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Efficient anomaly detection using deer hunting optimization algorithm via adaptive deep belief neural network in mobile network

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Abstract

Mobile network owns the network and user data. Such data helps provide the end-to-end visible and network intelligence. The user and the network data can be analyzed effectively using big data analytics. In modern approaches, major drawbacks are inadequate accuracy and a high false alarm rate. To overcome these limitations, a deer hunting optimization algorithm (DHOA) with an adaptive deep belief network (ADBN) is proposed in this paper for anomaly detection from the call detail records (CDR) data. Pre-processing is the initial step, in which the Kalman filter is used to pre-process the CDR data for eliminating the noise and the unwanted fields from the raw input data. Then, the pre-processed data is clustered using DHOA based on the activity level of CDR data. After clustering, data classification is performed to identify the normal and anomaly data. Moreover, the identified anomaly data are removed using this ADBN model. The dataset named CDR is utilized in this approach for the performance evaluation, and MATLAB is the implementation tool used to experiment with the proposed methodology. Precision, error rate, F1, recall, false-positive rate (FPR) and accuracy are evaluated to show the efficiency of the proposed method. From the experimental results, the proposed approach achieved high accuracy and low FPR. Therefore, the effectiveness of the proposed approach achieved precision by 99.6%, recall by 99.2%, F1 score by 99.3% and accuracy of the proposed method are 99%, respectively. The error rate attained by the proposed methodology is 0.99, and the FPR obtained is 0.25, which are very low compared to the existing methods.

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Data availability statement

Data sharing does not apply to this article as no datasets were generated or analyzed during the current study.

References

  • Aliakbarisani R, Ghasemi A, Wu SF (2019) A data-driven metric learning-based scheme for unsupervised network anomaly detection. Comput Electr Eng 73:71–83

    Article  Google Scholar 

  • Brammya G, Praveena S, Preetha NSN, Ramya R, Rajakumar BR, Binu D (2019) Deer hunting optimization algorithm: a new nature-inspired meta-heuristic paradigm. Comput J. https://doi.org/10.1093/comjnl/bxy133

    Article  Google Scholar 

  • Chen C, Ma Y, Ren G (2020) Hyperspectral classification using deep belief networks based on conjugate gradient update and pixel-centric spectral block features. IEEE J Sel Top Appl Earth Observ Remote Sens 13:4060–4069

    Article  Google Scholar 

  • Chiba Z, Abghour N, Moussaid K, El Omri A, Rida M (2018) A novel architecture combined with optimal parameters for back propagation neural networks applied to anomaly network intrusion detection. Comput Secur 75:36–58

    Article  Google Scholar 

  • Chin K, Huang H, Horn C, Kasanicky I, Weibel R (2019) Inferring fine-grained transport modes from mobile phone cellular signaling data. Comput Environ Urban Syst 77:101348

    Article  Google Scholar 

  • Ding N, Ma H, Zhao C, Ma Y, Ge H (2019) Data anomaly detection for Internet of vehicles based on traffic cellular automata and driving style. Sensors 19(22):4926

    Article  Google Scholar 

  • Dou S, Yang K, Luo P, Jiao Y (2022) Unsupervised anomaly detection in heterogeneous network time series with mixed sampling rates

  • Dridi A, Boucetta C, Hammami SE, Afifi H, Moungla H (2020) STAD: spatio-temporal anomaly detection mechanism for mobile network management. IEEE Trans Netw Serv Manag 18(1):894–906

    Article  Google Scholar 

  • Erhan L, Ndubuaku M, Di Mauro M, Song W, Chen M, Fortino G, Bagdasar O, Liotta A (2021) Smart anomaly detection in sensor systems: a multi-perspective review. Inf Fusion 67:64–79

    Article  Google Scholar 

  • Gunavathi C, Priya RS, Aarthy SL (2019) Big data analysis for anomaly detection in telecommunication using clustering techniques. Information systems design and intelligent applications. Springer, Singapore, pp 111–121

    Chapter  Google Scholar 

  • Hajisalem V, Babaie S (2018) A hybrid intrusion detection system based on ABC-AFS algorithm for misuse and anomaly detection. Comput Netw 136:37–50

    Article  Google Scholar 

  • Hamamoto AH, Carvalho LF, Sampaio LDH, Abrão T, Proença ML (2018) Network anomaly detection system using genetic algorithm and fuzzy logic. Expert Syst Appl 92:390–402

    Article  Google Scholar 

  • Hussain B, Du Q, Ren P (2018) Semi-supervised learning based big data-driven anomaly detection in mobile wireless networks. China Commun 15(4):41–57

    Article  Google Scholar 

  • Hussain B, Du Q, Imran A, Imran MA (2019a) Artificial intelligence-powered mobile edge computing-based anomaly detection in cellular networks. IEEE Trans Ind Inf 16(8):4986–4996

    Article  Google Scholar 

  • Hussain B, Du Q, Zhang S, Imran A, Imran MA (2019b) Mobile edge computing-based data-driven deep learning framework for anomaly detection. IEEE Access 7:137656–137667

