Realization of the importance for advanced tool and techniques to secure the network
infrastructure from the security risks has led to the development of many machine learning …

In the era of big data, with the increasing number of audit data features, human-centered
smart intrusion detection system performance is decreasing in training time and …

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 …

Many organizations recognize the necessities of utilizing sophisticated tools and systems to
protect their computer networks and reduce the risk of compromising their information …

Network security is becoming increasingly important in our daily lives—not only for
organizations but also for individuals. Intrusion detection systems have been widely used to …

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

A novel support vector machine (SVM) model combining kernel principal component
analysis (KPCA) with genetic algorithm (GA) is proposed for intrusion detection. In the …

From the last few decades, people do various transaction activities like air ticket reservation,
online banking, distance learning, group discussion and so on using the internet. Due to …

An efficient machine learning (ML) ensemble technique for categorizing Intrusion Detection
(ID) is proposed in this study. The tuning of the ML model's parameters is a critical topic …

In this paper, a hybrid method of support vector machine and genetic algorithm (GA) is
proposed and its implementation in intrusion detection problem is explained. The proposed …