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With the emergence of cyber technology, the biggest evolution has been observed in the use of Internet for financial purposes, in particular for the Internet banking sector. However, with the increase in the number of Internet banking users, many security issues have been discovered. In the recent past, there have been many successful cyber-attacks on the Internet banking services (IBS) throughout the world. There exists a huge amount of various data about the security of the banking systems, however, proper analysis of such data using various learning techniques is needed for security assessment. In this research work, we aim to assess the security of IBS by developing a framework based on deep analysis of big data (available in various formats) and the existing security requirements of the country. We propose a framework consisting of 93 data categories to assess the security of the IBS. We evaluate our proposed approach on a case study consisting of the banks providing IBS in Pakistan. A total of 21 Pakistani banks providing Internet banking services are analyzed thoroughly using our proposed framework. The result uncovered many deficiencies in the Internet banking services of the analyzed banks. All these deficiencies are conveyed to the respective banks for verification and helping them to take corrective measures. In addition, a comprehensive set of security recommendations is developed for the banks, their customers and the regularity authority for improving Internet banking security. [ABSTRACT FROM AUTHOR]

Abstract Process of information extraction (IE) is used to extract useful information from unstructured or semi-structured data. Big data arise new challenges for IE techniques with the rapid growth of multifaceted also called as multidimensional unstructured data. Traditional IE systems are inefficient to deal with this huge deluge of unstructured big data. The volume and variety of big data demand to improve the computational capabilities of these IE systems. It is necessary to understand the competency and limitations of the existing IE techniques related to data pre-processing, data extraction and transformation, and representations for huge volumes of multidimensional unstructured data. Numerous studies have been conducted on IE, addressing the challenges and issues for different data types such as text, image, audio and video. Very limited consolidated research work have been conducted to investigate the task-dependent and task-independent limitations of IE covering all data types in a single study. This research work address this limitation and present a systematic literature review of state-of-the-art techniques for a variety of big data, consolidating all data types. Recent challenges of IE are also identified and summarized. Potential solutions are proposed giving future research directions in big data IE. The research is significant in terms of recent trends and challenges related to big data analytics. The outcome of the research and recommendations will help to improve the big data analytics by making it more productive.

Multimedia data, ranging from text, audio, video to animation, undergoes intellectual property protection or has high sensitivity. To deal with privacy leakage during multimedia sharing and dissemination, it is crucial to trace the origin and transformation history of multimedia data, which is called multimedia provenance. In this paper, we construct a multimedia access control system with secure provenance in fog-cloud computing networks, which is designed based on attribute based encryption (ABE) and zero of knowledge technologies. The proposed scheme realizes confidentiality of multimedia big data that is outsourced to the cloud, anonymity of data provider, fine-grained access control of encrypted data, irrefutable of the provenance data, and traceability of data provider. We utilize the fog server to reduce user's decryption burden and transfer partial decryption tasks. The suggested system is formally proved indistinguishable against chosen plaintext attack (IND-CPA). The simulation and experimental results indicate that our system has low communication overhead and low computation cost. [ABSTRACT FROM AUTHOR]

Data dependencies represent one of the key metadata to characterize and profile multimedia and big data sources. With respect to traditional databases, in these new contexts it has been necessary to introduce some approximations in the definition of dependencies. This yields a proliferation of dependencies, which makes it difficult for a user to effectively analyze them. To this end, in this paper we present a technique for ranking and visualizing dependencies holding on big and multimedia data. A qualitative evaluation has highlighted the advantages of the proposed visualization metaphors. [ABSTRACT FROM AUTHOR]

Affective social multimedia computing provides us the opportunity to improve our daily lives. Various things, such as devices in ubiquitous computing environments and autonomous vehicles in real environments considering human beings, can be controlled by analyzing and learning affective social big data. Deep learning is a core learning algorithm for autonomous control; however, it requires huge amounts of learning data, and the process of collecting various types of learning data is expensive. The collection limit of affective social videos for deep learning is resolved by analyzing affective social videos, such as YouTube and Closed Circuit Television (CCTV) videos collected in advance, and generating new affective social videos more as learning data without human beings autonomously controlling other cameras. The control signals of the cameras are generated by Convolutional Neural Network (CNN)-based end-to-end controls. However, images captured consecutively need to be analyzed to improve the quality of the generated control signals. This paper proposes a system that generates affective social videos for deep learning by Convolutional Recurrent Neural Network (CRNN)-based end-to-end controls. The extracted images in affective social videos are utilized for calculating the control signals based on the CRNN. Additional affective social videos are then generated by the extracted consecutive images and camera control signals. The effectiveness of the proposed method was verified in the experiments by comparing the results obtained using the proposed method with those obtained using the traditional CNN. The results showed that the accuracy of the control signals obtained using the proposed method was 56.30% higher than that of the control signals obtained using the traditional CNN. [ABSTRACT FROM AUTHOR]

