Anomaly detection based on deep learning: Insights and opportunities
H Zhang, R Xie, K Li, W Huang… - 2023 IEEE 10th …, 2023 - ieeexplore.ieee.org
With the advent of the 5G/6G and Big Data, the network has become indispensable in
people's lives, and Cyber security has turned a relevant topic that people pay attention to …
people's lives, and Cyber security has turned a relevant topic that people pay attention to …
[PDF][PDF] Improved LSTM-Based Anomaly Detection Model with Cybertwin Deep Learning to Detect Cutting-Edge Cybersecurity Attacks
S Sengan, A Mehbodniya, JL Webber… - HUMAN-CENTRIC …, 2023 - hcisj.com
Anomalies in the time series may indicate future faults—real-time system state monitoring
and early alerting demand novel computational anomaly detection methods. Internet of …
and early alerting demand novel computational anomaly detection methods. Internet of …
An ensemble of prediction and learning mechanism for improving accuracy of anomaly detection in network intrusion environments
The connectivity of our surrounding objects to the internet plays a tremendous role in our
daily lives. Many network applications have been developed in every domain of life …
daily lives. Many network applications have been developed in every domain of life …
Deep learning-based network anomaly detection and classification in an imbalanced cloud environment
AD Vibhute, V Nakum - Procedia Computer Science, 2024 - Elsevier
With the advancements in computer networking, communication between end-to-end
systems has increased drastically. However, security issues have also been raised. Thus …
systems has increased drastically. However, security issues have also been raised. Thus …
Novel Machine Learning Technique for Intrusion Detection in Recent Network-based Attacks
A Srivastava, A Agarwal, G Kaur - 2019 4th International …, 2019 - ieeexplore.ieee.org
Intrusion Detection is a vastly growing area. Traditionally supervised learning techniques
were used for detecting intrusions in the network traffic data. But nowadays not only the rate …
were used for detecting intrusions in the network traffic data. But nowadays not only the rate …
[PDF][PDF] Anomaly-based intrusion detection through k-means clustering and naives bayes classification
Intrusion detection systems (IDSs) effectively balance extra security appliance by identifying
intrusive activities on a computer system, and their enhancement is emerging at an …
intrusive activities on a computer system, and their enhancement is emerging at an …
Review of anomaly detection systems in industrial control systems using deep feature learning approach
R Kabore, A Kouassi, R N'goran, O Asseu… - …, 2021 - imt-atlantique.hal.science
Industrial Control Systems (ICS) or SCADA networks are increasingly targeted by cyber-
attacks as their architectures shifted from proprietary hardware, software and protocols to …
attacks as their architectures shifted from proprietary hardware, software and protocols to …
An in-depth experimental study of anomaly detection using gradient boosted machine
BA Tama, KH Rhee - Neural Computing and Applications, 2019 - Springer
This paper proposes an improved detection performance of anomaly-based intrusion
detection system (IDS) using gradient boosted machine (GBM). The best parameters of GBM …
detection system (IDS) using gradient boosted machine (GBM). The best parameters of GBM …
PSO-driven feature selection and hybrid ensemble for network anomaly detection
As a system capable of monitoring and evaluating illegitimate network access, an intrusion
detection system (IDS) profoundly impacts information security research. Since machine …
detection system (IDS) profoundly impacts information security research. Since machine …
Effectively predicting cyber‐attacks through isolation forest learning‐based outlier detection
Due to the popularity of Internet of Things devices, the exponential progress of computer
networks, and a plethora of associated applications, cybersecurity has recently attracted …
networks, and a plethora of associated applications, cybersecurity has recently attracted …