Biblio

The new network based on software-defined network SDN and network function virtualization NFV will replace the traditional network, so it is urgent to study the network security architecture based on the new network environment. This paper presents a software - defined security SDS architecture. It is open and universal. It provides an open interface for security services, security devices, and security management. It enables different network security vendors to deploy security products and security solutions. It can realize the deployment, arrangement and customization of virtual security function VSFs. It implements fine-grained data flow control and security policy management. The author analyzes the different types of attacks that different parts of the system are vulnerable to. The defender can disable the network attacks by changing the server-side security configuration scheme. The future research direction of network security is put forward.

With the development of the Internet, the network attack technology has undergone tremendous changes. The forms of network attack and defense have also changed, which are features in attacks are becoming more diverse, attacks are more widespread and traditional security protection methods are invalid. In recent years, with the development of software defined security, network anomaly detection technology and big data technology, these challenges have been effectively addressed. This paper proposes a data-driven software defined security architecture with core features including data-driven orchestration engine, scalable network anomaly detection module and security data platform. Based on the construction of the analysis layer in the security data platform, real-time online detection of network data can be realized by integrating network anomaly detection module and security data platform under software defined security architecture. Then, data-driven security business orchestration can be realized to achieve efficient, real-time and dynamic response to detected anomalies. Meanwhile, this paper designs a deep learning-based HTTP anomaly detection algorithm module and integrates it with data-driven software defined security architecture so that demonstrating the flow of the whole system.

Software Defined Networking (SDN) support several administrators for quicker access of resources due to its manageability, cost-effectiveness and adaptability. Even though SDN is beneficial it also exists with security based challenges due to many vulnerable threats. Participation of such threats increases their impact and risk level. In this paper a multi-level security mechanism is proposed over SDN architecture design. In each level the flow packet is analyzed using different metric and finally it reaches a secure controller for processing. Benign flow packets are differentiated from non-benign flow by means of the packet features. Initially routers verify user, secondly policies are verified by using dual-fuzzy logic design and thirdly controllers are authenticated using signature based authentication before assigning flow packets. This work aims to enhance entire security of developed SDN environment. SDN architecture is implemented in OMNeT++ simulation tool that supports OpenFlow switches and controllers. Finally experimental results show better performances in following performance metrics as throughput, time consumption and jitter.

With the development of Software Defined Networking, its software programmability and openness brings new idea for network security. Therefore, many Software Defined Security Architectures emerged at the right moment. Software Defined Security decouples security control plane and security data plane. In Software Defined Security Architectures, underlying security devices are abstracted as security resources in resource pool, intellectualized and automated security business management and orchestration can be realized through software programming in security control plane. However, network management has been becoming extremely complicated due to expansible network scale, varying network devices, lack of abstraction and heterogeneity of network especially. Therefore, new-type open security devices are needed in SDS Architecture for unified management so that they can be conveniently abstracted as security resources in resource pool. This paper firstly analyses why open security devices are needed in SDS architecture and proposes a method of opening security devices. Considering this new architecture requires a new security scheduling mechanism, this paper proposes a security resource scheduling algorithm which is used for managing and scheduling security resources in resource pool according to user s security demand. The security resource scheduling algorithm aims to allocate a security protection task to a suitable security resource in resource pool so that improving security protection efficiency. In the algorithm, we use BP neural network to predict the execution time of security tasks to improve the performance of the algorithm. The simulation result shows that the algorithm has ideal performance. Finally, a usage scenario is given to illustrate the role of security resource scheduling in software defined security architecture.

Honeynet is deployed to trap attackers and learn their behavior patterns and motivations. Conventional honeynet is implemented by dedicated hardware and software. It suffers from inflexibility, high CAPEX and OPEX. There have been several virtualized honeynet architectures to solve those problems. But they lack a standard operating environment and common architecture for dynamic scheduling and adaptive resource allocation. Software Defined Security (SDS) framework has a centralized control mechanism and intelligent decision making ability for different security functions. In this paper, we present a new intelligent honeynet architecture based on SDS framework. It implements security functions over Network Function Virtualization Infrastructure (NFVI). Under uniform and intelligent control, security functional modules can be dynamically deployed and collaborated to complete different tasks. It migrates resources according to the workloads of each honeypot and power off unused modules. Simulation results show that intelligent honeynet has a better performance in conserving resources and reducing energy consumption. The new architecture can fit the needs of future honeynet development and deployment.