Machine Learning Techniques to Mitigate Security Attacks in IoT

2020 ◽  
pp. 65-93
Author(s):  
Kavi Priya S. ◽  
Vignesh Saravanan K. ◽  
Vijayalakshmi K.
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Information Exchange ◽  
Machine Learning Techniques ◽  
Smart Devices ◽  
The Internet ◽  
Sensitive Data ◽  
Iot Security ◽  
Security Issues ◽  
Learning Techniques

Evolving technologies involve numerous IoT-enabled smart devices that are connected 24-7 to the internet. Existing surveys propose there are 6 billion devices on the internet and it will increase to 20 billion devices within a few years. Energy conservation, capacity, and computational speed plays an essential part in these smart devices, and they are vulnerable to a wide range of security attack challenges. Major concerns still lurk around the IoT ecosystem due to security threats. Major IoT security concerns are Denial of service(DoS), Sensitive Data Exposure, Unauthorized Device Access, etc. The main motivation of this chapter is to brief all the security issues existing in the internet of things (IoT) along with an analysis of the privacy issues. The chapter mainly focuses on the security loopholes arising from the information exchange technologies used in internet of things and discusses IoT security solutions based on machine learning techniques including supervised learning, unsupervised learning, and reinforcement learning.

scholarly journals Mitigating Malicious Insider Attacks in the Internet of Things using Supervised Machine Learning Techniques

2021 ◽  
Vol 22 (1) ◽  
pp. 13-28
Author(s):  
Mir Shahnawaz Ahmad ◽  
Shahid Mehraj Shah
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Supervised Machine Learning ◽  
Security Requirements ◽  
Machine Learning Techniques ◽  
Smart Devices ◽  
The Internet ◽  
Prime Field ◽  
Learning Techniques ◽  
The Internet Of Things

The interconnection of large number of smart devices and sensors for critical information gathering and analysis over the internet has given rise to the Internet of Things (IoT) network. In recent times, IoT has emerged as a prime field for solving diverse real-life problems by providing a smart and affordable solutions. The IoT network has various constraints like: limited computational capacity of sensors, heterogeneity of devices, limited energy resource and bandwidth etc. These constraints restrict the use of high-end security mechanisms, thus making these type of networks more vulnerable to various security attacks including malicious insider attacks. Also, it is very difficult to detect such malicious insiders in the network due to their unpredictable behaviour and the ubiquitous nature of IoT network makes the task more difficult. To solve such problems machine learning techniques can be used as they have the ability to learn the behaviour of the system and predict the particular anomaly in the system. So, in this paper we have discussed various security requirements and challenges in the IoT network. We have also applied various supervised machine learning techniques on available IoT dataset to deduce which among them is best suited to detect the malicious insider attacks in the IoT network.

scholarly journals Crop Management with the IoT: An Interdisciplinary Survey

Agronomy ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 181 ◽  
Author(s):  
Giuliano Vitali ◽  
Matteo Francia ◽  
Matteo Golfarelli ◽  
Maurizio Canavari
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Crop Management ◽  
Cropping System ◽  
Heterogeneous Data ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Crop Fields ◽  
Learning Techniques ◽  
The Internet Of Things

In this study, we analyze how crop management will benefit from the Internet of Things (IoT) by providing an overview of its architecture and components from agronomic and technological perspectives. The present analysis highlights that IoT is a mature enabling technology with articulated hardware and software components. Cheap networked devices can sense crop fields at a finer grain to give timeliness warnings on the presence of stress conditions and diseases to a wider range of farmers. Cloud computing allows reliable storage, access to heterogeneous data, and machine-learning techniques for developing and deploying farm services. From this study, it emerges that the Internet of Things will draw attention to sensor quality and placement protocols, while machine learning should be oriented to produce understandable knowledge, which is also useful to enhance cropping system simulation systems.

scholarly journals Attack and Anomaly Detection in IoT Networks using Machine Learning Techniques: A Review

2021 ◽  
pp. 30-46
Author(s):  
Saad Hikmat Haji ◽  
Siddeeq Y. Ameen
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Anomaly Detection ◽  
Attack Detection ◽  
Machine Learning Techniques ◽  
Smart Devices ◽  
The Internet ◽  
Business Services ◽  
Industrial Services ◽  
The Internet Of Things

