Over the past few years, Internet applications have become more advanced and widely
used. This has increased the need for Internet networks to be secured. Intrusion detection …

The increasing trend toward using the Internet of Things (IoT) increased the number of
intrusions and intruders annually. Hence, the integration, confidentiality, and access to …

Purpose The primary aim of this study is to detail the use of soft computing techniques in
business and management research. Its objectives are as follows: to conduct a …

IoT devices generate enormous amounts of data, which deep learning algorithms can learn
from more effectively than shallow learning algorithms. The approach for threat detection …

The proposed work objective is to adopt the non-dominated sorting genetic algorithm II
(NSGA-II), a type of MOEA (multi-objective evolutionary algorithms), to reduce the …

Industrial control systems (ICSs) are facing serious and evolving security threats because of
a variety of malicious attacks. Deep learning-based intrusion detection systems (IDSs) have …

This study presents a comprehensive exploration of the hyperparameter optimization in one-
dimensional (1D) convolutional neural networks (CNNs) for network intrusion detection. The …

Cyber Loopholes in smart devices' applications invited intruders to conduct malicious
activities. The growing quantity and diversity of smart devices has posed significant cyber …

Inspired by the biological evolution, this paper proposes an evolutionary synthesis
mechanism to automatically evolve DenseNet towards high sparsity and efficiency for …

In a big data environment, data streams are sequences of dynamically changing data with
unlimited length; they are often associated with concept drift, caused by data distribution …