Worms Propagation Modeling and Analysis in Big Data Environment

The integration of the Internet and Mobile networks results in huge amount of data, as well as security threat. With the fragile capacity of security protection, worms can propagate in the integration network and undermine the stability and integrity of data. The propagation of worm is a great security risk to massive amounts of data in the integration network. We propose a kind of worm propagating in big data environment named BD-Worm. BD-Worm consumes computing resources and gets privacy information of users, which causes huge losses to our working and living. This paper constructs an integration network topology model and designs the BD-Worm propagating in the big data environment. To analyze the propagation of BD-Worm, we conduct a simulation and provide some recommendations to contain the widespread of BD-Worm according to the simulation results.

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A New Method for information security risk management in big data environment

2020 2nd International Conference on Information Technology and Computer Application (ITCA) ◽

10.1109/itca52113.2020.00100 ◽

2020 ◽

Author(s):

Zhengqi Zhang

Keyword(s):

Risk Management ◽

Big Data ◽

Information Security ◽

New Method ◽

Security Risk ◽

Information Security Risk ◽

Data Environment ◽

Security Risk Management

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Artificial Intelligence- (AI-) Enabled Internet of Things (IoT) for Secure Big Data Processing in Multihoming Networks

Wireless Communications and Mobile Computing ◽

10.1155/2021/5754322 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Geetanjali Rathee ◽

Adel Khelifi ◽

Razi Iqbal

Keyword(s):

Big Data ◽

Internet Of Things ◽

Data Processing ◽

Secure Communication ◽

Security Risk ◽

Huge Amount ◽

Big Data Processing ◽

Security Issues ◽

Real Time Analysis ◽

Artificial Network

The automated techniques enabled with Artificial Neural Networks (ANN), Internet of Things (IoT), and cloud-based services affect the real-time analysis and processing of information in a variety of applications. In addition, multihoming is a type of network that combines various types of networks into a single environment while managing a huge amount of data. Nowadays, the big data processing and monitoring in multihoming networks provide less attention while reducing the security risk and efficiency during processing or monitoring the information. The use of AI-based systems in multihoming big data with IoT- and AI-integrated systems may benefit in various aspects. Although multihoming security issues and their analysis have been well studied by various scientists and researchers; however, not much attention is paid towards big data security processing in multihoming especially using automated techniques and systems. The aim of this paper is to propose an IoT-based artificial network to process and compute big data processing by ensuring a secure communication multihoming network using the Bayesian Rule (BR) and Levenberg-Marquardt (LM) algorithms. Further, the efficiency and effect on multihoming information processing using an AI-assisted mechanism are experimented over various parameters such as classification accuracy, classification time, specificity, sensitivity, ROC, and F -measure.

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A Review on Big Data Analytics in Business

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset21841130 ◽

2018 ◽

pp. 210-214

Author(s):

Manbir Sandhu ◽

Purnima, Anuradha Saini

Keyword(s):

Big Data ◽

Big Data Analytics ◽

Smart Devices ◽

Data Streaming ◽

Huge Amount ◽

Business Units ◽

Enormous Amount ◽

Tools And Techniques ◽

Heath Care ◽

Data Analytic

Big data is a fast-growing technology that has the scope to mine huge amount of data to be used in various analytic applications. With large amount of data streaming in from a myriad of sources: social media, online transactions and ubiquity of smart devices, Big Data is practically garnering attention across all stakeholders from academics, banking, government, heath care, manufacturing and retail. Big Data refers to an enormous amount of data generated from disparate sources along with data analytic techniques to examine this voluminous data for predictive trends and patterns, to exploit new growth opportunities, to gain insight, to make informed decisions and optimize processes. Data-driven decision making is the essence of business establishments. The explosive growth of data is steering the business units to tap the potential of Big Data to achieve fueling growth and to achieve a cutting edge over their competitors. The overwhelming generation of data brings with it, its share of concerns. This paper discusses the concept of Big Data, its characteristics, the tools and techniques deployed by organizations to harness the power of Big Data and the daunting issues that hinder the adoption of Business Intelligence in Big Data strategies in organizations.

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Big Data Environment and Humanities Platform

10.33645/cnc.2017.10.39.5.177 ◽

2017 ◽

Vol 39 (5) ◽

pp. 177-202

Author(s):

Hyun-Cheol Choi

Keyword(s):

Big Data ◽

Data Environment

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Methodology for the Construction of Predictive Analysis Systems as Exemplified by the Mining Equipment In the Big Data Environment Using Smart Agents And Cybernetic Systems

Advances in Cyber-Physical Systems ◽

10.23939/acps2018.01.033 ◽

2018 ◽

Vol 3 (1) ◽

pp. 33-43

Author(s):

Andriy Kupin ◽

◽

Rodion Ivchenko

Keyword(s):

Big Data ◽

Predictive Analysis ◽

Mining Equipment ◽

Data Environment

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A Comparative Study on the Legislation about Anonymization or De-identification for Personal Information in Big Data Environment

Korean Association of International Association of Constitutional Law ◽

10.24324/kiacl.2019.25.2.131 ◽

2019 ◽

Vol 25 (2) ◽

pp. 131-163

Author(s):

Na Roo Kim

Keyword(s):

Big Data ◽

Comparative Study ◽

Personal Information ◽

Data Environment

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Impact of Big Data over Telecom Industry

Pakistan Journal of Engineering Technology & Science ◽

10.22555/pjets.v6i2.1958 ◽

2018 ◽

Vol 6 (2) ◽

Author(s):

Muhammad Waqar Khan ◽

Muhammad Asghar Khan ◽

Muhammad Alam ◽

Wajahat Ali

Keyword(s):

Big Data ◽

Data Science ◽

Cell Phones ◽

Smart Phones ◽

World Population ◽

Huge Amount ◽

Scale Down ◽

Telecom Industry ◽

Telecom Sector ◽

Theoretical Computing

During past few years, data is growing exponentially attracting researchers to work a popular term, the Big Data. Big Data is observed in various fields, such as information technology, telecommunication, theoretical computing, mathematics, data mining and data warehousing. Data science is frequently referred with Big Data as it uses methods to scale down the Big Data. Currently more than 3.2 billion of the world population is connected to internet out of which 46% are connected via smart phones. Over 5.5 billion people are using cell phones. As technology is rapidly shifting from ordinary cell phones towards smart phones, therefore proportion of using internet is also growing. There is a forecast that by 2020 around 7 billion people at the globe will be using internet out of which 52% will be using their smart phones to connect. In year 2050 that figure will be touching 95% of world population. Every device connect to internet generates data. As majority of the devices are using smart phones to generate this data by using applications such as Instagram, WhatsApp, Apple, Google, Google+, Twitter, Flickr etc., therefore this huge amount of data is becoming a big threat for telecom sector. This paper is giving a comparison of amount of Big Data generated by telecom industry. Based on the collected data we use forecasting tools to predict the amount of Big Data will be generated in future and also identify threats that telecom industry will be facing from that huge amount of Big Data.

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