TY - JOUR
T1 - Performance Evaluation of Anomaly Detection System on Portable LTE Telecommunication Networks Using OpenAirInterface and ELK
AU - Nugroho, Yeremia Nikanor
AU - Harwahyu, Ruki
AU - Sari, Riri Fitri
AU - Nikaein, Navid
AU - Cheng, Ray Guang
N1 - Funding Information:
We thank the Ministry of Education and Culture of the Republic of Indonesia for financial support for this research under the PDUPT Research Grant number NKB-255/UN2.RST/HKP.05.00/2020.
Publisher Copyright:
© 2023, International Journal of Technolog. All Rights Reserved.
PY - 2023
Y1 - 2023
N2 - Anomaly detection (AD) is a solution for cellular operators to overcome the difficulty of quality control over the proliferation of cellular phone usage. The telecommunications network monitoring system with anomaly detection enables immediate discovery of problems before they become more complex. Monitoring activities produce logs, which are then analyzed according to the interest, often with the help of statistics and visualizations. Relying on humans for analysis is increasingly difficult due to the immense amount of logs in modern telecommunication networks. This work extends the monitoring to automated anomaly detection by using various ELK modules to form an intelligent monitoring system. A testbed based on OpenAirInterface (OAI) and USRP B210 radiohead is used, which includes the functionalities of HSS, MME, SGW, PGW, eNB, and UE. The proposed system has an average accuracy of 91.5%. This is supported by an average value of the proportion of normal conditions that are correctly predicted at 99.31%. On the other hand, the system can still maintain the functionality of the cellular telecommunications network with an excellent predicate on service quality.
AB - Anomaly detection (AD) is a solution for cellular operators to overcome the difficulty of quality control over the proliferation of cellular phone usage. The telecommunications network monitoring system with anomaly detection enables immediate discovery of problems before they become more complex. Monitoring activities produce logs, which are then analyzed according to the interest, often with the help of statistics and visualizations. Relying on humans for analysis is increasingly difficult due to the immense amount of logs in modern telecommunication networks. This work extends the monitoring to automated anomaly detection by using various ELK modules to form an intelligent monitoring system. A testbed based on OpenAirInterface (OAI) and USRP B210 radiohead is used, which includes the functionalities of HSS, MME, SGW, PGW, eNB, and UE. The proposed system has an average accuracy of 91.5%. This is supported by an average value of the proportion of normal conditions that are correctly predicted at 99.31%. On the other hand, the system can still maintain the functionality of the cellular telecommunications network with an excellent predicate on service quality.
KW - Anomaly detection
KW - Elasticsearch-Logstash-Kibana stack
KW - Long-Term Evolution
KW - OpenAirInterface
KW - Telecommunication network
UR - http://www.scopus.com/inward/record.url?scp=85159212716&partnerID=8YFLogxK
U2 - 10.14716/ijtech.v14i3.4237
DO - 10.14716/ijtech.v14i3.4237
M3 - Article
AN - SCOPUS:85159212716
SN - 2086-9614
VL - 14
SP - 549
EP - 560
JO - International Journal of Technology
JF - International Journal of Technology
IS - 3
ER -