A Human-in-the-Loop Anomaly Detection Architecture for Big Traffic Data of Cellular Network
In the era of mobile big data, smart mobile devices have become an integral part of our daily life, which brings many benefits to the digital society. However, their popularity and relatively lax security make them vulnerable to various cyber threats. Traditional network traffic analysis techniques...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10471346/ |
| _version_ | 1797243425237499904 |
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| author | Shenglong Liu Yuxiao Xia Di Wang |
| author_facet | Shenglong Liu Yuxiao Xia Di Wang |
| author_sort | Shenglong Liu |
| collection | DOAJ |
| description | In the era of mobile big data, smart mobile devices have become an integral part of our daily life, which brings many benefits to the digital society. However, their popularity and relatively lax security make them vulnerable to various cyber threats. Traditional network traffic analysis techniques utilizing pattern matching and regular expressions matching algorithms are becoming insufficient for mobile big data. Network traffic anomaly detection is an effective method to replace traditional methods. Network traffic anomaly detection can solve many new challenges brought by future network and protect the security of network. In this article, we propose a streaming network framework for mobile big data, referred to as SNMDF, which provides massive data traffic collection, processing, analysis, and updating functions, to cope with the tremendous amount of data traffic. In particular, by analyzing the specific characteristics of anomaly traffic data from flow and user behavior, our proposed SNMDF demonstrates its capability to offer real data-based advice to address new challenges for future wireless networks from the viewpoints of operators. Tested by real mobile big data, SNMDF has proven its efficiency and reliability. Furthermore, SNMDF is accessed for the digital twin of the space Internet, which validates that it can be generalized to other environments with massive data traffic or big data. |
| first_indexed | 2024-04-24T18:54:54Z |
| format | Article |
| id | doaj.art-4148aa4192ad425187e11d6862bd94ef |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T18:54:54Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4148aa4192ad425187e11d6862bd94ef2024-03-26T17:44:25ZengIEEEIEEE Access2169-35362024-01-0112417874179710.1109/ACCESS.2024.337641310471346A Human-in-the-Loop Anomaly Detection Architecture for Big Traffic Data of Cellular NetworkShenglong Liu0Yuxiao Xia1Di Wang2https://orcid.org/0009-0000-3250-8309Big Data Center, State Grid Corporation of China, Beijing, ChinaBig Data Center, State Grid Corporation of China, Beijing, ChinaBig Data Center, State Grid Corporation of China, Beijing, ChinaIn the era of mobile big data, smart mobile devices have become an integral part of our daily life, which brings many benefits to the digital society. However, their popularity and relatively lax security make them vulnerable to various cyber threats. Traditional network traffic analysis techniques utilizing pattern matching and regular expressions matching algorithms are becoming insufficient for mobile big data. Network traffic anomaly detection is an effective method to replace traditional methods. Network traffic anomaly detection can solve many new challenges brought by future network and protect the security of network. In this article, we propose a streaming network framework for mobile big data, referred to as SNMDF, which provides massive data traffic collection, processing, analysis, and updating functions, to cope with the tremendous amount of data traffic. In particular, by analyzing the specific characteristics of anomaly traffic data from flow and user behavior, our proposed SNMDF demonstrates its capability to offer real data-based advice to address new challenges for future wireless networks from the viewpoints of operators. Tested by real mobile big data, SNMDF has proven its efficiency and reliability. Furthermore, SNMDF is accessed for the digital twin of the space Internet, which validates that it can be generalized to other environments with massive data traffic or big data.https://ieeexplore.ieee.org/document/10471346/Cyber threatsnetwork trafficnetwork securitybig dataSNMDF |
| spellingShingle | Shenglong Liu Yuxiao Xia Di Wang A Human-in-the-Loop Anomaly Detection Architecture for Big Traffic Data of Cellular Network IEEE Access Cyber threats network traffic network security big data SNMDF |
| title | A Human-in-the-Loop Anomaly Detection Architecture for Big Traffic Data of Cellular Network |
| title_full | A Human-in-the-Loop Anomaly Detection Architecture for Big Traffic Data of Cellular Network |
| title_fullStr | A Human-in-the-Loop Anomaly Detection Architecture for Big Traffic Data of Cellular Network |
| title_full_unstemmed | A Human-in-the-Loop Anomaly Detection Architecture for Big Traffic Data of Cellular Network |
| title_short | A Human-in-the-Loop Anomaly Detection Architecture for Big Traffic Data of Cellular Network |
| title_sort | human in the loop anomaly detection architecture for big traffic data of cellular network |
| topic | Cyber threats network traffic network security big data SNMDF |
| url | https://ieeexplore.ieee.org/document/10471346/ |
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