Deep learning for anomaly detection: A review
Anomaly detection, aka outlier detection or novelty detection, has been a lasting yet active
research area in various research communities for several decades. There are still some …
research area in various research communities for several decades. There are still some …
Anomaly detection based on zero-shot outlier synthesis and hierarchical feature distillation
Anomaly detection suffers from unbalanced data since anomalies are quite rare.
Synthetically generated anomalies are a solution to such ill or not fully defined data …
Synthetically generated anomalies are a solution to such ill or not fully defined data …
Robust variational autoencoders for outlier detection and repair of mixed-type data
S Eduardo, A Nazábal, CKI Williams… - International …, 2020 - proceedings.mlr.press
We focus on the problem of unsupervised cell outlier detection and repair inmixed-type
tabular data. Traditional methods are concerned only with detecting which rows in the …
tabular data. Traditional methods are concerned only with detecting which rows in the …
Combining OC-SVMs with LSTM for detecting anomalies in telemetry data with irregular intervals
J Wu, L Yao, B Liu, Z Ding, L Zhang - IEEE Access, 2020 - ieeexplore.ieee.org
To ensure the safety and stability of spacecrafts of which thousands of telemetry parameters
are monitored, fast and accurate response to anomalies or potential hazards is very …
are monitored, fast and accurate response to anomalies or potential hazards is very …
Comparison of network intrusion detection performance using feature representation
Intrusion detection is essential for the security of the components of any network. For that
reason, several strategies can be used in Intrusion Detection Systems (IDS) to identify the …
reason, several strategies can be used in Intrusion Detection Systems (IDS) to identify the …
Trustworthy anomaly detection: A survey
Anomaly detection has a wide range of real-world applications, such as bank fraud detection
and cyber intrusion detection. In the past decade, a variety of anomaly detection models …
and cyber intrusion detection. In the past decade, a variety of anomaly detection models …
Cybersecurity anomaly detection in adversarial environments
DA Bierbrauer, A Chang, W Kritzer… - arXiv preprint arXiv …, 2021 - arxiv.org
The proliferation of interconnected battlefield information-sharing devices, known as the
Internet of Battlefield Things (IoBT), introduced several security challenges. Inherent to the …
Internet of Battlefield Things (IoBT), introduced several security challenges. Inherent to the …
Explaining anomalies using denoising autoencoders for financial tabular data
Recent advances in Explainable AI (XAI) increased the demand for deployment of safe and
interpretable AI models in various industry sectors. Despite the latest success of deep neural …
interpretable AI models in various industry sectors. Despite the latest success of deep neural …
FADngs: Federated Learning for Anomaly Detection
With the increasing demand for data privacy, federated learning (FL) has gained popularity
for various applications. Most existing FL works focus on the classification task, overlooking …
for various applications. Most existing FL works focus on the classification task, overlooking …
Enhancing Traffic Flow Prediction using Outlier-Weighted AutoEncoders: Handling Real-Time Changes
H Choudhary, M Hassani - arXiv preprint arXiv:2312.16596, 2023 - arxiv.org
In today's urban landscape, traffic congestion poses a critical challenge, especially during
outlier scenarios. These outliers can indicate abrupt traffic peaks, drops, or irregular trends …
outlier scenarios. These outliers can indicate abrupt traffic peaks, drops, or irregular trends …