As a result of the widespread adoption of the Internet of Things, there are now hundreds of
millions of connected devices, increasing the likelihood that they may be vulnerable to …

Ever since the internet became part of the everyday lives of humans providing network
security has been considered of utmost importance. Over the years lot of time and energy …

In this paper, the authors have compared ensemble methods in Spark supported distributed
environment. With ever changing attack trends traditional machine learning algorithms fail to …

Abstract Application Layer DDoS (AL-DDoS) attacks are an extremely dangerous variety of
DDoS attacks that started becoming popular recently. They are executed using very few …

In the present paper we describe a new, updated and refined dataset specifically tailored to
train and evaluate machine learning based malware traffic analysis algorithms. To generate …

The botnet, a network of compromise internet connected devices, controlled by an attacker is
considered to be the most catastrophic cybersecurity threat. In the large-scale cyberattacks …

Abstract Machine learning techniques have been widely used for the classification of botnets
as they have been argued to have improved strengths compared to signature-based …

As Internet technology advances, the exponential growth of network security risks is
becoming increasingly apparent. Safeguarding the network poses a significant challenge in …

Malware is a serious threat to the modern Internet, as it is used to, eg, sending spam or
stealing bank login credentials. Typically, to communicate with the attacker, it utilizes …

In this paper, two blending-based ensemble models, namely, logistic regression-based
blending ensemble and SVM-based blending ensemble have been compared in terms of …