[HTML][HTML] Future of generative adversarial networks (GAN) for anomaly detection in network security: A review
Anomaly detection is crucial in various applications, particularly cybersecurity and network
intrusion. However, a common challenge across anomaly detection techniques is the …
intrusion. However, a common challenge across anomaly detection techniques is the …
A comprehensive review on deep learning algorithms: Security and privacy issues
Abstract Machine Learning (ML) algorithms are used to train the machines to perform
various complicated tasks that begin to modify and improve with experiences. It has become …
various complicated tasks that begin to modify and improve with experiences. It has become …
IoT intrusion detection using machine learning with a novel high performing feature selection method
The Internet of Things (IoT) ecosystem has experienced significant growth in data traffic and
consequently high dimensionality. Intrusion Detection Systems (IDSs) are essential self …
consequently high dimensionality. Intrusion Detection Systems (IDSs) are essential self …
An optimized CNN-based intrusion detection system for reducing risks in smart farming
Smart farming is a well-known and superior method of managing a farm, becoming more
prevalent in today's contemporary agricultural practices. Crops are monitored for their …
prevalent in today's contemporary agricultural practices. Crops are monitored for their …
NIDS-CNNLSTM: Network intrusion detection classification model based on deep learning
J Du, K Yang, Y Hu, L Jiang - IEEE Access, 2023 - ieeexplore.ieee.org
Intrusion detection is the core topic of network security, and the intrusion detection algorithm
based on deep learning has become a research hotspot in network security. In this paper, a …
based on deep learning has become a research hotspot in network security. In this paper, a …
Securing tomorrow: a comprehensive survey on the synergy of Artificial Intelligence and information security
This survey paper explores the transformative role of Artificial Intelligence (AI) in information
security. Traditional methods, especially rule-based approaches, faced significant …
security. Traditional methods, especially rule-based approaches, faced significant …
Machine learning-based intrusion detection system: an experimental comparison
Recently, networks are moving toward automation and getting more and more intelligent.
With the advent of big data and cloud computing technologies, lots and lots of data are being …
With the advent of big data and cloud computing technologies, lots and lots of data are being …
An imbalanced generative adversarial network-based approach for network intrusion detection in an imbalanced dataset
In modern networks, a Network Intrusion Detection System (NIDS) is a critical security device
for detecting unauthorized activity. The categorization effectiveness for minority classes is …
for detecting unauthorized activity. The categorization effectiveness for minority classes is …
Original Research Article Detection of Data imbalance in MANET network based on ADSY-AEAMBi-LSTM with DBO Feature selection
V Srinivasan, VH Raj, A Thirumalraj… - Journal of …, 2024 - jai.front-sci.com
Abstract A Mobile Ad Hoc Network (MANET) is a temporary wireless network formed by
mobile nodes. These nodes cooperate to relay information in a multi-hop fashion, but some …
mobile nodes. These nodes cooperate to relay information in a multi-hop fashion, but some …
Apelid: Enhancing real-time intrusion detection with augmented wgan and parallel ensemble learning
This paper proposes an AI-powered intrusion detection method that improves intrusion
detection performance by increasing the quality of the training set and employing numerous …
detection performance by increasing the quality of the training set and employing numerous …