عنوان

Intrusion Detection in IoT Systems Using Machine Learning Algorithms

شماره

TL52934

زبان متن نوشتاري يا گفتاري و مانند آن

انگلیسی

عنوان اصلي

Intrusion Detection in IoT Systems Using Machine Learning Algorithms

نام عام مواد

[Thesis]

نام نخستين پديدآور

Basalan, Abdurrahman

نام ساير پديدآوران

Salam, Mohammad Abdus

نام ناشر، پخش کننده و غيره

Southern University and Agricultural and Mechanical College

تاریخ نشرو بخش و غیره

2020

متن يادداشت

66 p.

جزئيات پايان نامه و نوع درجه آن

M.S.

کسي که مدرک را اعطا کرده

Southern University and Agricultural and Mechanical College

امتياز متن

2020

متن يادداشت

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.

اصطلاح موضوعی

Artificial intelligence

اصطلاح موضوعی

Computer science

مستند نام اشخاص تاييد نشده

Basalan, Abdurrahman

مستند نام اشخاص تاييد نشده

Salam, Mohammad Abdus

مستند نام تنالگان تاييد نشده

Southern University and Agricultural and Mechanical College

نام الکترونيکي

فرمت انتشار

p

نوع ماده

[Thesis]

کد کاربرگه

276903

سطح دسترسي

a

تكميل شده

Y