Improving Extreme Learning Machine Accuracy Utilizing Genetic Algorithm for Intrusion Detection Purposes

Network Intrusion Detection Model Based on Improved BYOL Self-Supervised Learning

“…For example, suppose the original array is [9,6,7,2,4,5,1,3]. Table 2 shows how the modern version of the Fisher-Yates algorithm performs the shuffle operation on this array.…”

“…Given q, R h , and R w , the content-position encoding of the image can be calculated from formula (6) as follows: content position…”

“…NIDS observes and analyses real-time network traffic and monitors multiple hosts, aiming to detect intrusions in the network by collecting packet information and viewing its contents [5]. Previous researchers have mostly used pattern-matching algorithms to analyse their data, and feature selection usually includes three schemes, which are filtered approaches (e.g., information gain and correlation coefficient algorithms), encapsulated methods (e.g., genetic algorithms [6] and particle swarm algorithms [7]), embedded methods (e.g., LASSO regression algorithms), and linear transformation methods for feature extraction, such as principal component analysis (PCA) and linear discriminant analysis, as well as nonlinear transformation methods, like kernel-based principal component analysis. However, all of the above methods have certain drawbacks.…”