This paper proposes a novel approach for intrusion detection system based on sampling
with Least Square Support Vector Machine (LS-SVM). Decision making is performed in two …

Intrusion Detection Systems (IDS) have become a necessary component of almost every
security infrastructure. Recently, Support Vector Machines (SVM) has been employed to …

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

Intrusion detection systems based on a hybrid approach have attracted considerable interest
from researchers. Hybrid classifiers are able to provide improved detection accuracy, but …

Intrusion detection systems (EDSs) deal with large amounts of data containing irrelevant
and/or redundant features. These features result in a slow training and testing process …

Intrusion detection is a process of identifying the Attacks in the networks. The main aim of
IDS is to identify the Normal and Intrusive activities. In recent years, many researchers are …

The growing prevalence of network attacks is a well-known problem which can impact the
availability, confidentiality, and integrity of critical information for both individuals and …

Abstract Machine learning plays an important role in constructing intrusion detection models.
However, the information era is an era of data. With the continuous increase in data size and …

Network attacks have become more pervasive in the cyber world. There are various attacks
such as denial of service, scanning, privilege escalation that is increasing day by day …

The success of any Intrusion Detection System (IDS) is a complicated problem due to its
nonlinearity and the quantitative or qualitative network traffic data stream with many features …