With the fast increase of multimedia traffic in Internet of Things (IoT) applications, IoT traffic now requires very different Quality of Service (QoS). By extensive statistical analysis of traffic flow data from a real world network, the authors find that there are some latent features hidden in the multimedia data, which can be useful for accurately differentiating multimedia traffic flows from the QoS perspective. Under limited training data conditions, existing shallow classification methods are limited in performance, and are thus not effective in classifying emerging multimedia traffic types, which have truly entered the era of big data and become very completed in QoS features. This situation inspires us to revisit the multimedia traffic classification problem with a deep learning (DL) approach. In this study, an improved DL‐based multimedia traffic classification method is proposed, which considers the inherent structure of QoS features in multimedia data. An improved stacked autoencoder model is employed to learn the relevant QoS features of multimedia traffic. Extensive experimental studies with multimedia datasets captured from a campus network demonstrate the effectiveness of the proposed method over six benchmark schemes. [ABSTRACT FROM AUTHOR]

Intelligent Traffic Systems (ITS), as integrated systems including control technologies, communication technologies, vehicle sensing and vehicle electronic technologies, have provided valuable solutions to the increasingly serious traffic problems. In the process of construction and operation of ITS, big data, especially multimedia data is produced at a rapid speed, which has made traffic information more and more complicated, causing traffic management facing new challenges. Hence, in order to achieve efficient management of all types of transportation resources and make better use of ITS, it is necessary and significant to study the architecture and performance of ITS in depth. Through dividing the system into different functional modules and assigning these modules to components in Performance Evaluation Process Algebra (PEPA), we can adopt a new method to realize the modeling and evaluating the working process of real-time positioning and route planning in ITS. Meanwhile, the fluid flow approximation is employed to conduct a performance analysis through PEPA models, guaranteeing that the response time, the maximum utilization and the throughput of the system can be achieved and analyzed. [ABSTRACT FROM AUTHOR]

With the significant improvements in mobile digital devices and wireless networking technologies, we have witnessed the explosion of multimedia data. Because it is dynamic, vast in volume, and heterogeneous, this data not only evokes various novel data-driven services and applications, but also brings considerable security threats. In this article, the authors focus on privacy leakage issues in multimedia systems and study how to maximize the total privacy weights and upgrade the security level given predefined time and resource constraints. To this end, they propose a selective privacy-preserving method that adaptively allocates encryption resources according to the privacy weight and execution time of each data package. That is, it selects the encryption method with the appropriate complexity and security level for each multimedia data package. It first divides the data randomly into two parts, then performs XOR operations and generates cipher keys in different cloud storages to prevent users’ original information from being attacked by untrusted cloud operators. Extensive simulation results have demonstrated the advantages and superiority of the proposed method over previous schemes. This article is part of a special issue on cybersecurity. [ABSTRACT FROM AUTHOR]

During the recent era of big data, a huge volume of unstructured data are being produced in various forms of audio, video, images, text, and animation. Effective use of these unstructured big data is a laborious and tedious task. Information extraction (IE) systems help to extract useful information from this large variety of unstructured data. Several techniques and methods have been presented for IE from unstructured data. However, numerous studies conducted on IE from a variety of unstructured data are limited to single data types such as text, image, audio, or video. This article reviews the existing IE techniques along with its subtasks, limitations, and challenges for the variety of unstructured data highlighting the impact of unstructured big data on IE techniques. To the best of our knowledge, there is no comprehensive study conducted to investigate the limitations of existing IE techniques for the variety of unstructured big data. The objective of the structured review presented in this article is twofold. First, it presents the overview of IE techniques from a variety of unstructured data such as text, image, audio, and video at one platform. Second, it investigates the limitations of these existing IE techniques due to the heterogeneity, dimensionality, and volume of unstructured big data. The review finds that advanced techniques for IE, particularly for multifaceted unstructured big data sets, are the utmost requirement of the organizations to manage big data and derive strategic information. Further, potential solutions are also presented to improve the unstructured big data IE systems for future research. These solutions will help to increase the efficiency and effectiveness of the data analytics process in terms of context-aware analytics systems, data-driven decision-making, and knowledge management. [ABSTRACT FROM AUTHOR]