Big Data Analytics Technique in Cyber Security: A Review

Author(s):  
Neha Srivastava ◽  
Umesh Chandra Jaiswal
Author(s):  
Fenio Annansingh

The concept of a smart city as a means to enhance the life quality of citizens has been gaining increasing importance in recent years globally. A smart city consists of city infrastructure, which includes smart services, devices, and institutions. Every second, these components of the smart city infrastructure are generating data. The vast amount of data is called big data. This chapter explores the possibilities of using big data analytics to prevent cybersecurity threats in a smart city. It also analyzed how big data tools and concepts can solve cybersecurity challenges and detect and prevent attacks. Using interviews and an extensive review of the literature have developed the data analytics and cyber prevention model. The chapter concludes by indicating that big data analytics allow a smart city to identify and solve cybersecurity challenges quickly and efficiently.


2019 ◽  
Vol 2019 ◽  
pp. 1-2 ◽  
Author(s):  
Pelin Angin ◽  
Bharat Bhargava ◽  
Rohit Ranchal

2018 ◽  
Vol 6 (7) ◽  
pp. 731-734
Author(s):  
Ashish Bajpai ◽  
Dayanand . ◽  
Arushi Arya

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2394 ◽  
Author(s):  
Tiago M. Fernández-Caramés ◽  
Oscar Blanco-Novoa ◽  
Iván Froiz-Míguez ◽  
Paula Fraga-Lamas

Industry 4.0 has paved the way for a world where smart factories will automate and upgrade many processes through the use of some of the latest emerging technologies. One of such technologies is Unmanned Aerial Vehicles (UAVs), which have evolved a great deal in the last years in terms of technology (e.g., control units, sensors, UAV frames) and have significantly reduced their cost. UAVs can help industry in automatable and tedious tasks, like the ones performed on a regular basis for determining the inventory and for preserving item traceability. In such tasks, especially when it comes from untrusted third parties, it is essential to determine whether the collected information is valid or true. Likewise, ensuring data trustworthiness is a key issue in order to leverage Big Data analytics to supply chain efficiency and effectiveness. In such a case, blockchain, another Industry 4.0 technology that has become very popular in other fields like finance, has the potential to provide a higher level of transparency, security, trust and efficiency in the supply chain and enable the use of smart contracts. Thus, in this paper, we present the design and evaluation of a UAV-based system aimed at automating inventory tasks and keeping the traceability of industrial items attached to Radio-Frequency IDentification (RFID) tags. To confront current shortcomings, such a system is developed under a versatile, modular and scalable architecture aimed to reinforce cyber security and decentralization while fostering external audits and big data analytics. Therefore, the system uses a blockchain and a distributed ledger to store certain inventory data collected by UAVs, validate them, ensure their trustworthiness and make them available to the interested parties. In order to show the performance of the proposed system, different tests were performed in a real industrial warehouse, concluding that the system is able to obtain the inventory data really fast in comparison to traditional manual tasks, while being also able to estimate the position of the items when hovering over them thanks to their tag’s signal strength. In addition, the performance of the proposed blockchain-based architecture was evaluated in different scenarios.


2019 ◽  
Vol 2 (1) ◽  
pp. 14-28
Author(s):  
Khalid Istiqlal Syaifullah

A study has been done to perceive the uptake and impact of Big Data in the exploration and production of oil and gas in Indonesia compared to Norway. Interviews were conducted to officials in the Ministry of Energy and Mineral Resources (MoEMR) and the state regulator, SKK Migas. In both industries, more data is being generated more than ever in exploration, production, drilling, and operations, indicating potential application of Big Data. However, approach towards data has remained classical with physical models in opposed to common Big Data approach, which is data-driven analytics. Several impacts of Big Data in both industries are highlighted, including new demand for data analysts, the need for regulations surrounding cyber-security, improvement of safety and environment (which hasn’t been considered in Indonesia), and growing need for more trust and regulations towards open data. Open data in the two industries has seen two different trajectories with Indonesia only implementing it very recently, while the NCS has seen open data drives competition since 1999. This study produced recommendations for the government of Indonesia on open data and how uptake and application of Big Data analytics in EOR could potentially increase national petroleum production to desired levels. Keywords: Big Data, open data, oil and gas in Indonesia, Norway Continental Shelf, data analytics, EOR


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