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.

scholarly journals Intelligent Systems for the Internet of Things: Essence, Perspectives and Problems

2021 ◽  
Vol 9 (1) ◽  
pp. 17-25
Author(s):  
Shafagat Mahmudova
Keyword(s):  
Internet Of Things ◽  
Data Sharing ◽  
Intelligent Systems ◽  
New Technology ◽  
Smart Devices ◽  
The Internet ◽  
Transfer Data ◽  
Iot Platform ◽  
High Level ◽  
The Internet Of Things

This article outlines the Internet of Things (IoT). The Internet of Things describes a network of physical objects, i.e., the “thing” including sensors, software, and other technologies for connection and data sharing with other devices and systems over the Internet. In other words, IoT is a relatively new technology enabling many “smart” devices to get connected, to analyze, process, and transfer data to each other and connect to a network. The article clarifies the essence of intelligent systems for the Internet of Things, and analyzes the most popular software for the IoT platform. It studies high-level systems for IoT and analyzes available literature in this field. It highlights most advanced IoT software of 2021. The article also identifies the prospects and challenges of intelligent systems for the Internet of Things. The creation of new intelligent systems for IoT and the development of technology will greatly contribute to the development of economy.

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.

From Users’ Intentions to IF-THEN Rules in the Internet of Things

2021 ◽  
Vol 39 (4) ◽  
pp. 1-33
Author(s):  
Fulvio Corno ◽  
Luigi De Russis ◽  
Alberto Monge Roffarello
Keyword(s):  
Internet Of Things ◽  
Smart Devices ◽  
The Internet ◽  
Short Term ◽  
Real World Data ◽  
Original Intention ◽  
Recommendation Accuracy ◽  
High Level ◽  
The Internet Of Things

In the Internet of Things era, users are willing to personalize the joint behavior of their connected entities, i.e., smart devices and online service, by means of trigger-action rules such as “IF the entrance Nest security camera detects a movement, THEN blink the Philips Hue lamp in the kitchen.” Unfortunately, the spread of new supported technologies makes the number of possible combinations between triggers and actions continuously growing, thus motivating the need of assisting users in discovering new rules and functionality, e.g., through recommendation techniques. To this end, we present , a semantic Conversational Search and Recommendation (CSR) system able to suggest pertinent IF-THEN rules that can be easily deployed in different contexts starting from an abstract user’s need. By exploiting a conversational agent, the user can communicate her current personalization intention by specifying a set of functionality at a high level, e.g., to decrease the temperature of a room when she left it. Stemming from this input, implements a semantic recommendation process that takes into account ( a ) the current user’s intention , ( b ) the connected entities owned by the user, and ( c ) the user’s long-term preferences revealed by her profile. If not satisfied with the suggestions, then the user can converse with the system to provide further feedback, i.e., a short-term preference , thus allowing to provide refined recommendations that better align with the original intention. We evaluate by running different offline experiments with simulated users and real-world data. First, we test the recommendation process in different configurations, and we show that recommendation accuracy and similarity with target items increase as the interaction between the algorithm and the user proceeds. Then, we compare with other similar baseline recommender systems. Results are promising and demonstrate the effectiveness of in recommending IF-THEN rules that satisfy the current personalization intention of the user.

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