Machine Learning Based Botnet Detection in Software Defined Networks

A Machine Learning Based Intrusion Detection System for Software Defined

As an inevitable trend of future 5G networks, Software Defined architecture has many advantages in providing centralized control and flexible resource management. But it is also confronted with various security challenges and potential threats with emerging services and technologies. As the focus of network security, Intrusion Detection Systems (IDS) are usually deployed separately without coll...

MBotCS: A Mobile Botnet Detection System Based on Machine Learning

As the use of mobile devices spreads dramatically, hackers have started making use of mobile botnets to steal user information or perform other malicious attacks. To address this problem, in this paper we propose a mobile botnet detection system, called MBotCS. MBotCS can detect mobile device traffic indicative of the presence of a mobile botnet based on prior training using machine learning te...

Performance of Botnet Detection by Neural Networks in Software-Defined Networks

The recent evolution of Internet to new paradigms such as network function virtualization and software defined networking poses new relevant challenges to the detection of Botnet attacks, calling for innovative approaches. In this work we propose a detection mechanism based on an Artificial Neural Net classifier trained by available data sets collected in conventional networks. We apply such de...

Machine Learning Approach for Botnet Detection

BotNet is a type of malware that has posed serious threats to Internet community and has been a common weapon for committing cybercrimes such as spam generation, stealing sensitive information, click fraud and DDOS attacks. In this document, we propose an approach for BotNet detection at large scale where network traffic is monitored at a central core in the Internet (say a Tier-1 ISP) so that ...

Correlation-based Botnet Detection in Enterprise Networks