Multi-attribute decision-making for intrusion detection systems: A systematic review
Intrusion detection systems (IDSs) employ sophisticated security techniques to detect
malicious activities on hosts and/or networks. IDSs have been utilized to ensure the security …
malicious activities on hosts and/or networks. IDSs have been utilized to ensure the security …
Enhancing network intrusion detection using an ensemble voting classifier for internet of things
In the context of 6G technology, the Internet of Everything aims to create a vast network that
connects both humans and devices across multiple dimensions. The integration of smart …
connects both humans and devices across multiple dimensions. The integration of smart …
A Comprehensive Survey on Ensemble Machine Learning Approaches for Detection of Intrusion in IoT Networks
JJ Shirley, M Priya - 2023 International Conference on …, 2023 - ieeexplore.ieee.org
Threats to network security have been increasing leading to severe network attacks such
that a simple firewall will not be sufficient to deter challenging and complicated attacks …
that a simple firewall will not be sufficient to deter challenging and complicated attacks …
Feature selection methods simultaneously improve the detection accuracy and model building time of machine learning classifiers
S Alabdulwahab, BK Moon - Symmetry, 2020 - mdpi.com
The detection accuracy and model building time of machine learning (ML) classifiers are
vital aspects for an intrusion detection system (IDS) to predict attacks in real life. Recently …
vital aspects for an intrusion detection system (IDS) to predict attacks in real life. Recently …
[PDF][PDF] Review of feature selection methods for text classification
1. Background With the immense growth of online information due to Internet, text-
categorization has become a very significant technology to classify a large number of …
categorization has become a very significant technology to classify a large number of …
Detecting cyber attacks with high-frequency features using machine learning algorithms
In computer networks, intrusion detection systems are used to detect cyber-attacks and
anomalies. Feature selection is important for intrusion detection systems to scan the network …
anomalies. Feature selection is important for intrusion detection systems to scan the network …
Enhancing cloud-based security: a novel approach for efficient cyber-threat detection using GSCSO-IHNN model
Develo** a simple and efficient attack detection system for ensuring the security of cloud
systems against cyberthreats is a crucial and demanding process in the present time. In …
systems against cyberthreats is a crucial and demanding process in the present time. In …
Intrusion detection system with an ensemble learning and feature selection framework for IoT networks
ABSTRACT The Internet of Things (IoT) and its applications are currently the most popular
research areas. The properties of IoT are easily adapted to real-life applications but they …
research areas. The properties of IoT are easily adapted to real-life applications but they …
[PDF][PDF] An enhanced intrusion detection system based on multi-layer feature reduction for probe and dos attacks
Wireless network has an exponential increase in various aspects of the human community.
Accordingly, transmitting a vast volume of sensitive and non-sensitive data over the network …
Accordingly, transmitting a vast volume of sensitive and non-sensitive data over the network …
Multi-attribute overlap** radar working pattern recognition based on K-NN and SVM-BP
Y Liao, X Chen - The Journal of Supercomputing, 2021 - Springer
A recognition model named the SVM-NP is proposed in this paper to address the multi-
attribute overlap in radar working recognition. The model is based on the K-NN boundary …
attribute overlap in radar working recognition. The model is based on the K-NN boundary …