scholarly journals A Cybersecure P300-Based Brain-to-Computer Interface against Noise-Based and Fake P300 Cyberattacks

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8280
Author(s):  
Giovanni Mezzina ◽  
Valerio F. Annese ◽  
Daniela De Venuto
Keyword(s):  
Artificial Intelligence ◽  
Psychological Safety ◽  
Computer Interface ◽  
Support Vector ◽  
The Internet ◽  
Transmission Channel ◽  
Computer Interfaces ◽  
Eeg Data ◽  
The Internet Of Things

In a progressively interconnected world where the internet of things (IoT), ubiquitous computing, and artificial intelligence are leading to groundbreaking technology, cybersecurity remains an underdeveloped aspect. This is particularly alarming for brain-to-computer interfaces (BCIs), where hackers can threaten the user’s physical and psychological safety. In fact, standard algorithms currently employed in BCI systems are inadequate to deal with cyberattacks. In this paper, we propose a solution to improve the cybersecurity of BCI systems. As a case study, we focus on P300-based BCI systems using support vector machine (SVM) algorithms and EEG data. First, we verified that SVM algorithms are incapable of identifying hacking by simulating a set of cyberattacks using fake P300 signals and noise-based attacks. This was achieved by comparing the performance of several models when validated using real and hacked P300 datasets. Then, we implemented our solution to improve the cybersecurity of the system. The proposed solution is based on an EEG channel mixing approach to identify anomalies in the transmission channel due to hacking. Our study demonstrates that the proposed architecture can successfully identify 99.996% of simulated cyberattacks, implementing a dedicated counteraction that preserves most of BCI functions.

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.

The Future of Human Workers

2018 ◽  
Author(s):  
Mahesh K. Joshi ◽  
J.R. Klein
Keyword(s):  
Artificial Intelligence ◽  
New Technologies ◽  
Mental Capacity ◽  
Machine Intelligence ◽  
The Internet ◽  
Traditional Model ◽  
Global Workforce ◽  
Repetitive Tasks ◽  
The Internet Of Things

New technologies like artificial intelligence, robotics, machine intelligence, and the Internet of Things are seeing repetitive tasks move away from humans to machines. Humans cannot become machines, but machines can become more human-like. The traditional model of educating workers for the workforce is fast becoming irrelevant. There is a massive need for the retooling of human workers. Humans need to be trained to remain focused in a society which is constantly getting bombarded with information. The two basic elements of physical and mental capacity are slowly being taken over by machines and artificial intelligence. This changes the fundamental role of the global workforce.

Development of algorithms for controlling and analyzing data of sugar beet storage processes based on the internet of things and artificial intelligence

2021 ◽  
Author(s):  
A.S. Travov ◽  
Keyword(s):  
Artificial Intelligence ◽  
Internet Of Things ◽  
Sugar Beet ◽  
The Internet ◽  
Storage Process ◽  
The Internet Of Things ◽  
Storage Processes

This article provides an overview of the decision to improve the field storage of sugar beet. The purpose of development is to preserve the crop. Methods of monitoring volumes of piles and microclimate inside them are considered. The method for obtaining data on volumes of piles and the further use thereof for optimizing the storage process is described.

Standards for the Internet of Things: A Case Study in Disaster Response

Computer ◽  
2016 ◽  
Vol 49 (5) ◽  
pp. 87-90 ◽  
Author(s):  
Phillip A. Laplante ◽  
Jeffrey Voas ◽  
Nancy Laplante
Keyword(s):  
Internet Of Things ◽  
Disaster Response ◽  
The Internet ◽  
The Internet Of Things

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.

scholarly journals Edge Computing for the Internet of Things: A Case Study

2018 ◽  
Vol 5 (2) ◽  
pp. 1275-1284 ◽  
Author(s):  
Gopika Premsankar ◽  
Mario Di Francesco ◽  
Tarik Taleb
Keyword(s):  
Internet Of Things ◽  
Edge Computing ◽  
The Internet ◽  
The Internet Of Things
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