    Article  Google Scholar 

  • Hwang RH, Peng MC, Huang CW, Lin PC, Nguyen VL (2020) An unsupervised deep learning model for early network traffic anomaly detection. IEEE Access 8:30387–30399

    Article  Google Scholar 

  • Iglesias JA, Ledezma A, Sanchis A, Angelov P (2017) Real-time recognition of calling pattern and behaviour of mobile phone users through anomaly detection and dynamically-evolving clustering. Appl Sci 7(8):798

    Article  Google Scholar 

  • Jaffry S, Shah ST, Hasan SF (2020) Data-driven semi-supervised anomaly detection using real-world call data record. In: 2020 IEEE wireless communications and networking conference workshops (WCNCW), IEEE, pp 1–6

  • Kim TY, Cho SB (2018) Web traffic anomaly detection using C-LSTM neural networks. Expert Syst Appl 106:66–76

    Article  Google Scholar 

  • Kurt MN, Yilmaz Y, Wang X (2020) Real-time nonparametric anomaly detection in high-dimensional settings. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TPAMI.2020.2970410

    Article  Google Scholar 

  • Li B, Zhao S, Zhang R, Shi Q, Yang K (2019) Anomaly detection for cellular networks using big data analytics. IET Commun 13(20):3351–3359

    Article  Google Scholar 

  • Maimó LF, Gómez ÁLP, Clemente FJG, Pérez MG, Pérez GM (2018) A self-adaptive deep learning-based system for anomaly detection in 5G networks. IEEE Access 6:7700–7712

    Article  Google Scholar 

  • Makkar G, Jayaraman M, Sharma S (2019) Network intrusion detection in an enterprise: unsupervised analytical methodology. Data management, analytics and innovation. Springer, Singapore, pp 451–463

    Chapter  Google Scholar 

  • Naseer S, Saleem Y, Khalid S, Bashir MK, Han J, Iqbal MM, Han K (2018) Enhanced network anomaly detection based on deep neural networks. IEEE Access 6:48231–48246

    Article  Google Scholar 

  • Pan L, Zhang J, Lee PP, Kalander M, Ye J, Wang P (2020) Proactive microwave link anomaly detection in cellular data networks. Comput Netw 167:106969

    Article  Google Scholar 

  • Parwez MS, Rawat DB, Garuba M (2017) Big data analytics for user-activity analysis and user-anomaly detection in mobile wireless network. IEEE Trans Ind Inf 13(4):2058–2065

    Article  Google Scholar 

  • Prabhakar TS, Veena MN (2020) Review on anomaly detection in mobile networks using traditional learning, machine learning and deep learning. J Comput Theor Nanosci 17(11):4789–4796

    Article  Google Scholar 

  • Rettig L, Khayati M, Cudré-Mauroux P, Piórkowski M (2019) Online anomaly detection over big data streams. Applied data science. Springer, Cham, pp 289–312

    Chapter  Google Scholar 

  • Sabokrou M, Fayyaz M, Fathy M, Moayed Z, Klette R (2018) Deep-anomaly: fully convolutional neural network for fast anomaly detection in crowded scenes. Comput vis Image Underst 172:88–97

    Article  Google Scholar 

  • Sultan K, Ali H, Zhang Z (2018) Call detail records driven anomaly detection and traffic prediction in mobile cellular networks. IEEE Access 6:41728–41737

    Article  Google Scholar 

  • Sultan K, Ali H, Ahmad A, Zhang Z (2019) Call details record analysis: a spatiotemporal exploration toward mobile traffic classification and optimization. Information 10(6):192

    Article  Google Scholar 

  • Ting JA, Theodorou E, Schaal S (2007) A Kalman filter for robust outlier detection. In: 2007 IEEE/RSJ international conference on intelligent robots and systems, pp 1514–1519

  • Xu S, Qian Y, Hu RQ (2020) Data-driven edge intelligence for robust network anomaly detection. IEEE Trans Netw Sci Eng 7(3):1481–1492. https://doi.org/10.1109/TNSE.2019.2936466

    Article  Google Scholar 

  • Zhu Q, Sun L (2020) Big data driven anomaly detection for cellular networks. IEEE Access 8:31398–31408. https://doi.org/10.1109/ACCESS.2020.2973214

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Information Science and Engineering, P.E.S. College of Engineering, Mandya, Karnataka, 571401, India

    T. S. Prabhakar

  2. MCA Department, P.E.S. College of Engineering, Mandya, Karnataka, 571401, India

    M. N. Veena

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  1. T. S. Prabhakar
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  2. M. N. Veena
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Correspondence to T. S. Prabhakar.

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Prabhakar, T.S., Veena, M.N. Efficient anomaly detection using deer hunting optimization algorithm via adaptive deep belief neural network in mobile network. J Ambient Intell Human Comput 14, 16409–16425 (2023). https://doi.org/10.1007/s12652-022-03861-6

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  • DOI: https://doi.org/10.1007/s12652-022-03861-6

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