly detection
Existing methods for visual anomaly detection predominantly rely on global level pixel
comparisons for anomaly score computation without emphasizing on unique local features …
comparisons for anomaly score computation without emphasizing on unique local features …
Old is gold: Redefining the adversarially learned one-class classifier training paradigm
A popular method for anomaly detection is to use the generator of an adversarial network to
formulate anomaly score over reconstruction loss of input. Due to the rare occurrence of …
formulate anomaly score over reconstruction loss of input. Due to the rare occurrence of …
Integrating prediction and reconstruction for anomaly detection
Anomaly detection in videos refers to identifying events that rarely or shouldn't happen in a
certain context. Among all existing methods, the idea of reconstruction or future frame …
certain context. Among all existing methods, the idea of reconstruction or future frame …
Toward deep supervised anomaly detection: Reinforcement learning from partially labeled anomaly data
We consider the problem of anomaly detection with a small set of partially labeled anomaly
examples and a large-scale unlabeled dataset. This is a common scenario in many …
examples and a large-scale unlabeled dataset. This is a common scenario in many …
Toward supervised anomaly detection
Anomaly detection is being regarded as an unsupervised learning task as anomalies stem
from adversarial or unlikely events with unknown distributions. However, the predictive …
from adversarial or unlikely events with unknown distributions. However, the predictive …
A deep reinforcement learning approach for anomaly network intrusion detection system
YF Hsu, M Matsuoka - 2020 IEEE 9th international conference …, 2020 - ieeexplore.ieee.org
Network intrusion detection systems (NIDS) are essential for organizations to ensure the
safety and security of their communication and information. In this paper, we propose a deep …
safety and security of their communication and information. In this paper, we propose a deep …
Graph regularized autoencoder and its application in unsupervised anomaly detection
Dimensionality reduction is a crucial first step for many unsupervised learning tasks
including anomaly detection and clustering. Autoencoder is a popular mechanism to …
including anomaly detection and clustering. Autoencoder is a popular mechanism to …