Advances in nature-inspired metaheuristic optimization for feature selection problem: A comprehensive survey
The main objective of feature selection is to improve learning performance by selecting
concise and informative feature subsets, which presents a challenging task for machine …
concise and informative feature subsets, which presents a challenging task for machine …
Intrusion detection system for large-scale IoT NetFlow networks using machine learning with modified Arithmetic Optimization Algorithm
With the rapid expansion of Internet of Things (IoT) networks, the need for robust security
measures to detect and report potential threats is becoming more urgent. In this paper, we …
measures to detect and report potential threats is becoming more urgent. In this paper, we …
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 improved PIO feature selection algorithm for IoT network intrusion detection system based on ensemble learning
With the rapid growth of the number of connected devices that exchange personal, sensitive,
and important data through the IoT based global network, attacks that are targeting security …
and important data through the IoT based global network, attacks that are targeting security …
A novel optimization based deep learning with artificial intelligence approach to detect intrusion attack in network system
Modern life is increasingly influenced by networks, making cybersecurity a crucial area of
study. However, due to their few resources and varied makeup, they are more vulnerable to …
study. However, due to their few resources and varied makeup, they are more vulnerable to …
Boosting chameleon swarm algorithm with consumption AEO operator for global optimization and feature selection
Feature selection (FS) plays a crucial role as a pre-processing tool in data mining, especially
for real-world applications in medical fields; it has been utilized exponentially and becomes …
for real-world applications in medical fields; it has been utilized exponentially and becomes …
A new meta-heuristics data clustering algorithm based on tabu search and adaptive search memory
Y Alotaibi - Symmetry, 2022 - mdpi.com
Clustering is a popular data analysis and data mining problem. Symmetry can be
considered as a pre-attentive feature, which can improve shapes and objects, as well as …
considered as a pre-attentive feature, which can improve shapes and objects, as well as …
A novel firefly algorithm approach for efficient feature selection with COVID-19 dataset
Feature selection is one of the most important challenges in machine learning and data
science. This process is usually performed in the data preprocessing phase, where the data …
science. This process is usually performed in the data preprocessing phase, where the data …
Breast cancer classification using Deep Q Learning (DQL) and gorilla troops optimization (GTO)
Breast cancer (BC) is a primary reason for death among the female population around the
world. Early identification can aid in decreasing the mortality rates associated with this …
world. Early identification can aid in decreasing the mortality rates associated with this …
Shielding networks: enhancing intrusion detection with hybrid feature selection and stack ensemble learning
The frequent usage of computer networks and the Internet has made computer networks
vulnerable to numerous attacks, highlighting the critical need to enhance the precision of …
vulnerable to numerous attacks, highlighting the critical need to enhance the precision of …