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.

[img] Text
05111740000135-Undergraduate_Thesis.pdf - Accepted Version
Restricted to Repository staff only until 1 October 2023.

Download (1MB) | Request a copy

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. =================================================================================================== 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.
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: https://repository.its.ac.id/id/eprint/84860

Actions (login required)

View Item View Item