Deep Learning Approaches for IoT Security in the Big Data Era

Big data analytics and deep learning are nowadays two of the most active research areas in computer science. As the data is becoming bigger and bigger, deep learning has a very important role to play in data analytics, and big data technologies will give it huge opportunities for different sectors. Deep learning brings new challenges especially when it comes to large amounts of data, the volume of datasets has to be processed and managed, also data in various applications come in a streaming way and deep learning approaches have to deal with this kind of applications. In this paper, the authors propose two novel approaches for discriminative deep learning, namely LS-DSN, and StreamDSN that are inspired from the deep stacking network algorithm. Two versions of the gradient descent algorithm were used to train the proposed algorithms. The experiment results have shown that the algorithms gave satisfying accuracy results and scale well when the size of data increases. In addition, StreamDSN algorithm have been applied to classify beats of ECG signals and provided good promising results.

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Investigation on Deep Learning Approach for Big Data

Advances in Systems Analysis, Software Engineering, and High Performance Computing - Handbook of Research on Pattern Engineering System Development for Big Data Analytics ◽

10.4018/978-1-5225-3870-7.ch002 ◽

2018 ◽

pp. 25-38

Author(s):

Dharmendra Singh Rajput ◽

T. Sunil Kumar Reddy ◽

Dasari Naga Raju

Keyword(s):

Big Data ◽

Deep Learning ◽

Data Analytics ◽

Probabilistic Models ◽

Big Data Analytics ◽

Data Sets ◽

Semantic Indexing ◽

Learning Approaches ◽

Data Sampling ◽

Learning Mechanisms

In recent years, big data analytics is the major research area where the researchers are focused. Complex structures are trained at each level to simplify the data abstractions. Deep learning algorithms are one of the promising researches for automation of complex data extraction from large data sets. Deep learning mechanisms produce better results in machine learning, such as computer vision, improved classification modelling, probabilistic models of data samples, and invariant data sets. The challenges handled by the big data are fast information retrieval, semantic indexing, extracting complex patterns, and data tagging. Some investigations are concentrated on integration of deep learning approaches with big data analytics which pose some severe challenges like scalability, high dimensionality, data streaming, and distributed computing. Finally, the chapter concludes by posing some questions to develop the future work in semantic indexing, active learning, semi-supervised learning, domain adaptation modelling, data sampling, and data abstractions.

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Novel Scalable Deep Learning Approaches for Big Data Analytics Applied to ECG Processing

Deep Learning and Neural Networks ◽

10.4018/978-1-7998-0414-7.ch035 ◽

2020 ◽

pp. 633-653

Author(s):

Rostom Mennour ◽

Mohamed Batouche

Keyword(s):

Big Data ◽

Deep Learning ◽

Data Analytics ◽

Big Data Analytics ◽

Learning Approaches ◽

Ecg Signals ◽

Research Areas ◽

Gradient Descent Algorithm ◽

Big Data Technologies ◽

Active Research

Big data analytics and deep learning are nowadays two of the most active research areas in computer science. As the data is becoming bigger and bigger, deep learning has a very important role to play in data analytics, and big data technologies will give it huge opportunities for different sectors. Deep learning brings new challenges especially when it comes to large amounts of data, the volume of datasets has to be processed and managed, also data in various applications come in a streaming way and deep learning approaches have to deal with this kind of applications. In this paper, the authors propose two novel approaches for discriminative deep learning, namely LS-DSN, and StreamDSN that are inspired from the deep stacking network algorithm. Two versions of the gradient descent algorithm were used to train the proposed algorithms. The experiment results have shown that the algorithms gave satisfying accuracy results and scale well when the size of data increases. In addition, StreamDSN algorithm have been applied to classify beats of ECG signals and provided good promising results.

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Optimal deep learning approaches and healthcare big data analytics for mobile networks toward 5G

Computers & Electrical Engineering ◽

10.1016/j.compeleceng.2021.107376 ◽

2021 ◽

Vol 95 ◽

pp. 107376

Author(s):

Denis A. Pustokhin ◽

Irina V. Pustokhina ◽

Poonam Rani ◽

Vineet Kansal ◽

Mohamed Elhoseny ◽

...

