New Hybrid Distributed Attack Detection System for IoT

Year 2024, , 232 - 246, 24.03.2024

Çiğdem Bakır

https://doi.org/10.17798/bitlisfen.1380547

Abstract

IoT is expressed as a network of physical objects with applications and various technologies that provide data connection and sharing with various devices and systems over the Internet. Security vulnerabilities in IoT devices are one of the biggest security issues in connecting devices to the internet and collecting and processing user data. These vulnerabilities can lead to increased attacks on IoT devices and malicious use of user data. In this article, we discuss these security problems that arise in IoT systems in detail in distributed systems technology. Distributed systems are increasingly used in the modern computing world. These systems are a structure where multiple independent computers communicate with each other for a common purpose. Distributed system technologies have become more common with the development of internet and cloud computing systems. However, the use of distributed systems has brought with it important security challenges such as security vulnerabilities, access controls and data integrity issues. Therefore, the security of distributed system technologies has been an important focus of work in this area. In this study, information about distributed system technologies and security for IoT is given. The all attack types were classified using ANN, developed RF and hybrid model. In RF, all feature vectors created from all datasets (bank and two financial datasets) were also analyzed separately and the classification performance was examined. In addition, a new RF algorithm based on weight values using the Gini algorithm has been proposed. With this algorithm, the traditional RF algorithm has been developed and the success rates have been increased. In addition, a hybrid method was created by classifying the datasets obtained by RF with ANN. With the hybrid method ANN and the enhanced RF method, its accuracy in detecting normal behaviors and attack types was calculated and the success of the methods was presented comparatively. In addition, the working times of the methods were determined.

Keywords

Random Forest, Artificial Neural Network, Security, IoT, Distributed System.

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