Investigating the method of intelligent defense mechanism against DDOS attacks of IOT
Subject Areas : information technology
reza ghaffari dizaji
1
(Master's student of Azad Technical College, West Tehran branch)
Keywords: Internet of Things security, DDoS Attack, edge computing, artificial intelligence,
Abstract :
The Internet of Things is a leading technology that enables widespread connectivity of various devices to provide services and automation in various areas of daily life to critical infrastructure systems. However, these devices are vulnerable to various attacks, including Distributed Denial of Service (DDoS) attacks. The goal is to deploy a valid device and prevent legitimate users from accessing services or network resources. These attacks can be carried out through distributed attack resources, diverse attack resources, and traffic variations. An intelligent defense method called Flow Guardian is presented to combat these attacks. This method involves detection, identification, classification, and reduction of attacks, with two main components called Flow Filter and Flow Initiator used for identification, detection, classification, and reduction of attacks. An attack detection algorithm based on traffic variations is presented, and two machine learning models called Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) are proposed for the identification and classification of DDoS attacks. These models can be used with appropriate delay in edge servers, which have higher computational power compared to a personal computer. Solutions to overcome limitations and weaknesses in protecting Internet of Things systems against security attacks include increasing computational power and storage space, using more secure protocols, employing advanced defense techniques, and developing artificial intelligence and deep learning methods.
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