scholarly journals Trust Assessment in Internet of Things Using Blockchain and Machine Learning

2020 ◽  
Author(s):  
Liming Wang ◽  
Hongqin Zhu ◽  
Jiawei Sun ◽  
Ran Dai ◽  
Qi Ma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Trust Management ◽  
Identity Management ◽  
Edge Computing ◽  
Management Model ◽  
Learning Methods ◽  
Transaction Model ◽  
Machine Learning Methods ◽  
Iot Devices

Abstract Since IoT devices are strengthened, edge computing with multi-center cooperation becomes a trend. Considering that edge nodes may belong to different center, they have different trust management model, it’s hard to assess trust among edge nodes. In this paper, we take blockchain to coordinate differences among centers, construct a trust environment for transactions in IoT. In detail, we propose a blockchain based identity management for IoT to ensure identity is credible, then design a transaction model to provide certification for IoT transactions. And, we take machine learning methods to analyze IoT transaction log, thus decide trust nodes or not. Experiment results show that our mechanism could effectively identify trustworthy edges in IoT.

scholarly journals Intrusion detection in internet of things networks based on machine learning methods

2021 ◽  
pp. 42-52
Author(s):  
Tatiana Tatarnikova ◽  
Pavel Bogdanov
Keyword(s):  
Machine Learning ◽  
Wireless Sensor Network ◽  
Internet Of Things ◽  
Sensor Network ◽  
Attack Detection ◽  
Wireless Sensor ◽  
Learning Methods ◽  
Network Attacks ◽  
Machine Learning Methods ◽  
Iot Devices

Introduction: The growing amount of digital data generated, among others, by smart devices of the Internet of Things makes it important to study the application of machine learning methods to the detection of network traffic anomalies, namely the presence of network attacks. Purpose: To propose a unified approach to detecting attacks at different levels of IoT network architecture, based on machine learning methods. Results: It was shown that at the wireless sensor network level, attack detection is associated with the detection of anomalous behavior of IoT devices, when the deviation of an IoT device behavior from its profile exceeds a predetermined level. Smart IoT devices are profiled on the basis of statistical characteristics, such as the intensity and duration of packet transmission, the proportion of retransmitted packets, etc. At the level of a local or global wired IoT network, data is aggregated and then analyzed using machine learning methods. Trained classifiers can become a part of a network attack detection system, making decisions about compromising a node on the fly. Models of classifiers of network attacks were experimentally selected both at the level of a wireless sensor network and at the level of a local or global wired network. The best results in terms of completeness and accuracy estimates are demonstrated by the random forest method for a wired local and/or global network and by all the considered methods for a wireless sensor network. Practical relevance: The proposed models of classifiers can be used for developing intrusion detection systems in IoT networks.

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 Usages of Spark Framework with Different Machine Learning Algorithms

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Mohamed Ali Mohamed ◽  
Ibrahim Mahmoud El-henawy ◽  
Ahmad Salah
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Internet Of Things ◽  
Large Data ◽  
Machine Learning Algorithms ◽  
Smart Devices ◽  
The Internet ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
The Internet Of Things

Sensors, satellites, mobile devices, social media, e-commerce, and the Internet, among others, saturate us with data. The Internet of Things, in particular, enables massive amounts of data to be generated more quickly. The Internet of Things is a term that describes the process of connecting computers, smart devices, and other data-generating equipment to a network and transmitting data. As a result, data is produced and updated on a regular basis to reflect changes in all areas and activities. As a consequence of this exponential growth of data, a new term and idea known as big data have been coined. Big data is required to illuminate the relationships between things, forecast future trends, and provide more information to decision-makers. The major problem at present, however, is how to effectively collect and evaluate massive amounts of diverse and complicated data. In some sectors or applications, machine learning models are the most frequently utilized methods for interpreting and analyzing data and obtaining important information. On their own, traditional machine learning methods are unable to successfully handle large data problems. This article gives an introduction to Spark architecture as a platform that machine learning methods may utilize to address issues regarding the design and execution of large data systems. This article focuses on three machine learning types, including regression, classification, and clustering, and how they can be applied on top of the Spark platform.

scholarly journals A novel trust management model for edge computing

2021 ◽  
Author(s):  
Rabia Latif ◽  
Malik Uzair Ahmed ◽  
Shahzaib Tahir ◽  
Seemab Latif ◽  
Waseem Iqbal ◽  
...  
Keyword(s):  
Trust Management ◽  
Service Level ◽  
Edge Computing ◽  
Management Model ◽  
Security Risks ◽  
Computing Paradigm ◽  
Malicious Behavior ◽  
Processing Power ◽  
Qos Parameters ◽  
Iot Devices

AbstractEdge computing is a distributed architecture that features decentralized processing of data near the source/devices, where data are being generated. These devices are known as Internet of Things (IoT) devices or edge devices. As we continue to rely on IoT devices, the amount of data generated by the IoT devices have increased significantly due to which it has become infeasible to transfer all the data over to the Cloud for processing. Since these devices contain insufficient storage and processing power, it gives rise to the edge computing paradigm. In edge computing data are processed by edge devices and only the required data are sent to the Cloud to increase robustness and decrease overall network overhead. IoT edge devices are inherently suffering from various security risks and attacks causing a lack of trust between devices. To reduce this malicious behavior, a lightweight trust management model is proposed that maintains the trust of a device and manages the service level trust along with quality of service (QoS). The model calculates the overall trust of the devices by using QoS parameters to evaluate the trust of devices through assigned weights. Trust management models using QoS parameters show improved results that can be helpful in identifying malicious edge nodes in edge computing networks and can be used for industrial purposes.

scholarly journals Social media analytics of the Internet of Things

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Jim A. 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

AbstractThe 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 research illuminates the public view of the Internet of Things through a content-based and network 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 2-week period. Using supervised and unsupervised machine learning methods, we have identified trends within the realm of IoT and their interconnecting relationships between the most mentioned industries. We have identified characteristics of language sentiment which can help to predict the popularity of IoT conversation topics. 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. Recent breaches and ransomware events denote that organizations should spend more time communicating about risks and mitigations. 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. We propose an IoT Cybersecurity Communication Scorecard to help organizations benchmark the density and sentiment of their corporate communications regarding security against their specific industry.

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