scholarly journals Internet of Things (IoT) Authentication and Access Control by Hybrid Deep Learning Method - A Study

2021 ◽  
Vol 2 (4) ◽  
pp. 236-245
Author(s):  
Joy Iong Zong Chen ◽  
Kong-Long Lai
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Real Time ◽  
Control Device ◽  
Security And Privacy ◽  
The Real ◽  
Intelligent Technique ◽  
History Of ◽  
Iot Devices ◽  
Future Direction

In the history of device computing, Internet of Things (IoT) is one of the fastest growing field that facing many security challenges. The effective efforts should have been made to address the security and privacy issues in IoT networks. The IoT devices are basically resource control device which provide routine attract impression for cyber attackers. The IoT participation nodes are increasing rapidly with more resource constrained that creating more challenging conditions in the real time. The existing methods provide an ineffective response to the tasks for effective IoT device. Also, it is an insufficient to involve the complete security and safety spectrum of the IoT networks. Because of the existing algorithms are not enriched to secure IoT bionetwork in the real time environment. The existing system is not enough to detect the proxy to the authorized person in the embedding devices. Also, those methods are believed in single model domain. Therefore, the effectiveness is dropping for further multimodal domain such as combination of behavioral and physiological features. The embedding intelligent technique will be securitizing for the IoT devices and networks by deep learning (DL) techniques. The DL method is addressing different security and safety problems arise in real time environment. This paper is highlighting hybrid DL techniques with Reinforcement Learning (RL) for the better performance during attack and compared with existing one. Also, here we discussed about DL combined with RL of several techniques and identify the higher accuracy algorithm for security solutions. Finally, we discuss the future direction of decision making of DL based IoT security system.

scholarly journals Internet of Things: A Comprehensive Study on Machine Learning-based Intrusion Detection approaches

2021 ◽  
Author(s):  
Priyanka Gupta ◽  
Lokesh Yadav ◽  
Deepak Singh Tomar
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Real Time ◽  
Detection System ◽  
Research Work ◽  
Security And Privacy ◽  
Iot Security ◽  
Advantages And Disadvantages ◽  
Iot Devices

The Internet of Things (IoT) connects billions of interconnected devices that can exchange information with each other with minimal user intervention. The goal of IoT to become accessible to anyone, anytime, and anywhere. IoT has engaged in multiple fields, including education, healthcare, businesses, and smart home. Security and privacy issues have been significant obstacles to the widespread adoption of IoT. IoT devices cannot be entirely secure from threats; detecting attacks in real-time is essential for securing devices. In the real-time communication domain and especially in IoT, security and protection are the major issues. The resource-constrained nature of IoT devices makes traditional security techniques difficult. In this paper, the research work carried out in IoT Intrusion Detection System is presented. The Machine learning methods are explored to provide an effective security solution for IoT Intrusion Detection systems. Then discussed the advantages and disadvantages of the selected methodology. Further, the datasets used in IoT security are also discussed. Finally, the examination of the open issues and directions for future trends are also provided.

Smart Surveillance System Using Computer Vision and Internet of Things

2020 ◽  
Vol 17 (1) ◽  
pp. 68-73
Author(s):  
M. Hemaanand ◽  
V. Sanjay Kumar ◽  
R. Karthika
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Real Time ◽  
Tracking System ◽  
Single Shot ◽  
Stream Data ◽  
Advanced Technique ◽  
The Real ◽  
Smart Surveillance ◽  
Bounding Boxes

With the evolution of technology ensuring people for their safety and security all around the time constantly is a big challenge. We propose an advanced technique based on deep learning and artificial intelligence platform that can monitor the people, their homes and their surroundings providing them a quantifiable increase in security. We have surveillance cameras in our homes for video capture as well as security purposes. Our proposed technique is to detect and classify as well as inform the user if there is any breach in security of the classified object using the cameras by implementing deep learning techniques and the technology of internet of things. It can serve as a perimeter monitoring and intruder alert system in smart surveillance environment. This paper provides a well-defined structure for live stream data analysis. It overcomes the challenge of static closed circuit cameras television as it serves as a motion based tracking system and monitors events in real time to ensure activities are limited to specific persons within authorized areas. It has the advantage of creating multiple bounding boxes to track down the objects which could be any living or non-living thing based on the trained modules. The trespasser or intruder can be efficiently detected using the CCTV camera surveillance which is being supported by the real-time object classifier algorithm at the intermediate module. The proposed method is mainly supported by the real time object detection and classification which is implemented using Mobile Net and Single shot detector.

scholarly journals A Hybrid Optimized LSTM Models for Human Activity Recognition with IOT Devices

2021 ◽  
pp. 182-189
Author(s):  
S. Arokiaraj ◽  
Dr. N. Viswanathan
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Short Term Memory ◽  
The Other ◽  
Learning Models ◽  
Computational Overhead ◽  
Temporal Features ◽  
Human Movements ◽  
Proposed Model ◽  
Iot Devices

