Mobile edge computing-based data-driven deep learning framework for anomaly detection

Hussain, B., Du, Q., Zhang, S., Imran, A. and Imran, M. A. (2019) Mobile edge computing-based data-driven deep learning framework for anomaly detection. IEEE Access, 7, pp. 137656-137667. (doi: 10.1109/ACCESS.2019.2942485)

Abstract

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.

• Item Type: Articles
• Additional Information: This work was supported in part by the National Natural Science Foundation of China under Grant No. 61671371, and in part by the Fundamental Research Funds for the Central Universities.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Imran, Professor Muhammad
• Authors: Hussain, B., Du, Q., Zhang, S., Imran, A., and Imran, M. A.
• College/School: College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: IEEE Access
• Publisher: IEEE
• ISSN: 2169-3536
• ISSN (Online): 2169-3536
• Copyright Holders: Copyright © 2019 IEEE
• First Published: First published in IEEE Access 7:137656-137667
• Publisher Policy: Reproduced under a Creative Commons License