Network Security for SDN-based Cloud IoT Using Deep Learning

Authors

  • A.Banushri, Dr.R.A.Karthika

Abstract

Abstract -The unpredictable rise of intellectual devices have significantly increased the traffic of Internet of Things (IoT) in cloud environment and produced prospective attack level. Conventional Security Structures are insufficient and ineffective to concentrate on security threats in cloud-based IoT networks. Machine Learning practices and SDN (Software Defined Networking) bring in an abundant advantage that could successfully resolve cyber security problems for cloud-based IoT systems. This paper defines an intrusion detection scheme with Deep Learning technology for SDN cloud IoT networks. This arrangement encompasses an intellectual IDS nodes present in the cloud and SDN Controller to identify the anomalies and devise the guidelines into the SDN IoT gateway devices to impede malicious traffic as speedy as possible. The system progression logic is generally investigated with the help of successive procedures such as Runtime process, Initialization and Database Update. Then the proposed system is implemented in an SDN-based environment to execute a range of try-outs. Ultimately, the assessment outcomes of the architecture give path for excellent performance in anomaly revealing, alleviation and also bottleneck problems seen in the SDN cloud IoT networks in association with existing solutions.

Published

2020-12-01

Issue

Section

Articles