In recent years, an enormous amount of research has been carried out on support vector
machines (SVMs) and their application in several fields of science. SVMs are one of the …

Support vector machines (SVMs) are a supervised classifier successfully applied in a
plethora of real-life applications. However, they suffer from the important shortcomings of …

Intrusion detection has become essential to network security because of the increasing
connectivity between computers. Several intrusion detection systems have been developed …

In the recent few decades there has been very significant developments in the theoretical
understanding of Support vector machines (SVMs) as well as algorithmic strategies for …

Quantum Machine Learning bridges the gap between abstract developments in quantum
computing and the applied research on machine learning. Paring down the complexity of the …

Data Mining: Concepts and Techniques, Fourth Edition introduces concepts, principles, and
methods for mining patterns, knowledge, and models from various kinds of data for diverse …

Finding useful patterns in large datasets has attracted considerable interest recently, and
one of the most widely studied problems in this area is the identification of clusters, or …

Standard SVM training has O (m3) time and O (m2) space complexities, where m is the
training set size. It is thus computationally infeasible on very large data sets. By observing …

As the network-based technologies become omnipresent, threat detection and prevention
for these systems become increasingly important. One of the effective ways to achieve …

This study proposed an SVM-based intrusion detection system, which combines a
hierarchical clustering algorithm, a simple feature selection procedure, and the SVM …