CyberPulse++: A machine learning‐based security framework for detecting link flooding attacks in software defined networks

2021 ◽  
Author(s):  
Raihan ur Rasool ◽  
Khandakar Ahmed ◽  
Zahid Anwar ◽  
Hua Wang ◽  
Usman Ashraf ◽  
...  
Keyword(s):  
Machine Learning ◽  
Software Defined Networks ◽  
Security Framework ◽  
Flooding Attacks

Machine Learning-Enabled Software-Defined Networks for QoE Management

2021 ◽  
Author(s):  
Chunzhi Wang ◽  
Le Yuan ◽  
Mykhailo Medvetskyi ◽  
Mykola Beshley ◽  
Andrii Pryslupskyi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Software Defined Networks

The Usage Analysis of Machine Learning Methods for Intrusion Detection in Software-Defined Networks

2021 ◽  
pp. 959-975
Author(s):  
Derya Yiltas-Kaplan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Network Architectures ◽  
Software Defined Networks ◽  
Learning Approaches ◽  
Advantages And Disadvantages ◽  
Machine Learning Methods ◽  
Network Problems ◽  
Usage Analysis

This chapter focuses on the process of the machine learning with considering the architecture of software-defined networks (SDNs) and their security mechanisms. In general, machine learning has been studied widely in traditional network problems, but recently there have been a limited number of studies in the literature that connect SDN security and machine learning approaches. The main reason of this situation is that the structure of SDN has emerged newly and become different from the traditional networks. These structural variances are also summarized and compared in this chapter. After the main properties of the network architectures, several intrusion detection studies on SDN are introduced and analyzed according to their advantages and disadvantages. Upon this schedule, this chapter also aims to be the first organized guide that presents the referenced studies on the SDN security and artificial intelligence together.

scholarly journals Cyberpulse: A Machine Learning Based Link Flooding Attack Mitigation System for Software Defined Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 34885-34899 ◽  
Author(s):  
Raihan Ur Rasool ◽  
Usman Ashraf ◽  
Khandakar Ahmed ◽  
Hua Wang ◽  
Wajid Rafique ◽  
...  
Keyword(s):  
Machine Learning ◽  
Software Defined Networks ◽  
Flooding Attack ◽  
Attack Mitigation

scholarly journals A Survey on Machine Learning and Deep Learning based Quality of Service aware Protocols for Software Defined Networks

2021 ◽  
Author(s):  
Hiren Kumar Deva Sarma
Keyword(s):  
Machine Learning ◽  
Quality Of Service ◽  
Deep Learning ◽  
Service Level Agreement ◽  
Service Level ◽  
Future Research ◽  
Traffic Classification ◽  
Software Defined Networks ◽  
Research Issues

<p>Quality of Service (QoS) is one of the most important parameters to be considered in computer networking and communication. The traditional network incorporates various quality QoS frameworks to enhance the quality of services. Due to the distributed nature of the traditional networks, providing quality of service, based on service level agreement (SLA) is a complex task for the network designers and administrators. With the advent of software defined networks (SDN), the task of ensuring QoS is expected to become feasible. Since SDN has logically centralized architecture, it may be able to provide QoS, which was otherwise extremely difficult in traditional network architectures. Emergence and popularity of machine learning (ML) and deep learning (DL) have opened up even more possibilities in the line of QoS assurance. In this article, the focus has been mainly on machine learning and deep learning based QoS aware protocols that have been developed so far for SDN. The functional areas of SDN namely traffic classification, QoS aware routing, queuing, and scheduling are considered in this survey. The article presents a systematic and comprehensive study on different ML and DL based approaches designed to improve overall QoS in SDN. Different research issues & challenges, and future research directions in the area of QoS in SDN are outlined. <b></b></p>

scholarly journals An Ontological Security Framework to Secure the SDN based IoT Networks

2021 ◽  
Vol 5 (1) ◽  
pp. 4-18
Author(s):  
Nazmul Hossain ◽  
Md Zobayer Hossain ◽  
Md Alam Hossain
Keyword(s):  
Internet Of Things ◽  
Network Model ◽  
Security Architecture ◽  
Software Defined Network ◽  
Software Defined Networks ◽  
Network Development ◽  
Ontological Security ◽  
Security Framework ◽  
Network Infrastructure ◽  
Iot Devices

The IoT (Internet of Things) is now a trendy technology with its numerous apps in multiple areas. It includes a heterogeneous amount of Internet and mutually linked devices. Since the IoT network is characterized by tiny assets that produce less energy and are more flexible, this number of machines is difficult to monitor. SDN (Software Defined Network) is a new network model that facilitates the creation and introduction of fresh networking abstractions, simplifies the management of network and facilitates network development. In this paper, by leveraging the fundamental characteristics represented by Software Defined Networks (SDN), we present an ontological security architecture for IoT networks. Our security architecture restricts access to independently verified IoT devices via the network. To secure the flows in the IoT network infrastructure, we introduced an extra layer and provide a lightweight protocol to authenticate IoT systems. Such an advanced strategy to protection containing IoT device authentication and allowing approved flows can assist secure IoT networks against malicious IoT devices and threats.

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