With the advent of Internet of things(IoT),HA (HA) recognition has contributed the more application in health care in terms of diagnosis and Clinical process. These devices must be aware of human movements to provide better aid in the clinical applications as well as user’s daily activity.Also , In addition to machine and deep learning algorithms, HA recognition systems has significantly improved in terms of high accurate recognition. However, the most of the existing models designed needs improvisation in terms of accuracy and computational overhead. In this research paper, we proposed a BAT optimized Long Short term Memory (BAT-LSTM) for an effective recognition of human activities using real time IoT systems. The data are collected by implanting the Internet of things) devices invasively. Then, proposed BAT-LSTM is deployed to extract the temporal features which are then used for classification to HA. Nearly 10,0000 dataset were collected and used for evaluating the proposed model. For the validation of proposed framework, accuracy, precision, recall, specificity and F1-score parameters are chosen and comparison is done with the other state-of-art deep learning models. The finding shows the proposed model outperforms the other learning models and finds its suitability for the HA recognition.

scholarly journals AdaGUM: An Adaptive Graph Updating Model-Based Anomaly Detection Method for Edge Computing Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xiang Yu ◽  
Chun Shan ◽  
Jilong Bian ◽  
Xianfei Yang ◽  
Ying Chen ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Anomaly Detection ◽  
Real Time ◽  
Detection Method ◽  
Edge Computing ◽  
Sensor Data ◽  
Computing Environment ◽  
The Real ◽  
Edge Node ◽  
Model Based

With the rapid development of Internet of Things (IoT), massive sensor data are being generated by the sensors deployed everywhere at an unprecedented rate. As the number of Internet of Things devices is estimated to grow to 25 billion by 2021, when facing the explicit or implicit anomalies in the real-time sensor data collected from Internet of Things devices, it is necessary to develop an effective and efficient anomaly detection method for IoT devices. Recent advances in the edge computing have significant impacts on the solution of anomaly detection in IoT. In this study, an adaptive graph updating model is first presented, based on which a novel anomaly detection method for edge computing environment is then proposed. At the cloud center, the unknown patterns are classified by a deep leaning model, based on the classification results, the feature graphs are updated periodically, and the classification results are constantly transmitted to each edge node where a cache is employed to keep the newly emerging anomalies or normal patterns temporarily until the edge node receives a newly updated feature graph. Finally, a series of comparison experiments are conducted to demonstrate the effectiveness of the proposed anomaly detection method for edge computing. And the results show that the proposed method can detect the anomalies in the real-time sensor data efficiently and accurately. More than that, the proposed method performs well when there exist newly emerging patterns, no matter they are anomalous or normal.

Toward Polymorphic Internet of Things Receivers Through Real-Time Waveform-Level Deep Learning

2022 ◽  
Vol 25 (3) ◽  
pp. 28-33
Author(s):  
Francesco Restuccia ◽  
Tommaso Melodia
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Real Time ◽  
Software Defined Radio ◽  
Wireless Systems ◽  
Physical World ◽  
Wireless Protocols ◽  
Key Innovation ◽  
Custom Made ◽  
Time Waveform

Wireless systems such as the Internet of Things (IoT) are changing the way we interact with the cyber and the physical world. As IoT systems become more and more pervasive, it is imperative to design wireless protocols that can effectively and efficiently support IoT devices and operations. On the other hand, today's IoT wireless systems are based on inflexible designs, which makes them inefficient and prone to a variety of wireless attacks. In this paper, we introduce the new notion of a deep learning-based polymorphic IoT receiver, able to reconfigure its waveform demodulation strategy itself in real time, based on the inferred waveform parameters. Our key innovation is the introduction of a novel embedded deep learning architecture that enables the solution of waveform inference problems, which is then integrated into a generalized hardware/software architecture with radio components and signal processing. Our polymorphic wireless receiver is prototyped on a custom-made software-defined radio platform. We show through extensive over-the-air experiments that the system achieves throughput within 87% of a perfect-knowledge Oracle system, thus demonstrating for the first time that polymorphic receivers are feasible.

IoT Device Onboarding, Monitoring, and Management

2021 ◽  
pp. 227-255
Author(s):  
Selvaraj Kesavan ◽  
Senthilkumar J. ◽  
Suresh Y. ◽  
Mohanraj V.
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Secure Communication ◽  
Mutual Authentication ◽  
Modern World ◽  
Cloud Platform ◽  
Privacy And Security ◽  
The Real ◽  
Healthy Environment

In establishing a healthy environment for connectivity devices, it is essential to ensure that privacy and security of connectivity devices are well protected. The modern world lives on data, information, and connectivity. Various kinds of sensors and edge devices stream large volumes of data to the cloud platform for storing, processing, and deriving insights. An internet of things (IoT) system poses certain difficulties in discretely identifying, remotely configuring, and controlling the devices, and in the safe transmission of data. Mutual authentication of devices and networks is crucial to initiate secure communication. It is important to keep the data in a secure manner during transmission and in store. Remotely operated devices help to monitor, control, and manage the IoT system efficiently. This chapter presents a review of the approaches and methodologies employed for certificate provisioning, device onboarding, monitoring, managing, and configuring of IoT systems. It also examines the real time challenges and limitations in and future scope for IoT systems.

