Insight into Anomaly Detection and Prediction and Mobile Network Security Enhancement Leveraging K-Means Clustering on Call Detail Records

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00375038" target="_blank" >RIV/68407700:21230/24:00375038 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.3390/s24061716" target="_blank" >https://doi.org/10.3390/s24061716</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/s24061716" target="_blank" >10.3390/s24061716</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Insight into Anomaly Detection and Prediction and Mobile Network Security Enhancement Leveraging K-Means Clustering on Call Detail Records

Popis výsledku v původním jazyce

The dynamic and evolving nature of mobile networks necessitates a proactive approach to security, one that goes beyond traditional methods and embraces innovative strategies such as anomaly detection and prediction. This study delves into the realm of mobile network security and reliability enhancement through the lens of anomaly detection and prediction, leveraging K-means clustering on call detail records (CDRs). By analyzing CDRs, which encapsulate comprehensive information about call activities, messaging, and data usage, this research aimed to unveil hidden patterns indicative of anomalous behavior within mobile networks and security breaches. We utilized 14 million one-year CDR records. The mobile network used had deployed the latest network generation, 5G, with various sources of network elements. Through a systematic analysis of historical CDR data, this study offers insights into the underlying trends and anomalies prevalent in mobile network traffic. Furthermore, by harnessing the predictive capabilities of the K-means algorithm, the proposed framework facilitates the anticipation of future anomalies based on learned patterns, thereby enhancing proactive security measures. The findings of this research can contribute to the advancement of mobile network security by providing a deeper understanding of anomalous behavior and effective prediction mechanisms. The utilization of K-means clustering on CDR data offers a scalable and efficient approach to anomaly detection, with 96% accuracy, making it well suited for network reliability and security applications in large-scale mobile networks for 5G networks and beyond.

Název v anglickém jazyce

Insight into Anomaly Detection and Prediction and Mobile Network Security Enhancement Leveraging K-Means Clustering on Call Detail Records

Popis výsledku anglicky

The dynamic and evolving nature of mobile networks necessitates a proactive approach to security, one that goes beyond traditional methods and embraces innovative strategies such as anomaly detection and prediction. This study delves into the realm of mobile network security and reliability enhancement through the lens of anomaly detection and prediction, leveraging K-means clustering on call detail records (CDRs). By analyzing CDRs, which encapsulate comprehensive information about call activities, messaging, and data usage, this research aimed to unveil hidden patterns indicative of anomalous behavior within mobile networks and security breaches. We utilized 14 million one-year CDR records. The mobile network used had deployed the latest network generation, 5G, with various sources of network elements. Through a systematic analysis of historical CDR data, this study offers insights into the underlying trends and anomalies prevalent in mobile network traffic. Furthermore, by harnessing the predictive capabilities of the K-means algorithm, the proposed framework facilitates the anticipation of future anomalies based on learned patterns, thereby enhancing proactive security measures. The findings of this research can contribute to the advancement of mobile network security by providing a deeper understanding of anomalous behavior and effective prediction mechanisms. The utilization of K-means clustering on CDR data offers a scalable and efficient approach to anomaly detection, with 96% accuracy, making it well suited for network reliability and security applications in large-scale mobile networks for 5G networks and beyond.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Sensors

ISSN

1424-8220

e-ISSN

1424-8220

Svazek periodika

24

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

18

Strana od-do

1-18

Kód UT WoS článku

001193064400001

EID výsledku v databázi Scopus

2-s2.0-85188945411