Skip to content
2000
  1. Home
  2. A-Z Publications
  3. Recent Patents on Engineering
  4. Volume 19, Issue 1
  5. Article
Volume 19, Issue 1
  • ISSN: 1872-2121
  • E-ISSN: 2212-4047

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.

Results

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.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/eng/10.2174/0118722121255695231008171935
2025-01-01
2024-11-26

  • From This Site

    /content/journals/eng/10.2174/0118722121255695231008171935
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. KalpanaR.N.L.S. Nageshwar RaoD. 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.
     [Google Scholar]
  2. AlbattahA. RassamM.A. A correlation-based anomaly detection model for wireless body area networks using convolutional long short-term memory neural network.Sensors 2022225195110.3390/s22051951 35271097
     [Google Scholar]
  3. NewazA.I. SikderA.K. RahmanM.A. UluagacA.S. Healthguard: A Machine Learning-Based Security Framework for Smart Healthcare SystemsIn: 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, 2019.10.1109/SNAMS.2019.8931716
     [Google Scholar]
  4. FahimM. SillittiA. Anomaly detection, analysis and prediction techniques in IoT environment: A systematic literature review.IEEE Access20197816648168110.1109/ACCESS.2019.2921912
     [Google Scholar]
  5. DehabadiM.S.Z. JahedM. Reliability Modeling of Anomaly Detection Algorithms for Wireless Body Area NetworksProceedings of the 2017 Iranian Conference on Electrical Engineering (ICEE)Tehran, Iran 2–4 May 2017. IEEE: New York, NY, USA, 2017, pp. 70-75.10.1109/IranianCEE.2017.7985142
     [Google Scholar]
  6. SaraswathiS. SureshG.R. KatiravanJ. False alarm detection using dynamic threshold in medical wireless sensor networks.Wirel. Netw.202127292593710.1007/s11276‑019‑02197‑y
     [Google Scholar]
  7. SalemO. SerhrouchniA. MehaouaA. BoutabaR. Event detection in wireless body area networks using Kalman filter and power divergence.IEEE Trans. Netw. Serv. Manag.20181531018103410.1109/TNSM.2018.2842195
     [Google Scholar]
  8. SanejaB. RaniR. An integrated framework for anomaly detection in big data of medical wireless sensors.Modern Physics.2018321850283
     [Google Scholar]
  9. PachauriG. SharmaS. Anomaly detection in medical wireless sensor networks using machine learning algorithms.Procedia Comput. Sci.20157032533310.1016/j.procs.2015.10.026
     [Google Scholar]
  10. KhanF.A. HaldarN.A.H. AliA. IftikharM. ZiaT.A. ZomayaA.Y. A continuous change detection mechanism to identify anomalies in ECG signals for WBAN-based healthcare environments.IEEE Access20175135311354410.1109/ACCESS.2017.2714258
     [Google Scholar]
  11. Al RasyidM.U.H. SetiawanF. NadhoriI.U. SudarsoncA. TamamiN. Anomalous Data Detection in WBAN MeasurementsProceedings of the 2018 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)Bali, Indonesia201810.1109/KCIC.2018.8628522
     [Google Scholar]
  12. MohamedM.B. MakhloufA.M. FakhfakhA. 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. 548-553.10.1109/IWCMC.2018.8450283
     [Google Scholar]
  13. SmrithyG. BalakrishnanR. SivakumarN. Anomaly detection using dynamic sliding window in wireless body area networks. Data Science and Big Data Analytics.Berlin/Heidelberg, GermanySpringer20199910810.1007/978‑981‑10‑7641‑1_8
     [Google Scholar]
  14. NairS.G.S. BalakrishnanR. Mitigating false alarms using accumulator rule and dynamic sliding window in wireless body area networks. CSI Transaction. 6, 203–208.ICT2018
     [Google Scholar]
  15. ArfaouiA. KribecheA. SenouciS.M. HamdiM. Game-based adaptive anomaly detection in wireless body area networks.Comput. Netw.201916310687010.1016/j.comnet.2019.106870
     [Google Scholar]
  16. SunL. HeJ. An extensible framework for ECG anomaly detection in wireless body sensor monitoring systems.Int. J. Sensor Netw201929210111010.1504/IJSNET.2019.097806
     [Google Scholar]