Applying Security to a Big Stream Cloud Architecture for the Internet of Things

2020 ◽  
pp. 1260-1284
Author(s):  
Laura Belli ◽  
Simone Cirani ◽  
Luca Davoli ◽  
Gianluigi Ferrari ◽  
Lorenzo Melegari ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Large Data ◽  
The Internet ◽  
Low Latency ◽  
Stream Data ◽  
Memory Processing ◽  
Cloud Architecture ◽  
Iot Devices ◽  
The Internet Of Things

The Internet of Things (IoT) is expected to interconnect billions (around 50 by 2020) of heterogeneous sensor/actuator-equipped devices denoted as “Smart Objects” (SOs), characterized by constrained resources in terms of memory, processing, and communication reliability. Several IoT applications have real-time and low-latency requirements and must rely on architectures specifically designed to manage gigantic streams of information (in terms of number of data sources and transmission data rate). We refer to “Big Stream” as the paradigm which best fits the selected IoT scenario, in contrast to the traditional “Big Data” concept, which does not consider real-time constraints. Moreover, there are many security concerns related to IoT devices and to the Cloud. In this paper, we analyze security aspects in a novel Cloud architecture for Big Stream applications, which efficiently handles Big Stream data through a Graph-based platform and delivers processed data to consumers, with low latency. The authors detail each module defined in the system architecture, describing all refinements required to make the platform able to secure large data streams. An experimentation is also conducted in order to evaluate the performance of the proposed architecture when integrating security mechanisms.

scholarly journals Setting Up Telemedicine: Internet of Things to Support Rural Oncology Clinics in a Low- and Middle-Income Country—A Pilot Project in Kenya

2020 ◽  
Vol 6 (Supplement_1) ◽  
pp. 58-58
Author(s):  
Lamech Sigu ◽  
Fredrick Chite ◽  
Emma Achieng ◽  
Andrew Koech
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Length Of Hospital Stay ◽  
Human Interaction ◽  
Sub Saharan Africa ◽  
Patient Treatment ◽  
The Internet ◽  
Tumor Boards ◽  
Iot Devices ◽  
Set Up

PURPOSE The Internet of Things (IoT) is a technology that involves all things connected to the Internet that share data over a network without requiring human-to-human interaction or human-to-computer interaction. Information collected from IoT devices can help physicians identify the best treatment process for patients and reach accurate and expected outcomes. METHODS The International Cancer Institute is partnering to set up remote oncology clinics in sub-Saharan Africa. Medical oncologists and expert teams from across the world connect with oncology clinics in other Kenyan counties—Kisumu, Meru, Makueni, Garissa, Kakamega, Bungoma, Siaya, and Vihiga counties. The furthest county is Garissa, approximately 651.1 km from Eldoret, and the nearest is Vihiga at 100.4 km from Eldoret. This study began July 2019, and as of November 30th, the team has hosted 21 sessions with an average of 11 participants attending a session led by a medical oncologist. RESULTS IoT devices have become a way by which a patient gets all the information he or she needs from a physician without going to the clinic. Patient monitoring can be done in real time, allowing access to real-time information with improved patient treatment outcomes and a decrease in cost. Through IoT-enabled devices, the International Cancer Institute has set up weekly virtual tumor boards during which cancer cases are presented and discussed by all participating counties. An online training module on cancer is also offered. Furthermore, remote monitoring of a patient’s health helps to reduce the length of hospital stay and prevents readmissions. CONCLUSION In our setting, which has a few oncologists, use of IoT and tumor boards has helped to improve patient decision support as well as training for general physicians.

A Survey of Authentication Schemes in the Internet of Things

2019 ◽  
Vol 6 (1) ◽  
pp. 15-30 ◽  
Author(s):  
Yasmine Labiod ◽  
Abdelaziz Amara Korba ◽  
Nacira Ghoualmi-Zine
Keyword(s):  
Internet Of Things ◽  
Security Requirements ◽  
Security And Privacy ◽  
The Internet ◽  
Privacy And Security ◽  
Depth Study ◽  
Authentication Schemes ◽  
Iot Devices ◽  
Privacy Issues ◽  
The Internet Of Things

In the recent years, the Internet of Things (IoT) has been widely deployed in different daily life aspects such as home automation, electronic health, the electric grid, etc. Nevertheless, the IoT paradigm raises major security and privacy issues. To secure the IoT devices, many research works have been conducted to counter those issues and discover a better way to remove those risks, or at least reduce their effects on the user's privacy and security requirements. This article mainly focuses on a critical review of the recent authentication techniques for IoT devices. First, this research presents a taxonomy of the current cryptography-based authentication schemes for IoT. In addition, this is followed by a discussion of the limitations, advantages, objectives, and attacks supported of current cryptography-based authentication schemes. Finally, the authors make in-depth study on the most relevant authentication schemes for IoT in the context of users, devices, and architecture that are needed to secure IoT environments and that are needed for improving IoT security and items to be addressed in the future.

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