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ISSN (Print): 1872-2121
ISSN (Online): 2212-4047

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Research Article

Finding the Efficiency of ConvBi-LSTM Over Anticipation of Adversaries in WBANs

Author(s): R.N.L.S. Kalpana, Ajit Kumar Patro* and D. Nageshwar Rao

Volume 19, Issue 1, 2025

Published on: 16 October, 2023

Article ID: e161023222246 Pages: 13

DOI: 10.2174/0118722121255695231008171935

Price: $65

Abstract

Introduction: Wireless Body Area Networks (WBANs) are similar to custom Wireless Sensor Networks, so these networks are prone to adversaries through their activities, but in healthcare applications, security is necessary for the patient data. Moreover, providing reliable healthcare to patients is essential, and for the right treatment, correct patient data is required. For this purpose, we need to eliminate anomalies and irrelevant data created by malicious persons, attackers, and unauthorized users. However, existing technologies are not able to detect adversaries and are unable to maintain the data for a long duration while transferring it.

Aims: This patent research aims to identify adversarial attacks and solutions for these attacks to maintain reliable smart healthcare services.

Methodology: We proposed a Convolutional-Bi-directional Long Short-Term Memory (ConvBi- LSTM) model that provides a solution for the detection of adversaries and robustness against adversaries. Bi-LSTM (Bidirectional-Long Short Term Memory), where the hyperparameters of BiLSTM are tuned using the PHMS (Prognosis Health Monitoring System) to detect malicious or irrelevant anomalies data.

Result: Thus, the empirical outcomes of the proposed model showed that it accurately categorizes a patient's health status founded on abnormal vital signs and is useful for providing the proper medical care to the patients. Furthermore, the Convolution Neural Networks (CNN) performance is also evaluated spatially to examine the relationship between the sensor and CMS (Central Monitoring System) or doctor’s device. The accuracy, recall, precision, loss, time, and F1 score metrics are used for the performance evaluation of the proposed model.

Conclusion: Besides, the proposed model performance is compared with the existing approaches using the MIMIC (Medical Information Mart for Intensive Care) data set.