  17. NagdeoS.K. MahapatroJ. Wireless Body Area Network Sensor Faults and Anomalous Data Detection and Classification Using Ma-chine LearningProceedings of the 2019 IEEE Bombay Section Signature Conference (IBSSC)Mumbai, India 26–28 July 2019. IEEE: New York, NY, USA, 2019, pp. 1-6.10.1109/IBSSC47189.2019.8973004
     [Google Scholar]
  18. BoudarghamN. El SibaiR. Bou AbdoJ. DemerjianJ. GuyeuxC. MakhoulA. Toward fast and accurate emergency cases detection in BSNs.IET Wirel. Sens. Syst.2020101476010.1049/iet‑wss.2019.0134
     [Google Scholar]
  19. SalemO. AlsubhiK. MehaouaA. BoutabaR. Markov models for anomaly detection in wireless body area networks for secure health monitoring. IEEE Journal of.IEEE J. Sel. Areas Comm.202139252654010.1109/JSAC.2020.3020602
     [Google Scholar]
  20. ŠabićE. KeeleyD. HendersonB. NannemannS. Healthcare and anomaly detection: Using machine learning to predict anomalies in heart rate data.AI Soc.202136114915810.1007/s00146‑020‑00985‑1
     [Google Scholar]
  21. KumarM.S. DhulipalaV.S. BaskarS. Fuzzy unordered rule induction algorithm based classification for reliable communication using wearable computing devices in healthcare.Journal of Ambient. Intell. Humaniz. Comput. Sci.20211235153526
     [Google Scholar]
  22. CarliniN. WagnerD. Towards evaluating the robustness of neural networks2017 IEEE Symposium on Security and Privacy (SP). IEEE2017395710.1109/SP.2017.49
     [Google Scholar]
  23. IanJ ShlensJ SzegedyC Explaining and harnessing adversarial examples arXiv, 2014.
     [Google Scholar]
  24. QiuS. LiuQ. ZhouS. WuC. Review of artificial intelligence adversarial attack and defense technologies.Appl. Sci. (Basel)20199590910.3390/app9050909
     [Google Scholar]
  25. XuW EvansD QiY Feature squeezing: Detecting adversarial examples in deep neural networks arXiv , 2017.
     [Google Scholar]
  26. BiggioB. NelsonB. LaskovP. Support vector machines under adversarial label noiseAsian conference on machine learning201197112
     [Google Scholar]
  27. PapernotN. McDanielP. WuX. JhaS. SwamiA. Distillation as a defense to adversarial perturbations against deep neural networks2016 IEEE Symposium on Security and Privacy (SP). IEEE201658259710.1109/SP.2016.41
     [Google Scholar]
  28. GaoJ. WangB. LinZ. XuW. QiY Deepcloak: Masking deep neural network models for robustness against adversarial samplesarXiv, 2017.
     [Google Scholar]
  29. BalasubramanianV. HoS-S. VovkV. Conformal prediction for reliable machine learning: theory, adaptations and applications.Newnes2014
     [Google Scholar]
  30. VolkhonskiyD. NouretdinovI. GammermanA. VovkV. BurnaevE Inductive conformal martingales for change-point detectionarXiv, 2017.
     [Google Scholar]
  31. KurakinA. GoodfellowI. BengioS Adversarial examples in the physical worldarXiv, 2016.
     [Google Scholar]
  32. SchusterM. PaliwalK.K. Bidirectional recurrent neural networks.IEEE Trans. Signal Process.199745112673268110.1109/78.650093
     [Google Scholar]
  33. XiaoC. ChenN. HuC. WangK. XuZ. CaiY. XuL. ChenZ. GongJ. A spatiotemporal deep learning model for sea surface tem-perature field prediction using time-series satellite data.Environ. Model. Softw.201912010450210.1016/j.envsoft.2019.104502
     [Google Scholar]
  34. ModeG.R. HoqueK.A. Adversarial examples in deep learning for multivariate time series regression arXiv, 2020.
     [Google Scholar]
  35. ModeG.R. Adversarial robustness of deep learning enabled industry 4.0 prognostics.ColumbiaUniversity of Missouri2020
     [Google Scholar]
/content/journals/eng/10.2174/0118722121255695231008171935
Loading
Finding the Efficiency of ConvBi-LSTM Over Anticipation of Adversaries in WBANs
Recent Pat Eng 19, E161023222246 (2025); https://doi.org/10.2174/0118722121255695231008171935
/content/journals/eng/10.2174/0118722121255695231008171935
/content/journals/eng/10.2174/0118722121255695231008171935
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): adversarial attacks; CMS; CNN; ConvBi- LSTM; MIMIC; WBANs

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test