Neural network models in big data analytics and cyber security

2017 ◽  
Author(s):  
Ana-Maria Ghimes ◽  
Victor-Valeriu Patriciu
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Cyber Security ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Network Models ◽  
Neural Network Models

scholarly journals Exploratory Data Analysis and Offence Prediction

2021 ◽  
Vol 12 (6) ◽  
pp. 2761-2768
Author(s):  
Vivek Gaurav Singh Et al.
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Analytics ◽  
Data Science ◽  
Visual Analysis ◽  
Big Data Analytics ◽  
Network Models ◽  
Trend Prediction ◽  
Neural Network Models ◽  
Indian States

Big data is a part of data science that pinpoint different ways to diagnosis, systematically withdraw facts from informational collections that are excessively enormous or complex to be managed by customary information handling application software. Big Data Analytics(BDA) is a specific tactic for breaking down and recognizing assorted examples, kindred, and patterns inside a massive volume in order. Big data analytics (BDA) is a meticulous approach to data analysing and recognising unique layers, connections, and trends ina gigantic volume of data. We apply BDA to illegitimate information collected in this paper, where preliminary data analysis was conducted for visual analysis and trend prediction. Following statistical analysis and visualisation, some incredibly interesting facts and patterns emerge from illegal data in INDIAN states i.e. (Uttar Pradesh, New Delhi, Goa). The prognostic results demonstrate that Kerasstateful LSTM execute enhanced than neural network models. These capable outcomes will allow police departments and law enforcement agencies to better understand crime problems and gain insights that will allow them to schedule activities, predict the likelihood of incidents, efficiently allocate resources, and optimise decision making.

Using Big Data Analytics to Assist a Smart City to Prevent Cyber Security Threats

2021 ◽  
pp. 107-124
Author(s):  
Fenio Annansingh
Keyword(s):  
Big Data ◽  
Cyber Security ◽  
Smart City ◽  
Data Analytics ◽  
Life Quality ◽  
Big Data Analytics ◽  
Review Of The Literature ◽  
Smart Services ◽  
Prevention Model

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.

scholarly journals Big Data Analytics for Cyber Security

2019 ◽  
Vol 2019 ◽  
pp. 1-2 ◽  
Author(s):  
Pelin Angin ◽  
Bharat Bhargava ◽  
Rohit Ranchal
Keyword(s):  
Big Data ◽  
Cyber Security ◽  
Data Analytics ◽  
Big Data Analytics

scholarly journals Crime Data Forecasting Using Machine Learning and Big Data Analytics

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 591-606
Author(s):  
R. Brindha ◽  
Dr.M. Thillaikarasi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Geographical Information ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Crime Data

Big data analytics (BDA) is a system based method with an aim to recognize and examine different designs, patterns and trends under the big dataset. In this paper, BDA is used to visualize and trends the prediction where exploratory data analysis examines the crime data. “A successive facts and patterns have been taken in following cities of California, Washington and Florida by using statistical analysis and visualization”. The predictive result gives the performance using Keras Prophet Model, LSTM and neural network models followed by prophet model which are the existing methods used to find the crime data under BDA technique. But the crime actions increases day by day which is greater task for the people to overcome the challenging crime activities. Some ignored the essential rate of influential aspects. To overcome these challenging problems of big data, many studies have been developed with limited one or two features. “This paper introduces a big data introduces to analyze the influential aspects about the crime incidents, and examine it on New York City. The proposed structure relates the dynamic machine learning algorithms and geographical information system (GIS) to consider the contiguous reasons of crime data. Recursive feature elimination (RFE) is used to select the optimum characteristic data. Exploitation of gradient boost decision tree (GBDT), logistic regression (LR), support vector machine (SVM) and artificial neural network (ANN) are related to develop the optimum data model. Significant impact features were then reviewed by applying GBDT and GIS”. The experimental results illustrates that GBDT along with GIS model combination can identify the crime ranking with high performance and accuracy compared to existing method.”

Big Data Analytics in Cyber Security

2018 ◽  
Vol 6 (7) ◽  
pp. 731-734
Author(s):  
Ashish Bajpai ◽  
Dayanand . ◽  
Arushi Arya
Keyword(s):  
Big Data ◽  
Cyber Security ◽  
Data Analytics ◽  
Big Data Analytics
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