Mobile Edge Computing-Based Data-Driven Deep Learning Framework for Anomaly Detection
5G is anticipated to embed an artificial intelligence (AI)-empowerment to adroitly plan, optimize and manage the highly complex network by leveraging data generated at different positions of the network architecture. Outages and situation leading to congestion in a cell pose severe hazard for the ne...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8844663/ |
| _version_ | 1818411072782073856 |
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| author | Bilal Hussain Qinghe Du Sihai Zhang Ali Imran Muhammad Ali Imran |
| author_facet | Bilal Hussain Qinghe Du Sihai Zhang Ali Imran Muhammad Ali Imran |
| author_sort | Bilal Hussain |
| collection | DOAJ |
| description | 5G is anticipated to embed an artificial intelligence (AI)-empowerment to adroitly plan, optimize and manage the highly complex network by leveraging data generated at different positions of the network architecture. Outages and situation leading to congestion in a cell pose severe hazard for the network. High false alarms and inadequate accuracy are the major limitations of modern approaches for the anomaly-outage and sudden hype in traffic activity that may result in congestion-detection in mobile cellular networks. This indicates wasting limited resources that ultimately leads to an elevated operational expenditure (OPEX) and also interrupting quality of service (QoS) and quality of experience (QoE). Motivated by the outstanding success of deep learning (DL) technology, our study applies it for detection of the above-mentioned anomalies and also supports mobile edge computing (MEC) paradigm in which core network (CN)'s computations are divided across the cellular infrastructure among different MEC servers (co-located with base stations), to relief the CN. Each server monitors user activities of multiple cells and utilizes L -layer feedforward deep neural network (DNN) fueled by real call detail record (CDR) dataset for anomaly detection. Our framework achieved 98.8% accuracy with 0.44% false positive rate (FPR)-notable improvements that surmount the deficiencies of the old studies. The numerical results explicate the usefulness and dominance of our proposed detector. |
| first_indexed | 2024-12-14T10:25:36Z |
| format | Article |
| id | doaj.art-b1ffbee7dad144fa8ea76eb0f73b6dc1 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T10:25:36Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b1ffbee7dad144fa8ea76eb0f73b6dc12022-12-21T23:06:20ZengIEEEIEEE Access2169-35362019-01-01713765613766710.1109/ACCESS.2019.29424858844663Mobile Edge Computing-Based Data-Driven Deep Learning Framework for Anomaly DetectionBilal Hussain0https://orcid.org/0000-0002-0046-9007Qinghe Du1Sihai Zhang2https://orcid.org/0000-0001-5758-2169Ali Imran3Muhammad Ali Imran4https://orcid.org/0000-0002-7097-9969Shaanxi Smart Networks and Ubiquitous Access Research Center, School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an, ChinaShaanxi Smart Networks and Ubiquitous Access Research Center, School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an, ChinaKey Laboratory of Wireless-Optical Communications, Chinese Academy of Sciences, University of Science and Technology of China, Hefei, ChinaSchool of Electrical and Computer Engineering, University of Oklahoma, Tulsa, USASchool of Engineering, University of Glasgow, Glasgow, U.K5G is anticipated to embed an artificial intelligence (AI)-empowerment to adroitly plan, optimize and manage the highly complex network by leveraging data generated at different positions of the network architecture. Outages and situation leading to congestion in a cell pose severe hazard for the network. High false alarms and inadequate accuracy are the major limitations of modern approaches for the anomaly-outage and sudden hype in traffic activity that may result in congestion-detection in mobile cellular networks. This indicates wasting limited resources that ultimately leads to an elevated operational expenditure (OPEX) and also interrupting quality of service (QoS) and quality of experience (QoE). Motivated by the outstanding success of deep learning (DL) technology, our study applies it for detection of the above-mentioned anomalies and also supports mobile edge computing (MEC) paradigm in which core network (CN)'s computations are divided across the cellular infrastructure among different MEC servers (co-located with base stations), to relief the CN. Each server monitors user activities of multiple cells and utilizes L -layer feedforward deep neural network (DNN) fueled by real call detail record (CDR) dataset for anomaly detection. Our framework achieved 98.8% accuracy with 0.44% false positive rate (FPR)-notable improvements that surmount the deficiencies of the old studies. The numerical results explicate the usefulness and dominance of our proposed detector.https://ieeexplore.ieee.org/document/8844663/Cellular networkanomaly detectioncall detail recorddeep learningbig data analyticssleeping cell |
| spellingShingle | Bilal Hussain Qinghe Du Sihai Zhang Ali Imran Muhammad Ali Imran Mobile Edge Computing-Based Data-Driven Deep Learning Framework for Anomaly Detection IEEE Access Cellular network anomaly detection call detail record deep learning big data analytics sleeping cell |
| title | Mobile Edge Computing-Based Data-Driven Deep Learning Framework for Anomaly Detection |
| title_full | Mobile Edge Computing-Based Data-Driven Deep Learning Framework for Anomaly Detection |
| title_fullStr | Mobile Edge Computing-Based Data-Driven Deep Learning Framework for Anomaly Detection |
| title_full_unstemmed | Mobile Edge Computing-Based Data-Driven Deep Learning Framework for Anomaly Detection |
| title_short | Mobile Edge Computing-Based Data-Driven Deep Learning Framework for Anomaly Detection |
| title_sort | mobile edge computing based data driven deep learning framework for anomaly detection |
| topic | Cellular network anomaly detection call detail record deep learning big data analytics sleeping cell |
| url | https://ieeexplore.ieee.org/document/8844663/ |
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