Artificial Neural Network for the Internet of Things Security

2020 ◽  
Vol 68 (11) ◽  
pp. 129-136
Author(s):  
Amit Sagu ◽  
Nasib Singh Gill ◽  
Preeti Gulia
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Internet Of Things ◽  
The Internet ◽  
Artificial Neural ◽  
The Internet Of Things

scholarly journals Security-Aware Routing Protocol Based on Artificial Neural Network Algorithm and 6LoWPAN in the Internet of Things

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Jiangdong Lu ◽  
Dongfang Li ◽  
Penglong Wang ◽  
Fen Zheng ◽  
Meng Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Internet Of Things ◽  
Routing Protocols ◽  
Intelligent Systems ◽  
Smart Devices ◽  
The Internet ◽  
Routing Mechanism ◽  
Artificial Neural ◽  
The Internet Of Things

Today, with increasing information technology such as the Internet of Things (IoT) in human life, interconnection and routing protocols need to find optimal solution for safe data transformation with various smart devices. Therefore, it is necessary to provide an enhanced solution to address routing issues with respect to new interconnection methodologies such as the 6LoWPAN protocol. The artificial neural network (ANN) is based on the structure of intelligent systems as a branch of machine interference, has shown magnificent results in previous studies to optimize security-aware routing protocols. In addition, IoT devices generate large amounts of data with variety and accuracy. Therefore, higher performance and better data handling can be achieved when this technology incorporates data for sending and receiving nodes in the environment. Therefore, this study presents a security-aware routing mechanism for IoT technologies. In addition, a comparative analysis of the relationship between previous approaches discusses with quality of service (QoS) factors such as throughput and accuracy for improving routing mechanism. Experimental results show that the use of time-division multiple access (TDMA) method to schedule the sending and receiving of data and the use of the 6LoWPAN protocol when routing the sending and receiving of data can carry out attacks with high accuracy.

Neural network system for detecting and neutralizing remote unauthorized interference into the components of the Internet of Things

2021 ◽  
Author(s):  
A. I. Vlasov ◽  
E. R. Zakharov ◽  
V. O. Zakharova
Keyword(s):  
Neural Network ◽  
Internet Of Things ◽  
Normal Modes ◽  
The Internet ◽  
Network System ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Neural Network System ◽  
Artificial Neural ◽  
The Internet Of Things

In this work the authors have analyzed the neural network system for detecting and neutralizing remote and unauthorized interference with components of the Internet of Things. The main focus is on considering the neural network approach to detecting intrusions into the Internet of Things network, its monitoring and countering suspicious activity on the host. Features of development of model of artificial neural networks for application of apparatus of neural network in this direction have been considered. This allows you to reflect the successful identification of various types of attacks in terms of true and false positive results. However, the problems of obtaining data on overload and critical modes of the system remain unresolved. The use of a neural network system for detecting and neutralizing remote and unauthorized interference with components of the Internet of Things allows you to implement a module for detecting anomalies in the network, based on the Voltaire series, which considers the theoretical prerequisites of the method of dynamically building an artificial neural network. The main types of attacks, types of intrusion detection systems, interpretations of the obtained data, a brief study of works in the field of neural network solutions have been analyzed. An effective solution has been offered to protect workstations in the Internet of Things network from unauthorized access, and to configure security for all component modules. In conclusion, recommendations have been given for implementing the construction of a neural network module that detects deviations in the operation of the Internet of Things from normal modes.

scholarly journals Analysis of Usability for the Dice CAPTCHA

Information ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 221
Author(s):  
Alessia Amelio ◽  
Ivo Rumenov Draganov ◽  
Radmila Janković ◽  
Dejan Tanikić
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Association Rule ◽  
Association Rule Mining ◽  
Internet Usage ◽  
Solution Time ◽  
The Internet ◽  
Rule Mining ◽  
Artificial Neural

This paper explores the usability of the Dice CAPTCHA via analysis of the time spent to solve the CAPTCHA, and number of tries for solving the CAPTCHA. The experiment was conducted on a set of 197 subjects who use the Internet, and are discriminated by age, daily Internet usage in hours, Internet experience in years, and type of device where a solution to the CAPTCHA is found. Each user was asked to find a solution to the Dice CAPTCHA on a tablet or laptop, and the time to successfully find a solution to the CAPTCHA for a given number of attempts was registered. Analysis was performed on the collected data via association rule mining and artificial neural network. It revealed that the time to find a solution in a given number of attempts of the CAPTCHA depended on different combinations of values of user’s features, as well as the most meaningful features influencing the solution time. In addition, this dependence was explored through prediction of the CAPTCHA solution time from the user’s features via artificial neural network. The obtained results are very helpful to analyze the combination of features having an influence on the CAPTCHA solution, and consequently, to find the CAPTCHA mostly complying to the postulate of “ideal” test.

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