Context-aware, Composable Anomaly Detection in Large-scale Mobile Networks

Nguyen Ngoc Nhu Trang, Linh Truong

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

199 Lataukset (Pure)

Abstrakti

In a large-scale mobile network, due to the diversity of data characteristics, detection purposes of operation teams, and analytics and machine learning algorithm abilities, building big data anomaly detection pipelines without considering different analytics and team situations may not yield expected quality of analytics, including detection relevancy, performance and quality. This is especially for analytics subjects, such as mobile network zones, of which characteristics are dynamic and contextual. Moreover, due to the lack of labeled data and the high cost of creating labeled data, building anomaly detection analytics models based on (supervised) deep learning or advanced models is even more challenging from various aspects of effort, cost and deployment. In this paper, we present a novel framework that enables anomaly detection through context-aware, composable components to provide efficient detection pipelines suitable for lightweight, resource constrained and geographical operation teams. First, we identify and categorize different types of analytics feature contexts and evaluate existing algorithms suitable for these contexts, mapping anomaly detection algorithms, patterns and configurations for data pre-processing and unsupervised detection tasks in individual analytics functionality. These context-specific pipelines detect anomalies and their relevancy for dynamic analytics subjects such as mobile network zones. Then we develop dynamic configuration and combination techniques for such pipelines to produce highly relevant, multi-context detection of anomalies. Our framework provides flexibility and configurations for team contexts to carry out the anomaly detection in the team’s operations. We will demonstrate our work through real data gathered for a large-scale mobile network covering multiple types of sites with different geographical zones and equipment. We especially focus on district zones and user-defined zones as analytics subjects that must be managed by teams in our experiments.
AlkuperäiskieliEnglanti
OtsikkoProceedings - 2023 IEEE 47th Annual Computers, Software, and Applications Conference, COMPSAC 2023
ToimittajatHossain Shahriar, Yuuichi Teranishi, Alfredo Cuzzocrea, Moushumi Sharmin, Dave Towey, AKM Jahangir Alam Majumder, Hiroki Kashiwazaki, Ji-Jiang Yang, Michiharu Takemoto, Nazmus Sakib, Ryohei Banno, Sheikh Iqbal Ahamed
KustantajaIEEE
Sivut183-192
Sivumäärä10
ISBN (elektroninen)979-8-3503-2697-0
DOI - pysyväislinkit
TilaJulkaistu - 2023
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE Annual Computer Software and Applications Conference - Torino, Italia
Kesto: 26 kesäk. 202330 kesäk. 2023
Konferenssinumero: 47

Julkaisusarja

Nimi Proceedings : International Computer Software & Applications Conference
ISSN (painettu)0730-3157

Conference

ConferenceIEEE Annual Computer Software and Applications Conference
LyhennettäCOMPSAC
Maa/AlueItalia
KaupunkiTorino
Ajanjakso26/06/202330/06/2023

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