The Internet of Things (IoT) is one of today's most rapidly growing technologies. It is a technology that allows billions of smart devices or objects known as "Things" to collect different types of data about themselves and their surroundings using various sensors. They may then share it with the authorized parties for various purposes, including controlling and monitoring industrial services or increasing business services or functions. However, the Internet of Things currently faces more security threats than ever before. Machine Learning (ML) has observed a critical technological breakthrough, which has opened several new research avenues to solve current and future IoT challenges. However, Machine Learning is a powerful technology to identify threats and suspected activities in intelligent devices and networks. In this paper, various ML algorithms have been compared in terms of attack detection and anomaly detection, following a thorough literature review on Machine Learning methods and the significance of IoT security in the context of various types of potential attacks. Furthermore, possible ML-based IoT protection technologies have been introduced.

scholarly journals Automated diagnosis of attacks in internet of things using machine learning and frequency distribution techniques

2021 ◽  
Vol 10 (2) ◽  
pp. 950-961
Author(s):  
Toufik Ghrib ◽  
Mohamed Benmohammed ◽  
Purnendu.Shekhar Pandey
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Ensemble Methods ◽  
Machine Learning Techniques ◽  
Sensitive Data ◽  
Learning Techniques ◽  
Image Set ◽  
Iot Devices ◽  
Table Data ◽  
Multi Class Classification

The Internet of Things (IoT) is the interconnection of things around us to make our daily process more efficient by providing more comfort and productivity. However, these connections also reveal a lot of sensitive data. Therefore, thinking about the methods of information security and coding are important as the security approaches that rely heavily on coding are not a strong match for these restricted devices. Consequently, this research aims to contribute to filling this gap, which adopts machine learning techniques to enhance network-level security in the low-power devices that use the lightweight MQTT protocol for their work. This study used a set of tools tools and, through various techniques, trained the proposed system ranging from Ensemble methods to deep learning models. The system has come to know what type of attack has occurred, which helps protect IoT devices. The log loss of the Ensemble methods is 0.44, and the accuracy of multi-class classification is 98.72% after converting the table data into an image set. The work also uses a Convolution Neural Network, which has a log loss of 0.019 and an accuracy of 99.3%. It also aims to implement these functions in IDS.

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.

scholarly journals Mining knowledge graphs to map heterogeneous relations between the internet of things patterns

2021 ◽  
Vol 11 (6) ◽  
pp. 5066
Author(s):  
Vusi Sithole ◽  
Linda Marshall
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Machine Learning Techniques ◽  
Small Scale ◽  
The Internet ◽  
Use Case ◽  
Object Oriented Design ◽  
Learning Techniques ◽  
Knowledge Graphs ◽  
The Internet Of Things

<span lang="EN-US">Patterns for the internet of things (IoT) which represent proven solutions used to solve design problems in the IoT are numerous. Similar to object-oriented design patterns, these IoT patterns contain multiple mutual heterogeneous relationships. However, these pattern relationships are hidden and virtually unidentified in most documents. In this paper, we use machine learning techniques to automatically mine knowledge graphs to map these relationships between several IoT patterns. The end result is a semantic knowledge graph database which outlines patterns as vertices and their relations as edges. We have identified four main relationships between the IoT patterns-a pattern is similar to another pattern if it addresses the same use case problem, a large-scale pattern uses a small- scale pattern in a lower level layer, a large pattern is composed of multiple smaller scale patterns underneath it, and patterns complement and combine with each other to resolve a given use case problem. Our results show some promising prospects towards the use of machine learning techniques to generate an automated repository to organise the IoT patterns, which are usually extracted at various levels of abstraction and granularity.</span>

Smart IoT Systems

2021 ◽  
pp. 100-119
Author(s):  
Ritu Chauhan ◽  
Sandhya Avasthi ◽  
Bhavya Alankar ◽  
Harleen Kaur
Keyword(s):  
Machine Learning ◽  
Smart Home ◽  
Machine Learning Techniques ◽  
Smart Devices ◽  
The Internet ◽  
Due Diligence ◽  
Everyday Objects ◽  
Learning Techniques ◽  
Smart Home Environment ◽  
The Internet Of Things

The IoT or the internet of things started as a technology to connect everyday objects over the internet, which has evolved into something big and invaded into every single aspect of our lives. As technology is gaining momentum, IoT-based smart devices usage among users is expanding, which generates massive data at our disposal across various domains. The authors have systematically studied the taxonomy of data analytics and the benefits of using advanced machine learning techniques in converting data into valuable assets. In the studies, they have identified and did due diligence on different smart home systems, their features, and configuration. During this course of study, they have also identified the vulnerability of such a system and threats associated with these vulnerabilities in a secure smart home environment.

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