Fog-Embedded Deep Learning for the Internet of Things

2019 ◽  
Vol 15 (7) ◽  
pp. 4206-4215 ◽  
Author(s):  
Lingjuan Lyu ◽  
James C. Bezdek ◽  
Xuanli He ◽  
Jiong Jin
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
The Internet ◽  
The Internet Of Things

Role of the Internet of Things and deep learning for the growth of healthcare technology

2021 ◽  
pp. 113-127
Author(s):  
Dinesh Bhatia ◽  
S. Bagyaraj ◽  
S. Arun Karthick ◽  
Animesh Mishra ◽  
Amit Malviya
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
The Internet ◽  
Healthcare Technology ◽  
The Internet Of Things

scholarly journals RPL Attack Detection and Prevention in the Internet of Things Networks Using a GRU Based Deep Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 183678-183689
Author(s):  
Semih Cakir ◽  
Sinan Toklu ◽  
Nesibe Yalcin
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Attack Detection ◽  
The Internet ◽  
The Internet Of Things

scholarly journals Towards Deep-Learning-Driven Intrusion Detection for the Internet of Things

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1977 ◽  
Author(s):  
Geethapriya Thamilarasu ◽  
Shiven Chawla
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Learning Algorithm ◽  
Detection System ◽  
Cyber Attacks ◽  
The Internet ◽  
Critical Systems ◽  
The Internet Of Things

Cyber-attacks on the Internet of Things (IoT) are growing at an alarming rate as devices, applications, and communication networks are becoming increasingly connected and integrated. When attacks on IoT networks go undetected for longer periods, it affects availability of critical systems for end users, increases the number of data breaches and identity theft, drives up the costs and impacts the revenue. It is imperative to detect attacks on IoT systems in near real time to provide effective security and defense. In this paper, we develop an intelligent intrusion-detection system tailored to the IoT environment. Specifically, we use a deep-learning algorithm to detect malicious traffic in IoT networks. The detection solution provides security as a service and facilitates interoperability between various network communication protocols used in IoT. We evaluate our proposed detection framework using both real-network traces for providing a proof of concept, and using simulation for providing evidence of its scalability. Our experimental results confirm that the proposed intrusion-detection system can detect real-world intrusions effectively.

scholarly journals Human Activity Recognition with Deep Learning: Overview, Challenges & Possibilities

2021 ◽  
Author(s):  
Pranjal Kumar
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Activity Recognition ◽  
Human Activity ◽  
Recognition System ◽  
The Internet ◽  
Learning Approaches ◽  
The Public ◽  
Sensory Data ◽  
The Internet Of Things

The growing use of sensor tools and the Internet of Things requires sensors to understand the applications. There are major difficulties in realistic situations, though, that can impact the efficiency of the recognition system. Recently, as the utility of deep learning in many fields has been shown, various deep approaches were researched to tackle the challenges of detection and recognition. We present in this review a sample of specialized deep learning approaches for the identification of sensor-based human behaviour. Next, we present the multi-modal sensory data and include information for the public databases which can be used in different challenge tasks for study. A new taxonomy is then suggested, to organize deep approaches according to challenges. Deep problems and approaches connected to problems are summarized and evaluated to provide an analysis of the ongoing advancement in science. By the conclusion of this research, we are answering unanswered issues and providing perspectives into the future.

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