[HTML][HTML] Artificial intelligence for cybersecurity: Literature review and future research directions
R Kaur, D Gabrijelčič, T Klobučar - Information Fusion, 2023 - Elsevier
Artificial intelligence (AI) is a powerful technology that helps cybersecurity teams automate
repetitive tasks, accelerate threat detection and response, and improve the accuracy of their …
repetitive tasks, accelerate threat detection and response, and improve the accuracy of their …
Surface defect detection methods for industrial products with imbalanced samples: A review of progress in the 2020s
Industrial products typically lack defects in smart manufacturing systems, which leads to an
extremely imbalanced task of recognizing surface defects. With this imbalanced sample …
extremely imbalanced task of recognizing surface defects. With this imbalanced sample …
Dual-IDS: A bagging-based gradient boosting decision tree model for network anomaly intrusion detection system
The mission of an intrusion detection system (IDS) is to monitor network activities and
assess whether or not they are malevolent. Specifically, anomaly-based IDS can discover …
assess whether or not they are malevolent. Specifically, anomaly-based IDS can discover …
Anomaly detection in 6G networks using machine learning methods
While the cloudification of networks with a micro-services-oriented design is a well-known
feature of 5G, the 6G era of networks is closely related to intelligent network orchestration …
feature of 5G, the 6G era of networks is closely related to intelligent network orchestration …
Synthetic attack data generation model applying generative adversarial network for intrusion detection
Detecting a large number of attack classes accurately applying machine learning (ML) and
deep learning (DL) techniques depends on the number of representative samples available …
deep learning (DL) techniques depends on the number of representative samples available …
Addressing the class imbalance problem in network intrusion detection systems using data resampling and deep learning
A Abdelkhalek, M Mashaly - The journal of Supercomputing, 2023 - Springer
Network intrusion detection systems (NIDS) are the most common tool used to detect
malicious attacks on a network. They help prevent the ever-increasing different attacks and …
malicious attacks on a network. They help prevent the ever-increasing different attacks and …
Advanced feature-selection-based hybrid ensemble learning algorithms for network intrusion detection systems
As cyber-attacks become remarkably sophisticated, effective Intrusion Detection Systems
(IDSs) are needed to monitor computer resources and to provide alerts regarding unusual or …
(IDSs) are needed to monitor computer resources and to provide alerts regarding unusual or …
MEMBER: A multi-task learning model with hybrid deep features for network intrusion detection
With the continuous occurrence of cybersecurity incidents, network intrusion detection has
become one of the most critical issues in cyber ecosystems. Although previous machine …
become one of the most critical issues in cyber ecosystems. Although previous machine …
Design of an intrusion detection model for IoT-enabled smart home
Machine learning (ML) provides effective solutions to develop efficient intrusion detection
system (IDS) for various environments. In the present paper, a diversified study of various …
system (IDS) for various environments. In the present paper, a diversified study of various …
MLTs-ADCNs: Machine learning techniques for anomaly detection in communication networks
From a security perspective, the research of the jeopardized 6G wireless communications
and its expected ultra-densified ubiquitous wireless networks urge the development of a …
and its expected ultra-densified ubiquitous wireless networks urge the development of a …