Social Multimedia Network Research Articles

Social Multimedia Security and Suspicious Activity Detection in SDN using Hybrid Deep Learning Technique

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.

Trust-Based Video Management Framework for Social Multimedia Networks

Social multimedia networks (SMNs) have attracted much attention from both academia and industry due to their impact on our daily lives. The requirements of SMN users are increasing along with time, which make the satisfaction of those requirements a very challenging process. One important challenge facing SMNs consists of their internal users that can upload and manipulate insecure, untrusted, and unauthorized contents. For this purpose, controlling and verifying content delivered to end users is becoming a highly challenging process. So far, many researchers have investigated the possibilities of implementing a trustworthy SMN. In this vein, the aim of this paper is to propose a framework that allows collaboration between humans and machines to ensure secure delivery of trusted video content over SMNs while ensuring an optimal deployment cost in the form of CPU, RAM, and storage. The key concepts beneath the proposed framework consist in assigning to each user a level of trust based on his/her history, creating an intelligent agent that decides which content can be automatically published on the network and which content should be reviewed or rejected, and checking the videos’ integrity and delivery during the streaming process. Accordingly, we ensure that the trust level of the SMNs increases. Simultaneously, efficient capital expenditure and operational expenditures can be achieved.

Hybrid Deep-Learning-Based Anomaly Detection Scheme for Suspicious Flow Detection in SDN: A Social Multimedia Perspective

The continuous development and usage of multi-media-based applications and services have contributed to the exponential growth of social multimedia traffic. In this context, secure transmission of data plays a critical role in realizing all of the key requirements of social multimedia networks such as reliability, scalability, quality of information, and quality of service (QoS). Thus, a trust-based paradigm for multimedia analytics is highly desired to meet the increasing user requirements and deliver more timely and actionable insights. In this regard, software-defined networks (SDNs) play a vital role; however, several factors such as as-runtime security, and energy-aware networking limit its capabilities to facilitate efficient network control and management. Thus, with the view to enhance the reliability of the SDN, a hybrid deep-learning-based anomaly detection scheme for suspicious flow detection in the context of social multimedia is proposed. It consists of the following two modules: 1) an anomaly detection module that leverages improved restricted Boltzmann machine and gradient descent-based support vector machine to detect the abnormal activities, and 2) an end-to-end data delivery module to satisfy strict QoS requirements of the SDN, that is, high bandwidth and low latency. Finally, the proposed scheme has been experimentally evaluated on both real-time and benchmark datasets to prove its effectiveness and efficiency in terms of anomaly detection and data delivery essential for social multimedia. Further, a large-scale analysis over a Carnegie Mellon University (CMU)-based insider threat dataset has been conducted to identify its performance in terms of detecting malicious events such as-Identity theft, profile cloning, confidential data collection, etc.

Social multimedia network service quality, user satisfaction, and prosumer activity

This study addresses the effects of social multimedia network service quality on user satisfaction and prosumer activity by analyzing data from 400 Korean sports game and event enthusiasts who are social networking service (SNS) users. We also examine the mediating role of value in the relationship between SNS information service quality and prosumer activity. The findings show that trust, interactivity, and search convenience, which are sub-constructs of SNS information service quality, positively influence value and that trust and accuracy are positively associated with user satisfaction. Further, while value has a positive effect on user satisfaction and prosumer activity, user satisfaction has a positive effect on prosumer activity. We also find that value has a partial mediating role in the relationship between SNS information service quality and prosumer activity. In our conclusion, the implications of our findings for future research and practice are discussed.

Social Multimedia Networks Behaviour Model & Architecture

AbstractPeople constantly use social multimedia networks to communicate with one another, with users mostly sharing data, such as photos and videos. We examine the motivations that drive colluders to form alliances over social networking platforms and determine how these groups create coalitions to advance their interests. We also investigate the network architectures that underlie social multimedia networks and how these platforms circulate. Such architectures are connected to different protocols, including WebID, Semantic Pingback and PubSubHubbub, to form a logical semantic circulating social multimedia network that delivers a centralised social network structure.

Guest Editorial: Automated Big Data Analysis for Social Multimedia Network Environments

Guest Editorial: Automated Big Data Analysis for Social Multimedia Network Environments

Texture feature-based text region segmentation in social multimedia data

This paper proposes a method of effectively segmenting text areas that exist in images by using the texture features of various types of input images obtained in social multimedia networks with an artificial neural network. The proposed text segmentation method consists of four main steps: a step for extracting candidate text areas, a step for localizing the text areas, a step for separating the text from the background, and a step for verifying the candidate text areas. In the candidate text area extraction step, candidate blocks that have any text areas are segmented in an input image on the basis of the texture features of the candidate blocks. In the text area localization step, only strings are extracted from the candidate text blocks. In the text and background separation step, the text areas are separated from the background area in the localized text blocks. In the candidate text area verification step, an artificial neural network is used to verify whether the extracted text blocks include actual text areas and exclude non-text areas. In the experimental results, the proposed method was applied to various types of news and non-news images, and it was found that the proposed method extracted text regions more accurately than existing methods.

A secure biometric based multi-server authentication scheme for social multimedia networks

Social networking is one of the major source of massive data. Such data is not only difficult to store, manipulate and maintain but it's open access makes it security prone. Therefore, robust and efficient authentication should be devised to make it invincible against the known security attacks. Moreover, social networking services are intrinsically multi-server environments, therefore compatible and suitable authentication should be designed accordingly. Sundry authentication protocols are being utilized at the moment and many of them are designed for single server architecture. This type of remote architecture resists each user to get itself register with each server if multiple servers are employed to offer online social services. Recently multi-server architecture for authentication has replaced the single server architecture, and it enable users to register once and procure services from multiple servers. A short time ago, Lu et al. presented two authentication schemes based on three factors. Furthermore, both Lu et al.'s schemes are designed for multi-server architecture. Lu et al. claimed the schemes to be invincible against the known attacks. However, this paper shows that one of the Lu et al.'s scheme is susceptible to user anonymity violation and impersonation attacks, whereas Lu et al.'s second scheme is susceptible to user impersonation attack. Therefore an enhanced scheme is introduced in this paper. The proposed scheme is more robust than subsisting schemes. The proposed scheme is thoroughly verified and validated with formal and informal security discussion, and through the popular automated tool ProVerif. The in-depth analysis affirms that proposed scheme is lightweight in terms of computations while attaining mutual authentication and is invincible against the known attacks, hence is more suitable for automated big data analysis for social multimedia networking environments.

Online distribution and interaction of video data in social multimedia network

As the emerging multimedia on the latest social multimedia network, high dimensional video (HDV) data distribution is one of the most challenging problems in social multimedia applications. In the paper, we provide a state-of-the-art analysis on the streaming and interactive technologies for the two most popular representations of HDV, video plus depth map and multi-view video since both of them can enable the view-point interaction and behavior analysis in social media applications. Additionally, view-point switching techniques in HDV streaming with emphasis on interactive functionality are discussed, including SP-frame, Wyner-ziv frame and view switchable stream generation. Moreover, the overall performance of key tools focusing on HDV streaming is evaluated and potential improvements are discussed.