Analisis Performa Kompresi Autoencoder pada Intrusion Detection System

Pamungkas, I Gede Agung Krisna (2021) Analisis Performa Kompresi Autoencoder pada Intrusion Detection System. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Banyak sekali ancaman yang didapatkan bila kita
menggunakan teknologi jaringan dikarenakan mudahnya kita
mengakses berbagai informasi disana. Salah satu cara untuk
meminimalisir hal tersebut adalah dengan menggunakan
teknologi Intrusion Detection System (IDS).
Banyak sekali Teknik yang digunakan untuk membangun
Intrusion Detection System salah satunya dengan memanfaatkan
machine learning untuk mendeteksi apakah paket yang diterima
termasuk serangan atau tidak. Disini penulis membangun
Intrusion Detection System dengan menggunakan Autoencoder
sebagai kompresi data. Dengan Autoencoder ini diharapkan bisa
mendapatkan performa yang terbaik karena data tersebut
terkompresi sehingga ukurannya menjadi lebih kecil.
Dengan adanya teknik Autoencoder ini diharapkan bisa
mendapatkan performa yang lebih baik dalam mendeteksi
serangan dengan menggunakan data dari hasil Autoencoder
tersebut.
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There are so many threats that we get when we use network
technology because it is easy for us to access various information
there. One way to minimize this is to use Intrusion Detection
System (IDS) technology.
There are so many techniques used to build an Intrusion
Detection System, one of which is by utilizing machine learning to
detect whether the received packet is an attack or not. Here the
author builds an Intrusion Detection System using an
Autoencoder as data compression. With this autoencoder, it is
expected to get the best performance because the data is
compressed so that the size becomes smaller.
With the Autoencoder technique, it is expected to get better
performance in detecting attacks using data from the
Autoencoder results.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Intrusion Detection System, Autoencoder, Deep Learning, Machine Learning Intrusion Detection System, Autoencoder, Deep Learning, Machine Learning
Subjects:	Q Science > Q Science (General) > Q325.5 Machine learning. Support vector machines. Q Science > Q Science (General) > Q337.5 Pattern recognition systems T Technology > T Technology (General) > T57.5 Data Processing T Technology > T Technology (General) > T58.5 Information technology. IT--Auditing T Technology > TA Engineering (General). Civil engineering (General) > TA158.7 Computer network resources
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Informatics Engineering > 55201-(S1) Undergraduate Thesis
Depositing User:	I Gede Agung Krisna Pamungkas
Date Deposited:	04 Aug 2021 23:28
Last Modified:	04 Aug 2021 23:28
URI:	http://repository.its.ac.id/id/eprint/84860

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