scholarly journals A State of the Art Survey of Machine Learning Algorithms for IoT Security

2021 ◽  
pp. 12-34
Author(s):  
Alan Fuad Jahwar ◽  
Subhi R. M. Zeebaree
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Internet Of Things ◽  
Smart Cities ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Security Measures ◽  
Iot Security ◽  
Security Issues ◽  
The Internet Of Things

The Internet of Things (IoT) is a paradigm shift that enables billions of devices to connect to the Internet. The IoT's diverse application domains, including smart cities, smart homes, and e-health, have created new challenges, chief among them security threats. To accommodate the current networking model, traditional security measures such as firewalls and Intrusion Detection Systems (IDS) must be modified. Additionally, the Internet of Things and Cloud Computing complement one another, frequently used interchangeably when discussing technical services and collaborating to provide a more comprehensive IoT service. In this review, we focus on recent Machine Learning (ML) and Deep Learning (DL) algorithms proposed in IoT security, which can be used to address various security issues. This paper systematically reviews the architecture of IoT applications, the security aspect of IoT, service models of cloud computing, and cloud deployment models. Finally, we discuss the latest ML and DL strategies for solving various security issues in IoT networks.

THE FUTURE OF SMART INDUSTRY: TECHNOLOGIES, MACRO-TRENDS AND APPLICATION AREAS

10.6036/10342 ◽  
2021 ◽  
Vol 96 (6) ◽  
pp. 561-562
Author(s):  
MIKEL NIÑO
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Big Data ◽  
Data Analysis ◽  
Internet Of Things ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Big Data Technologies ◽  
Smart Industry ◽  
The Internet Of Things

The Smart Industry has been developing has been developing at an accelerated pace since the beginning of the last decade, driven by of the last decade, driven by the by the emergence of technologies such as the Internet of Things, Compute of Things, Cloud Computing and Big Data Cloud Computing and Big Data technologies, as well as their connection and Big Data technologies, as well as their connection with machine learning algorithms for predictive data analysis [1] of data [1].

Detecting Abnormal Behavior of an IoT Device in the Network Based on a Traffic Model

Telecom IT ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 50-55
Author(s):  
D. Saharov ◽  
D. Kozlov
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Mobile Networks ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Abnormal Behavior ◽  
Traffic Model ◽  
The Internet ◽  
Wide Spread ◽  
The Internet Of Things

The article deals with the СoAP Protocol that regulates the transmission and reception of information traf-fic by terminal devices in IoT networks. The article describes a model for detecting abnormal traffic in 5G/IoT networks using machine learning algorithms, as well as the main methods for solving this prob-lem. The relevance of the article is due to the wide spread of the Internet of things and the upcoming update of mobile networks to the 5g generation.

scholarly journals IMPROVING INTERNET OF THINGS PARKING SYSTEMS

2021 ◽  
Vol 19 (3) ◽  
pp. 163
Author(s):  
Dušan Bogićević
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cloud Computing ◽  
Internet Of Things ◽  
Information Needs ◽  
Vehicle Classification ◽  
The Internet ◽  
Parking Space ◽  
Automated Systems ◽  
The Internet Of Things

Edge data processing represents the new evolution of the Internet and Cloud computing. Its application to the Internet of Things (IoT) is a step towards faster processing of information from sensors for better performance. In automated systems, we have a large number of sensors, whose information needs to be processed in the shortest possible time and acted upon. The paper describes the possibility of applying Artificial Intelligence on Edge devices using the example of finding a parking space for a vehicle, and directing it based on the segment the vehicle belongs to. Algorithm of Machine Learning is used for vehicle classification, which is based on vehicle dimensions.

