Real-time big data processing for anomaly detection : a survey

The advent of connected devices and omnipresence of Internet have paved way for intruders to attack networks, which leads to cyber-attack, financial loss, information theft in healthcare, and cyber war. Hence, network security analytics has become an important area of concern and has gained intensive attention among researchers, off late, specifically in the domain of anomaly detection in network, which is considered crucial for network security. However, preliminary investigations have revealed that the existing approaches to detect anomalies in network are not effective enough, particularly to detect them in real time. The reason for the inefficacy of current approaches is mainly due the amassment of massive volumes of data though the connected devices. Therefore, it is crucial to propose a framework that effectively handles real time big data processing and detect anomalies in networks. In this regard, this paper attempts to address the issue of detecting anomalies in real time. Respectively, this paper has surveyed the state-of-the-art real-time big data processing technologies related to anomaly detection and the vital characteristics of associated machine learning algorithms. This paper begins with the explanation of essential contexts and taxonomy of real-time big data processing, anomalous detection, and machine learning algorithms, followed by the review of big data processing technologies. Finally, the identified research challenges of real-time big data processing in anomaly detection are discussed. © 2018 Elsevier Lt

Deep learning and big data technologies for IoT security

Technology has become inevitable in human life, especially the growth of Internet of Things (IoT), which enables communication and interaction with various devices. However, IoT has been proven to be vulnerable to security breaches. Therefore, it is necessary to develop fool proof solutions by creating new technologies or combining existing technologies to address the security issues. Deep learning, a branch of machine learning has shown promising results in previous studies for detection of security breaches. Additionally, IoT devices generate large volumes, variety, and veracity of data. Thus, when big data technologies are incorporated, higher performance and better data handling can be achieved. Hence, we have conducted a comprehensive survey on state-of-the-art deep learning, IoT security, and big data technologies. Further, a comparative analysis and the relationship among deep learning, IoT security, and big data technologies have also been discussed. Further, we have derived a thematic taxonomy from the comparative analysis of technical studies of the three aforementioned domains. Finally, we have identified and discussed the challenges in incorporating deep learning for IoT security using big data technologies and have provided directions to future researchers on the IoT security aspects

Service Management for IoT: Requirements, Taxonomy, Recent Advances and Open Research Challenges

The exponential growth in the number of smart devices connected to the Internet of Things (IoT), and associated with various IoT-based smart applications and services, raises interoperability challenges which could affect the sustainability of IoT services. IoT software applications are built using different software platforms and embedded in diverse types of terminals and sensing devices. Aiming to offer smart services over a range of network technologies that use different communication protocols. The concept of Web service with service-oriented solutions was introduced to cope with the heterogeneity of hardware and software, and to tackle issues of interoperability, flexibility and scalability. The main step of this solution was the integration of Web of Things technologies into smart device networks, with the utilization of IoT gateways. Service management is a crucial factor in sustaining service-oriented solutions in dynamic and highly scalable IoT systems, and is concerned with several issues associated with service provisioning, orchestration, composition and adaption. This work was motivated by the need for robust and flexible service management systems that can meet the requirements for the rapid scalability and heterogeneity associated with the exponential growth of IoT systems. In the literature there is no survey of service management issues and associated research efforts in the field of IoT. In this article, we identify the key requirements for managing IoT services as well as common service management platforms for IoT. We provide a thematic taxonomy based on the important factors, and investigate recent advances in service management for IoT systems. Finally, the major challenges that remain open are presented as a guide for future research directions. © 2013 IEEE