Advancing cybersecurity: a comprehensive review of AI-driven detection techniques
As the number and cleverness of cyber-attacks keep increasing rapidly, it's more important
than ever to have good ways to detect and prevent them. Recognizing cyber threats quickly …
than ever to have good ways to detect and prevent them. Recognizing cyber threats quickly …
[HTML][HTML] A metaheuristic-based ensemble feature selection framework for cyber threat detection in IoT-enabled networks
Abstract Internet of Things (IoT) enabled networks are highly vulnerable to cyber threats due
to insecure wireless communication, resource constraint architecture, different types of IoT …
to insecure wireless communication, resource constraint architecture, different types of IoT …
[HTML][HTML] Multi-objective optimization algorithms for intrusion detection in IoT networks: A systematic review
The significance of intrusion detection systems in networks has grown because of the digital
revolution and increased operations. The intrusion detection method classifies the network …
revolution and increased operations. The intrusion detection method classifies the network …
A hybrid approach for efficient feature selection in anomaly intrusion detection for IoT networks
The exponential growth of Internet of Things (IoT) devices underscores the need for robust
security measures against cyber-attacks. Extensive research in the IoT security community …
security measures against cyber-attacks. Extensive research in the IoT security community …
A novel feature-selection algorithm in IoT networks for intrusion detection
The Internet of Things (IoT) and network-enabled smart devices are crucial to the digitally
interconnected society of the present day. However, the increased reliance on IoT devices …
interconnected society of the present day. However, the increased reliance on IoT devices …
Feature selection in intrusion detection systems: a new hybrid fusion of Bat algorithm and Residue Number System
This research introduces innovative approaches to enhance intrusion detection systems
(IDSs) by addressing critical challenges in existing methods. Various machine-learning …
(IDSs) by addressing critical challenges in existing methods. Various machine-learning …
RNA-Seq analysis for breast cancer detection: A study on paired tissue samples using hybrid optimization and deep learning techniques
Problem Breast cancer is a leading global health issue, contributing to high mortality rates
among women. The challenge of early detection is exacerbated by the high dimensionality …
among women. The challenge of early detection is exacerbated by the high dimensionality …
DDoS attacks detection based on machine learning algorithms in IoT environments
In today's digital era, most electrical gadgets have become smart, and the great majority of
them can connect to the internet. The Internet of Things (IoT) refers to a network comprised …
them can connect to the internet. The Internet of Things (IoT) refers to a network comprised …
[HTML][HTML] Defensive strategies against PCC attacks based on ideal (t, n)-secret sharing scheme
We present a method to increase the dependability of cloud-based applications. Traditional
Secret Sharing Schemes (SSSs) typically fail to counter the challenges brought on by …
Secret Sharing Schemes (SSSs) typically fail to counter the challenges brought on by …
Enhanced Grey Wolf Optimization (EGWO) and random forest based mechanism for intrusion detection in IoT networks
Smart devices are enabled via the Internet of Things (IoT) and are connected in an
uninterrupted world. These connected devices pose a challenge to cybersecurity systems …
uninterrupted world. These connected devices pose a challenge to cybersecurity systems …