Generalized out-of-distribution detection: A survey
Abstract Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of
machine learning systems. For instance, in autonomous driving, we would like the driving …
machine learning systems. For instance, in autonomous driving, we would like the driving …
[HTML][HTML] DCNNBiLSTM: An efficient hybrid deep learning-based intrusion detection system
In recent years, all real-world processes have been shifted to the cyber environment
practically, and computers communicate with one another over the Internet. As a result, there …
practically, and computers communicate with one another over the Internet. As a result, there …
Application of deep reinforcement learning to intrusion detection for supervised problems
M Lopez-Martin, B Carro… - Expert Systems with …, 2020 - Elsevier
The application of new techniques to increase the performance of intrusion detection
systems is crucial in modern data networks with a growing threat of cyber-attacks. These …
systems is crucial in modern data networks with a growing threat of cyber-attacks. These …
Anomaly-based intrusion detection from network flow features using variational autoencoder
S Zavrak, M Iskefiyeli - IEEE Access, 2020 - ieeexplore.ieee.org
The rapid increase in network traffic has recently led to the importance of flow-based
intrusion detection systems processing a small amount of traffic data. Furthermore, anomaly …
intrusion detection systems processing a small amount of traffic data. Furthermore, anomaly …
[HTML][HTML] A deep encoder-decoder network for anomaly detection in driving trajectory behavior under spatio-temporal context
Traditional trajectory anomaly detection aims to find abnormal trajectory points or sequences
using data mining techniques. As a comparison, we focus on the evaluation of the …
using data mining techniques. As a comparison, we focus on the evaluation of the …
Unsupervised anomaly detection with LSTM autoencoders using statistical data-filtering
To address one of the most challenging industry problems, we develop an enhanced
training algorithm for anomaly detection in unlabelled sequential data such as time-series …
training algorithm for anomaly detection in unlabelled sequential data such as time-series …
Metamorphic malware and obfuscation: a survey of techniques, variants, and generation kits
K Brezinski, K Ferens - Security and Communication Networks, 2023 - Wiley Online Library
The competing landscape between malware authors and security analysts is an ever‐
changing battlefield over who can innovate over the other. While security analysts are …
changing battlefield over who can innovate over the other. While security analysts are …
LSTM-Markov based efficient anomaly detection algorithm for IoT environment
V Shanmuganathan, A Suresh - Applied Soft Computing, 2023 - Elsevier
The seamless integration of wireless and IoT device in normal day to day life and for smart
homes has enabled more security and privacy needs. The attack or unauthorized access to …
homes has enabled more security and privacy needs. The attack or unauthorized access to …
A real-time adaptive model for bearing fault classification and remaining useful life estimation using deep neural network
Rolling element bearings are essential components of a wide variety of industrial machinery
and the leading cause of equipment failure. The prediction of Remaining Useful Life (RUL) …
and the leading cause of equipment failure. The prediction of Remaining Useful Life (RUL) …
Fraud detection using machine learning and deep learning
P Raghavan, N El Gayar - 2019 international conference on …, 2019 - ieeexplore.ieee.org
Frauds are known to be dynamic and have no patterns, hence they are not easy to identify.
Fraudsters use recent technological advancements to their advantage. They somehow …
Fraudsters use recent technological advancements to their advantage. They somehow …