Network traffic anomaly prediction using Artificial Neural Network

Hening Titi Ciptaningtyas*, Chastine Fatichah, Altea Sabila

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

As the excessive increase of internet usage, the malicious software (malware) has also increase significantly. Malware is software developed by hacker for illegal purpose(s), such as stealing data and identity, causing computer damage, or denying service to other user[1]. Malware which attack computer or server often triggers network traffic anomaly phenomena. Based on Sophos's report[2], Indonesia is the riskiest country of malware attack and it also has high network traffic anomaly. This research uses Artificial Neural Network (ANN) to predict network traffic anomaly based on malware attack in Indonesia which is recorded by Id-SIRTII/CC (Indonesia Security Incident Response Team on Internet Infrastructure/Coordination Center). The case study is the highest malware attack (SQL injection) which has happened in three consecutive years: 2012, 2013, and 2014[4]. The data series is preprocessed first, then the network traffic anomaly is predicted using Artificial Neural Network and using two weight update algorithms: Gradient Descent and Momentum. Error of prediction is calculated using Mean Squared Error (MSE) [7]. The experimental result shows that MSE for SQL Injection is 0.03856. So, this approach can be used to predict network traffic anomaly.

Original languageEnglish
Title of host publicationEngineering International Conference, EIC 2016
Subtitle of host publicationProceedings of the 5th International Conference on Education, Concept, and Application of Green Technology
Editors Subiyanto, Rini Kusumawardani, Adhi Kusumastuti, Megawati, Dwi Widjanarko
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735414860
DOIs
Publication statusPublished - 9 Mar 2017
Event5th Engineering International Conference on Education, Concept, and Application of Green Technology, EIC 2016 - Semarang, Indonesia
Duration: 5 Oct 20166 Oct 2016

Publication series

NameAIP Conference Proceedings
Volume1818
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference5th Engineering International Conference on Education, Concept, and Application of Green Technology, EIC 2016
Country/TerritoryIndonesia
CitySemarang
Period5/10/166/10/16

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