Intrusion Detection in IoT Systems Using Machine Learning Algorithms

مرکز و کتابخانه مطالعات اسلامی به زبان های اروپایی

منو

" Intrusion Detection in IoT Systems Using Machine Learning Algorithms "
Basalan, Abdurrahman Salam, Mohammad Abdus

Document Type	:	Latin Dissertation
Language of Document	:	English
Record Number	:	1106963
Doc. No	:	TLpq2423767658
Main Entry	:	Basalan, Abdurrahman
	:	Salam, Mohammad Abdus
Title & Author	:	Intrusion Detection in IoT Systems Using Machine Learning Algorithms\ Basalan, AbdurrahmanSalam, Mohammad Abdus
College	:	Southern University and Agricultural and Mechanical College
Date	:	2020
student score	:	2020
Degree	:	M.S.
Page No	:	66
Abstract	:	In recent years, Internet of Things (IoT) has grown up rapidly and tremendously. This growth has brought big and special problems. Two of the urgent topics of problems are security and privacy for IoT devices. Those devices are creating and gathering all data in their connections. For the security of IoT, detection of anomaly attacks is the first and crucial point for avoiding any interruption in the connection. Machine Learning algorithms have been rising and improving substantially year by year. Many classic tests can detect large amount of attacks in current time. However, those techniques are not enough for security since the types of attacks are changing and getting stronger frequently. In this study, we propose that how we can improve security level of IoT. Also, deep learning techniques, especially Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) have been applied in order to have better accuracy performance. Dataset is presumably one of the most important starting point for the use of those techniques. UNSW-NB15 dataset which is publicly available has been used for this study. Two different models are created in this study. One detects attack or no attack and another detects what type of attack or no attack. The combinations of LSTM and CNN algorithms have 98.2% accuracy which is best performance within the selected algorithms.
Subject	:	Artificial intelligence
	:	Computer science