An Enhanced Data Plane for Network Event Processing in Software Defined Networking

Software-defined networking is a new paradigm that overcomes problems associated with traditional network architecture by separating the control logic from data plane devices. It also enhances performance by providing a highly-programmable interface that adapts to dynamic changes in network policies. As software-defined networking controllers are prone to single-point failures, providing security is one of the biggest challenges in this framework. This paper intends to provide an intrusion detection mechanism in both the control plane and data plane to secure the controller and forwarding devices respectively. In the control plane, we imposed a flow-based intrusion detection system that inspects every new incoming flow towards the controller. In the data plane, we assigned a signature-based intrusion detection system to inspect traffic between Open Flow switches using port mirroring to analyse and detect malicious activity. Our flow-based system works with the help of trained, multi-layer machine learning-based classifier, while our signature-based system works with rule-based classifiers using the Snort intrusion detection system. The ensemble feature selection technique we adopted in the flow-based system helps to identify the prominent features and hasten the classification process. Our proposed work ensures a high level of security in the Software-defined networking environment by working simultaneously in both control plane and data plane.

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Load Balancing in Software-Defined Networking using Controller Placement

10.21203/rs.3.rs-53407/v1 ◽

2020 ◽

Author(s):

Hamid Nejadnik ◽

Rasool Sadeghi ◽

Sayed Mahdi Faghih Imani

Keyword(s):

Load Balancing ◽

Software Defined Networking ◽

Network Simulator ◽

Control Plane ◽

Placement Problem ◽

Data Plane ◽

Controller Placement ◽

Controller Placement Problem ◽

Load Balancer

Abstract Software Defined Networking (SDN) is a novel architecture that separates the data plane from the control plane using an external controller. Similar to traditional networks, load balancing has a great impact on the performance and availability of SDN. Therefore, the Controller Placement Problem (CPP) in SDN influences on the load balancing solutions. In this paper, various topologies of CPP including different load balancer controllers are simulated and evaluated in the SDN using the OFSwitch13 module of ns-3 network simulator. The results provide a solid comparison of the proposed topologies in different network situations.

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Attribute-Guard: Attribute-Based Flow Access Control Framework in Software-Defined Networking

Security and Communication Networks ◽

10.1155/2020/6302739 ◽

2020 ◽

Vol 2020 ◽

pp. 1-18 ◽

Cited By ~ 1

Author(s):

Xianwei Zhu ◽

ChaoWen Chang ◽

Qin Xi ◽

ZhiBin Zuo

Keyword(s):

Access Control ◽

Software Defined Networking ◽

Authentication Protocol ◽

Network Characteristics ◽

Fine Grained ◽

Control Framework ◽

Data Plane ◽

Data Validity ◽

The One ◽

Matching Field

Software-defined networking (SDN) decouples the control plane from the data plane, offering flexible network configuration and management. Because of this architecture, some security features are missing. On the one hand, because the data plane only has the packet forwarding function, it is impossible to effectively authenticate the data validity. On the other hand, OpenFlow can only match based on network characteristics, and it is impossible to achieve fine-grained access control. In this paper, we aim to develop solutions to guarantee the validity of flow in SDN and present Attribute-Guard, a fine-grained access control and authentication scheme for flow in SDN. We design an attribute-based flow authentication protocol to verify the legitimacy of the validity flow. The attribute identifier is used as a matching field to define a forwarding control. The flow matching based on the attribute identifier and the flow authentication protocol jointly implement fine-grained access control. We conduct theoretical analysis and simulation-based evaluation of Attribute-Guard. The results show that Attribute-Guard can efficiently identify and reject fake flow.

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Software Defined Networking Flow Table Management of OpenFlow Switches Performance and Security Challenges: A Survey

Future Internet ◽

10.3390/fi12090147 ◽

2020 ◽

Vol 12 (9) ◽

pp. 147 ◽

Cited By ~ 1

Author(s):

Babangida Isyaku ◽

Mohd Soperi Mohd Zahid ◽

Maznah Bte Kamat ◽

Kamalrulnizam Abu Bakar ◽

Fuad A. Ghaleb

Keyword(s):

Large Scale ◽

Network Performance ◽

Software Defined Networking ◽

Cloud Services ◽

Future Research ◽

Control Plane ◽

Processing Load ◽

Flow Table ◽

Data Plane ◽

Large Scale Networks

Software defined networking (SDN) is an emerging network paradigm that decouples the control plane from the data plane. The data plane is composed of forwarding elements called switches and the control plane is composed of controllers. SDN is gaining popularity from industry and academics due to its advantages such as centralized, flexible, and programmable network management. The increasing number of traffics due to the proliferation of the Internet of Thing (IoT) devices may result in two problems: (1) increased processing load of the controller, and (2) insufficient space in the switches’ flow table to accommodate the flow entries. These problems may cause undesired network behavior and unstable network performance, especially in large-scale networks. Many solutions have been proposed to improve the management of the flow table, reducing controller processing load, and mitigating security threats and vulnerabilities on the controllers and switches. This paper provides comprehensive surveys of existing schemes to ensure SDN meets the quality of service (QoS) demands of various applications and cloud services. Finally, potential future research directions are identified and discussed such as management of flow table using machine learning.

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Artificial Intelligence Enabled Routing in Software Defined Networking

Applied Sciences ◽

10.3390/app10186564 ◽

2020 ◽

Vol 10 (18) ◽

pp. 6564 ◽

Cited By ~ 1

Author(s):

Yan-Jing Wu ◽

Po-Chun Hwang ◽

Wen-Shyang Hwang ◽

Ming-Hua Cheng

Keyword(s):

Artificial Intelligence ◽

Performance Metrics ◽

Packet Delay ◽

Software Defined Networking ◽

Dynamic Routing ◽

Learning Ability ◽

Control Plane ◽

Monitoring Period ◽

Data Plane ◽

The Impact

Software defined networking (SDN) is an emerging networking architecture that separates the control plane from the data plane and moves network management to a central point, called the controller. The controller is responsible for preparing the flow tables of each switch in the data plane. Although dynamic routing can perform rerouting in case of congestion by periodically monitoring the status of each data flow, problems concerning a suitable monitoring period duration and lack of learning ability from past experiences to avoid similar but ineffective route decisions remain unsolved. This paper presents an artificial intelligence enabled routing (AIER) mechanism with congestion avoidance in SDN, which can not only alleviate the impact of monitoring periods with dynamic routing, but also provide learning ability and superior route decisions by introducing artificial intelligence (AI) technology. We evaluate the performance of the proposed AIER mechanism on the Mininet simulator by installing three additional modules, namely, topology discovery, monitoring period, and an artificial neural network, in the control plane. The effectiveness and superiority of our proposed AIER mechanism are demonstrated by performance metrics, including average throughput, packet loss ratio, and packet delay versus data rate for different monitoring periods in the system.

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