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Cloud computing refers to the application and service that run on a distributed system using virtualized resources and access by common internet protocol and networking standard. Cloud computing virtualizes system by pooling and sharing resources. System and resources can be monitored from central infrastructure as needed. It requires high security because now day’s companies are placing more essential and huge amount of data on cloud. Hence traditional access control models are not sufficient for cloud computing applications. So encryption based on Attribute (“ABE”-“Attribute based encryption”) has been offered for access control of subcontracted data in cloud computing with complex access control policies. Traditional HASBE provides Flexibility, scalability and fine-grained access control but does not support hierarchical domain structure. In this paper, we had enhanced “Hierarchical attribute-set-based encryption” (“HASBE”) access control with a hierarchical assembly of users, with flexible domain Hierarchy structure and Secure key distribution with predefined policy

Presently, the advances of the internet towards a wide-spread growth and the static nature of traditional networks has limited capacity to cope with organizational business needs. The new network architecture software defined networking (SDN) appeared to address these challenges and provides distinctive features. However, these programmable and centralized approaches of SDN face new security challenges which demand innovative security mechanisms like intrusion detection systems (IDS's). The IDS of SDN are designed currently with a machine learning approach; however, a deep learning approach is also being explored to achieve better efficiency and accuracy. In this article, an overview of the SDN with its security concern and IDS as a security solution is explained. A survey of existing security solutions designed to secure the SDN, and a comparative study of various IDS approaches based on a deep learning model and machine learning methods are discussed in the article. Finally, we describe future directions for SDN security.

Presently, the advances of the internet towards a wide-spread growth and the static nature of traditional networks has limited capacity to cope with organizational business needs. The new network architecture software defined networking (SDN) appeared to address these challenges and provides distinctive features. However, these programmable and centralized approaches of SDN face new security challenges which demand innovative security mechanisms like intrusion detection systems (IDS's). The IDS of SDN are designed currently with a machine learning approach; however, a deep learning approach is also being explored to achieve better efficiency and accuracy. In this article, an overview of the SDN with its security concern and IDS as a security solution is explained. A survey of existing security solutions designed to secure the SDN, and a comparative study of various IDS approaches based on a deep learning model and machine learning methods are discussed in the article. Finally, we describe future directions for SDN security.

No abstract