scholarly journals Framework for Mobile Internet of Things Security Monitoring Based on Big Data Processing and Machine Learning

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 72714-72723 ◽  
Author(s):  
Igor Kotenko ◽  
Igor Saenko ◽  
Alexander Branitskiy
2018 ◽  
Vol 4 (59) ◽  
pp. 5 ◽  
Author(s):  
Igor Vitalievich Kotenko ◽  
Igor Borisovich Saenko ◽  
Alexey Gennadievich Kushnerevich

Author(s):  
Snigdha Sen ◽  
Sonali Agarwal ◽  
Pavan Chakraborty ◽  
Krishna Pratap Singh

Author(s):  
Amitava Choudhury ◽  
Kalpana Rangra

Data type and amount in human society is growing at an amazing speed, which is caused by emerging new services such as cloud computing, internet of things, and location-based services. The era of big data has arrived. As data has been a fundamental resource, how to manage and utilize big data better has attracted much attention. Especially with the development of the internet of things, how to process a large amount of real-time data has become a great challenge in research and applications. Recently, cloud computing technology has attracted much attention to high performance, but how to use cloud computing technology for large-scale real-time data processing has not been studied. In this chapter, various big data processing techniques are discussed.


2020 ◽  
Vol 10 (14) ◽  
pp. 4901
Author(s):  
Waleed Albattah ◽  
Rehan Ullah Khan ◽  
Khalil Khan

Processing big data requires serious computing resources. Because of this challenge, big data processing is an issue not only for algorithms but also for computing resources. This article analyzes a large amount of data from different points of view. One perspective is the processing of reduced collections of big data with less computing resources. Therefore, the study analyzed 40 GB data to test various strategies to reduce data processing. Thus, the goal is to reduce this data, but not to compromise on the detection and model learning in machine learning. Several alternatives were analyzed, and it is found that in many cases and types of settings, data can be reduced to some extent without compromising detection efficiency. Tests of 200 attributes showed that with a performance loss of only 4%, more than 80% of the data could be ignored. The results found in the study, thus provide useful insights into large data analytics.


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