[1]
R.N.L.S. Kalpana, and D. Nageshwar Rao, A survey on deep learning techniques for anomaly detection in human activity recognition Rural and Tribal Development using Iot and Cloud Computing, advances in Sustainability Science and Technology, 2022..
[2]
A. Albattah, and M.A. Rassam, "A correlation-based anomaly detection model for wireless body area networks using convolutional long short-term memory neural network", Sensors , vol. 22, no. 5, p. 1951, 2022.
[http://dx.doi.org/10.3390/s22051951] [PMID: 35271097]
[3]
A.I. Newaz, A.K. Sikder, M.A. Rahman, and A.S. Uluagac, Healthguard: A Machine Learning-Based Security Framework for Smart Healthcare SystemsIn In: Proceedings of the 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS)Granada, Spain 22–25 October 2019. IEEE: New York, NY, USA, 2019.
[http://dx.doi.org/10.1109/SNAMS.2019.8931716]
[4]
M. Fahim, and A. Sillitti, "Anomaly detection, analysis and prediction techniques in IoT environment: A systematic literature review", IEEE Access, vol. 7, pp. 81664-81681, 2019.
[http://dx.doi.org/10.1109/ACCESS.2019.2921912]
[5]
M.S.Z. Dehabadi, and M. Jahed, "Reliability Modeling of Anomaly Detection Algorithms for Wireless Body Area Networks", In Proceedings of the 2017 Iranian Conference on Electrical Engineering (ICEE)Tehran, Iran 2–4 May 2017. IEEE: New York, NY, USA, 2017, pp. 70-75.
[http://dx.doi.org/10.1109/IranianCEE.2017.7985142]
[6]
S. Saraswathi, G.R. Suresh, and J. Katiravan, "False alarm detection using dynamic threshold in medical wireless sensor networks", Wirel. Netw., vol. 27, no. 2, pp. 925-937, 2021.
[http://dx.doi.org/10.1007/s11276-019-02197-y]
[7]
O. Salem, A. Serhrouchni, A. Mehaoua, and R. Boutaba, "Event detection in wireless body area networks using Kalman filter and power divergence", IEEE Trans. Netw. Serv. Manag., vol. 15, no. 3, pp. 1018-1034, 2018.
[http://dx.doi.org/10.1109/TNSM.2018.2842195]
[8]
B. Saneja, and R. Rani, "An integrated framework for anomaly detection in big data of medical wireless sensors", Modern Physics., vol. 32, .1850283 2018
[9]
G. Pachauri, and S. Sharma, "Anomaly detection in medical wireless sensor networks using machine learning algorithms", Procedia Comput. Sci., vol. 70, pp. 325-333, 2015.
[http://dx.doi.org/10.1016/j.procs.2015.10.026]
[10]
F.A. Khan, N.A.H. Haldar, A. Ali, M. Iftikhar, T.A. Zia, and A.Y. Zomaya, "A continuous change detection mechanism to identify anomalies in ECG signals for WBAN-based healthcare environments", IEEE Access, vol. 5, pp. 13531-13544, 2017.
[http://dx.doi.org/10.1109/ACCESS.2017.2714258]
[11]
M.U.H. Al Rasyid, F. Setiawan, I.U. Nadhori, A. Sudarsonc, and N. Tamami, "Anomalous Data Detection in WBAN Measurements", In Proceedings of the 2018 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)Bali, Indonesia
2018 [http://dx.doi.org/10.1109/KCIC.2018.8628522]
[12]
M.B. Mohamed, A.M. Makhlouf, and A. Fakhfakh, "Correlation for Efficient Anomaly Detection in Medical Environment", In: Proceedings of the 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC). Limassol, Cyprus, 25-29, 2018, pp. 548-553",
[http://dx.doi.org/10.1109/IWCMC.2018.8450283]
[13]
G. Smrithy, R. Balakrishnan, and N. Sivakumar, Anomaly detection using dynamic sliding window in wireless body area networks. Data Science and Big Data Analytics., Springer: Berlin/Heidelberg, Germany, 2019, pp. 99-108.
[http://dx.doi.org/10.1007/978-981-10-7641-1_8]
[14]
S.G.S. Nair, and R. Balakrishnan, Mitigating false alarms using accumulator rule and dynamic sliding window in wireless body area networks. CSI Transaction. 6, 203–208., ICT, 2018.
[15]
A. Arfaoui, A. Kribeche, S.M. Senouci, and M. Hamdi, "Game-based adaptive anomaly detection in wireless body area networks", Comput. Netw., vol. 163, .106870
2019 [http://dx.doi.org/10.1016/j.comnet.2019.106870]
[16]
L. Sun, and J. He, "An extensible framework for ECG anomaly detection in wireless body sensor monitoring systems", Int. J. Sensor Netw, vol. 29, no. 2, pp. 101-110, 2019.