Keyword(s):

Big Data ◽

Deep Learning ◽

Mobile Networks ◽

Data Analytics ◽

Big Data Analytics ◽

Learning Approaches

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Investigation on Deep Learning Approach for Big Data

Deep Learning and Neural Networks ◽

10.4018/978-1-7998-0414-7.ch056 ◽

2020 ◽

pp. 1016-1029

Author(s):

Dharmendra Singh Rajput ◽

T. Sunil Kumar Reddy ◽

Dasari Naga Raju

Keyword(s):

Big Data ◽

Deep Learning ◽

Data Analytics ◽

Probabilistic Models ◽

Big Data Analytics ◽

Data Sets ◽

Semantic Indexing ◽

Learning Approaches ◽

Data Sampling ◽

Learning Mechanisms

In recent years, big data analytics is the major research area where the researchers are focused. Complex structures are trained at each level to simplify the data abstractions. Deep learning algorithms are one of the promising researches for automation of complex data extraction from large data sets. Deep learning mechanisms produce better results in machine learning, such as computer vision, improved classification modelling, probabilistic models of data samples, and invariant data sets. The challenges handled by the big data are fast information retrieval, semantic indexing, extracting complex patterns, and data tagging. Some investigations are concentrated on integration of deep learning approaches with big data analytics which pose some severe challenges like scalability, high dimensionality, data streaming, and distributed computing. Finally, the chapter concludes by posing some questions to develop the future work in semantic indexing, active learning, semi-supervised learning, domain adaptation modelling, data sampling, and data abstractions.

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Deep Learning Approaches for Big Data Analysis

2019 6th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) ◽

10.23919/eecsi48112.2019.8977075 ◽

2019 ◽

Author(s):

Naomie Salim

Keyword(s):

Big Data ◽

Deep Learning ◽

Data Analysis ◽

Big Data Analysis ◽

Learning Approaches

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A systematic review on Deep Learning approaches for IoT security

Computer Science Review ◽

10.1016/j.cosrev.2021.100389 ◽

2021 ◽

Vol 40 ◽

pp. 100389

Author(s):

Lerina Aversano ◽

Mario Luca Bernardi ◽

Marta Cimitile ◽

Riccardo Pecori

Keyword(s):

Systematic Review ◽

Deep Learning ◽

Learning Approaches ◽

Iot Security

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Literature review and analysis on big data stream classification techniques

International Journal of Knowledge-based and Intelligent Engineering Systems ◽

10.3233/kes-200042 ◽

2020 ◽

Vol 24 (3) ◽

pp. 205-215

Author(s):

B. Srivani ◽

N. Sandhya ◽

B. Padmaja Rani

Keyword(s):

Neural Network ◽

Big Data ◽

Deep Learning ◽

Data Classification ◽

Support Vector ◽

Complex Data ◽

Learning Approaches ◽

K Nearest Neighbor ◽

Data Stream Classification ◽

Big Data Classification

Rapid growth in technology and information lead the human to witness the improved growth in velocity, volume of data, and variety. The data in the business organizations demonstrate the development of big data applications. Because of the improving demand of applications, analysis of sophisticated streaming big data tends to become a significant area in data mining. One of the significant aspects of the research is employing deep learning approaches for effective extraction of complex data representations. Accordingly, this survey provides the detailed review of big data classification methodologies, like deep learning based techniques, Convolutional Neural Network (CNN) based techniques, K-Nearest Neighbor (KNN) based techniques, Neural Network (NN) based techniques, fuzzy based techniques, and Support vector based techniques, and so on. Moreover, a detailed study is made by concerning the parameters, like evaluation metrics, implementation tool, employed framework, datasets utilized, adopted classification methods, and accuracy range obtained by various techniques. Eventually, the research gaps and issues of various big data classification schemes are presented.

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