Machine learning algorithms for smart data analysis in the Internet of things: an overview

2021 ◽  
pp. 307-327
Author(s):  
Mohammed H. Alsharif ◽  
Anabi Hilary Kelechi ◽  
Imran Khan ◽  
Mahmoud A. Albreem ◽  
Abu Jahid ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Internet Of Things ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Smart Data ◽  
The Internet Of Things

scholarly journals Management of Software-Defined Networking Powered by Artificial Intelligence

2021 ◽  
Author(s):  
Jehad Ali ◽  
Byeong-hee Roh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Internet Of Things ◽  
Heterogeneous Networks ◽  
Software Defined Networking ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Underlying Network ◽  
And Control ◽  
The Internet Of Things

Separating data and control planes by Software-Defined Networking (SDN) not only handles networks centrally and smartly. However, through implementing innovative protocols by centralized controllers, it also contributes flexibility to computer networks. The Internet-of-Things (IoT) and the implementation of 5G have increased the number of heterogeneous connected devices, creating a huge amount of data. Hence, the incorporation of Artificial Intelligence (AI) and Machine Learning is significant. Thanks to SDN controllers, which are programmable and versatile enough to incorporate machine learning algorithms to handle the underlying networks while keeping the network abstracted from controller applications. In this chapter, a software-defined networking management system powered by AI (SDNMS-PAI) is proposed for end-to-end (E2E) heterogeneous networks. By applying artificial intelligence to the controller, we will demonstrate this regarding E2E resource management. SDNMS-PAI provides an architecture with a global view of the underlying network and manages the E2E heterogeneous networks with AI learning.

scholarly journals Social Media Analytics of the Internet of Things

2021 ◽  
Author(s):  
Jim Scheibmeir ◽  
Yashwant K. Malaiya
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Internet Of Things ◽  
Smart Cities ◽  
The Internet ◽  
Social Media Analytics ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Internet Of Things Technology ◽  
The Internet Of Things

Abstract The Internet of Things technology offers convenience and innovation in areas such as smart homes and smart cities. Internet of Things solutions require careful management of devices and the risk mitigation of potential vulnerabilities within cyber-physical systems. The Internet of Things concept, its implementations, and applications are frequently discussed on social media platforms. This article illuminates the public view of the Internet of Things through a content-based analysis of contemporary conversations occurring on the Twitter platform. Tweets can be analyzed with machine learning methods to converge the volume and variety of conversations into predictive and descriptive models. We have reviewed 684,503 tweets collected in a two-week period. Using supervised and unsupervised machine learning methods, we have identified interconnecting relationships between trending themes and the most mentioned industries. We have identified characteristics of language sentiment which can help to predict popularity within the realm of IoT conversation. We found the healthcare industry as the leading use case industry for IoT implementations. This is not surprising as the current Covid-19 pandemic is driving significant social media discussions. There was an alarming dearth of conversations towards cybersecurity. Only 12% of the tweets relating to the Internet of Things contained any mention of topics such as encryption, vulnerabilities, or risk, among other cybersecurity-related terms.

scholarly journals A Survey on IoT related Security Issues, Challenges And their Solutions

2020 ◽  
pp. 378-383
Keyword(s):  
Internet Of Things ◽  
Smart Cities ◽  
Smart Homes ◽  
Human Interaction ◽  
Security And Privacy ◽  
The Internet ◽  
Security Issues ◽  
High Interest ◽  
The Future ◽  
The Internet Of Things

The future of Internet of Things (IoT) is already upon us. The Internet of Things (IoT) is the ability to provide everyday devices with a way of identification and another way for communication with each other. The spectrum of IoT application domains is very large including smart homes, smart cities, wearables, e-health, etc. Consequently, tens and even hundreds of billions of devices will be connected. Such devices will have smart capabilities to collect, analyze and even make decisions without any human interaction. Security is a supreme requirement in such circumstances, and in particular authentication is of high interest given the damage that could happen from a malicious unauthenticated device in an IoT system. While enjoying the convenience and efficiency that IoT brings to us, new threats from IoT also have emerged. There are increasing research works to ease these threats, but many problems remain open. To better understand the essential reasons of new threats and the challenges in current research, this survey first proposes the concept of “IoT features”. Then, the security and privacy effects of eight IoT new features were discussed including the threats they cause, existing solutions and challenges yet to be solved.

