Open AccessA Survey of Intrusion Detection Using Deep Learning in Internet of Things
Author(s) -
Baraa I. Farhan,
Ammar D. Jasim
Publication year - 2022
Publication title -
iraqi journal for computer science and mathematics
Language(s) - English
Resource type - Journals
eISSN - 2958-0544
pISSN - 2788-7421
DOI - 10.52866/ijcsm.2022.01.01.009
Subject(s) - intrusion detection system , computer science , internet of things , the internet , deep learning , intrusion , computer security , artificial intelligence , data mining , machine learning , world wide web , geochemistry , geology
The use of deep learning in various models is a powerful tool in detecting IoT attacks, identifying new types of intrusion to access a better secure network. Need to developing an intrusion detection system to detect and classify attacks in appropriate time and automated manner increases especially due to the use of IoT and the nature of its data that causes increasing in attacks. Malicious attacks are continuously changing, that cause new attacks. In this paper we present a survey about the detection of anomalies, thus intrusion detection by distinguishing between normal behavior and malicious behavior while analyzing network traffic to discover new attacks. This paper surveys previous researches by evaluating their performance through two categories of new datasets of real traffic are (CSE-CIC-IDS2018 dataset, Bot-IoT dataset). To evaluate the performance we show accuracy measurement for detect intrusion in different systems.