[http://dx.doi.org/10.1504/IJSNET.2019.097806]
[17]
S.K. Nagdeo, and J. Mahapatro, "Wireless Body Area Network Sensor Faults and Anomalous Data Detection and Classification Using Ma-chine Learning", In Proceedings of the 2019 IEEE Bombay Section Signature Conference (IBSSC)Mumbai, India 26–28 July 2019. IEEE: New York, NY, USA, 2019, pp. 1-6.
[http://dx.doi.org/10.1109/IBSSC47189.2019.8973004]
[18]
N. Boudargham, R. El Sibai, J. Bou Abdo, J. Demerjian, C. Guyeux, and A. Makhoul, "Toward fast and accurate emergency cases detection in BSNs", IET Wirel. Sens. Syst., vol. 10, no. 1, pp. 47-60, 2020.
[http://dx.doi.org/10.1049/iet-wss.2019.0134]
[19]
O. Salem, K. Alsubhi, A. Mehaoua, and R. Boutaba, "Markov models for anomaly detection in wireless body area networks for secure health monitoring. IEEE Journal of", IEEE J. Sel. Areas Comm., vol. 39, no. 2, pp. 526-540, 2021.
[http://dx.doi.org/10.1109/JSAC.2020.3020602]
[20]
E. Šabić, D. Keeley, B. Henderson, and S. Nannemann, "Healthcare and anomaly detection: Using machine learning to predict anomalies in heart rate data", AI Soc., vol. 36, no. 1, pp. 149-158, 2021.
[http://dx.doi.org/10.1007/s00146-020-00985-1]
[21]
M.S. Kumar, V.S. Dhulipala, and S. Baskar, "Fuzzy unordered rule induction algorithm based classification for reliable communication using wearable computing devices in healthcare", Journal of Ambient. Intell. Humaniz. Comput. Sci., vol. 12, pp. 3515-3526, 2021.
[22]
N. Carlini, and D. Wagner, "Towards evaluating the robustness of neural networks", In 2017 IEEE Symposium on Security and Privacy (SP). IEEE, 2017, pp. 39-57
[http://dx.doi.org/ 10.1109/SP.2017.49]
[23]
J Ian, J Shlens, and C Szegedy, Explaining and harnessing adversarial examples. arXiv, 2014.
[24]
S. Qiu, Q. Liu, S. Zhou, and C. Wu, "Review of artificial intelligence adversarial attack and defense technologies", Appl. Sci. (Basel), vol. 9, no. 5, p. 909, 2019.
[http://dx.doi.org/10.3390/app9050909]
[25]
W Xu, D Evans, and Y Qi, Feature squeezing: Detecting adversarial examples in deep neural networks. arXiv , 2017.
[26]
B. Biggio, B. Nelson, and P. Laskov, "Support vector machines under adversarial label noise", In Asian conference on machine learning, 2011, pp. 97-112
[27]
N. Papernot, P. McDaniel, X. Wu, S. Jha, and A. Swami, "Distillation as a defense to adversarial perturbations against deep neural networks", In 2016 IEEE Symposium on Security and Privacy (SP). IEEE, 2016, pp. 582-597
[http://dx.doi.org/10.1109/SP.2016.41]
[28]
J. Gao, B. Wang, Z. Lin, W. Xu, and Y Qi, Deepcloak: Masking deep neural network models for robustness against adversarial samples. arXiv, 2017.
[29]
V. Balasubramanian, S-S. Ho, and V. Vovk, Conformal prediction for reliable machine learning: theory, adaptations and applications., Newnes, 2014.
[30]
D. Volkhonskiy, I. Nouretdinov, A. Gammerman, V. Vovk, and E Burnaev, Inductive conformal martingales for change-point detection. arXiv, 2017.
[31]
A. Kurakin, I. Goodfellow, and S Bengio, Adversarial examples in the physical world. arXiv, 2016.
[32]
M. Schuster, and K.K. Paliwal, "Bidirectional recurrent neural networks", IEEE Trans. Signal Process., vol. 45, no. 11, pp. 2673-2681, 1997.
[http://dx.doi.org/10.1109/78.650093]
[33]
C. Xiao, N. Chen, C. Hu, K. Wang, Z. Xu, Y. Cai, L. Xu, Z. Chen, and J. Gong, "A spatiotemporal deep learning model for sea surface tem-perature field prediction using time-series satellite data", Environ. Model. Softw., vol. 120, .104502
2019 [http://dx.doi.org/10.1016/j.envsoft.2019.104502]
[34]
G.R. Mode, and K.A. Hoque, Adversarial examples in deep learning for multivariate time series regression. arXiv, 2020.
[35]
G.R. Mode, Adversarial robustness of deep learning enabled industry 4.0 prognostics., University of Missouri: Columbia, 2020.

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