scholarly journals Security Issues and Challenges in IOT: a Comprehensive Study

2018 ◽  
Vol 7 (2.32) ◽  
pp. 298 ◽  
Author(s):  
M Bhargavi ◽  
Dr M.Nagabhushana Rao
Keyword(s):  
Internet Of Things ◽  
Data Exchange ◽  
Denial Of Service ◽  
The Internet ◽  
Security Architecture ◽  
Iot Security ◽  
Security Issues ◽  
Continuous Support ◽  
The Internet Of Things ◽  
Comprehensive Study

The Internet of Things (IoT) is a revolutionary model, with rising wireless sensor network technology. In IoT network devices are connected and communicated with each other or with human. IoT is extremely available to security assaults. In recent years, the internet of things has a continuous support in research. In the upcoming scenario, IoT will play an important role and changes our day-to-day life, principles as well as industry models. In this paper we provide ensuring security of data exchange, IoT architecture and IoT Security architecture, applications, drawbacks of IoT. We study about various security issues, Problems, normal and Denial of service attacks in different layers, issues and research defy in IoT are also discussed.   

scholarly journals ADLAuth: Passive Authentication Based on Activity of Daily Living Using Heterogeneous Sensing in Smart Cities

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2466 ◽  
Author(s):  
Maryam Naseer Malik ◽  
Muhammad Awais Azam ◽  
Muhammad Ehatisham-Ul-Haq ◽  
Waleed Ejaz ◽  
Asra Khalid
Keyword(s):  
Internet Of Things ◽  
User Authentication ◽  
Smart Cities ◽  
Activity Patterns ◽  
Wearable Sensors ◽  
Machine Learning Algorithms ◽  
Smart Devices ◽  
The Internet ◽  
Healthcare Applications ◽  
The Internet Of Things

The Internet of Things is a rapidly growing paradigm for smart cities that provides a way of communication, identification, and sensing capabilities among physically distributed devices. With the evolution of the Internet of Things (IoTs), user dependence on smart systems and services, such as smart appliances, smartphone, security, and healthcare applications, has been increased. This demands secure authentication mechanisms to preserve the users’ privacy when interacting with smart devices. This paper proposes a heterogeneous framework “ADLAuth” for passive and implicit authentication of the user using either a smartphone’s built-in sensor or wearable sensors by analyzing the physical activity patterns of the users. Multiclass machine learning algorithms are applied to users’ identity verification. Analyses are performed on three different datasets of heterogeneous sensors for a diverse number of activities. A series of experiments have been performed to test the effectiveness of the proposed framework. The results demonstrate the better performance of the proposed scheme compared to existing work for user authentication.

Artificial Intelligence powered Internet of Things and smart public service

2019 ◽  
Vol 38 (1) ◽  
pp. 165-179 ◽  
Author(s):  
Ying Ma ◽  
Kang Ping ◽  
Chen Wu ◽  
Long Chen ◽  
Hui Shi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Internet Of Things ◽  
Public Service ◽  
Smart Cities ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Content Type ◽  
Academic Fields ◽  
The Internet Of Things

Purpose The Internet of Things (IoT) has attracted a lot of attention in both industrial and academic fields for recent years. Artificial intelligence (AI) has developed rapidly in recent years as well. AI naturally combines with the Internet of Things in various ways, enabling big data applications, machine learning algorithms, deep learning, knowledge discovery, neural networks and other technologies. The purpose of this paper is to provide state of the art in AI powered IoT and study smart public services in China. Design/methodology/approach This paper reviewed the articles published on AI powered IoT from 2009 to 2018. Case study as a research method has been chosen. Findings The AI powered IoT has been found in the areas of smart cities, healthcare, intelligent manufacturing and so on. First, this study summarizes recent research on AI powered IoT systematically; and second, this study identifies key research topics related to the field and real-world applications. Originality/value This research is of importance and significance to both industrial and academic fields researchers who need to understand the current and future development of intelligence in IoT. To the best of authors’ knowledge, this is the first study to review the literature on AI powered IoT from 2009 to 2018. This is also the first literature review on AI powered IoT with a case study of smart public service in China.

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