TY - GEN
T1 - Network traffic anomaly prediction using Artificial Neural Network
AU - Ciptaningtyas, Hening Titi
AU - Fatichah, Chastine
AU - Sabila, Altea
N1 - Publisher Copyright:
© 2017 Author(s).
PY - 2017/3/9
Y1 - 2017/3/9
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85017524198&partnerID=8YFLogxK
U2 - 10.1063/1.4976874
DO - 10.1063/1.4976874
M3 - Conference contribution
AN - SCOPUS:85017524198
T3 - AIP Conference Proceedings
BT - Engineering International Conference, EIC 2016
A2 - Subiyanto, null
A2 - Kusumawardani, Rini
A2 - Kusumastuti, Adhi
A2 - Megawati, null
A2 - Widjanarko, Dwi
PB - American Institute of Physics Inc.
T2 - 5th Engineering International Conference on Education, Concept, and Application of Green Technology, EIC 2016
Y2 - 5 October 2016 through 6 October 2016
ER -