Social multimedia traffic is growing exponentially with the increased usage and continuous development of services and applications based on multimedia. Quality of Service (QoS), Quality of Information (QoI), scalability, reliability and such factors that are essential for social multimedia networks are realized by secure data transmission. For delivering actionable and timely insights in order to meet the growing demands of the user, multimedia analytics is performed by means of a trust-based paradigm. Efficient management and control of the network is facilitated by limiting certain capabilities such as energy-aware networking and runtime security in Software Defined Networks. In social multimedia context, suspicious flow detection is performed by a hybrid deep learning based anomaly detection scheme in order to enhance the SDN reliability. The entire process is divided into two modules namely – Abnormal activities detection using support vector machine based on Gradient descent and improved restricted Boltzmann machine which facilitates the anomaly detection module, and satisfying the strict requirements of QoS like low latency and high bandwidth in SDN using end-to-end data delivery module. In social multimedia, data delivery and anomaly detection services are essential in order to improve the efficiency and effectiveness of the system. For this purpose, we use benchmark datasets as well as real time evaluation to experimentally evaluate the proposed scheme. Detection of malicious events like confidential data collection, profile cloning and identity theft are performed to analyze the performance of the system using CMU-based insider threat dataset for large scale analysis.
Large-Scale Social Multimedia Analysis
