Artificial Intelligence and Machine Learning Techniques for Anomaly Detection and Threat Mitigation in Cloud-Connected Medical Devices
DOI:
https://doi.org/10.38124/ijsrmt.v3i3.26Abstract
The Internet of Medical Things (IoMT) has begun functioning like this: improved patient monitoring and an easily accessible digital data warehouse. Despite that, this methodology of the internet will potentially have a counter balance which risks for patient data might involve hacking, data theft, and unauthorized access that may contain great consequences for patient privacy and safety. This article examines the possibility of utilizing new AI technology, including inter alia deep learning, unsupervised learning, and ensemble learning to further boost anomaly detection and threat management in connected cloud medical systems. Many old rules and approaches based on statistics lose relevancy versus the dynamics and unpredictability of modern cyberattacks. Identification of anomalies in cyber security is nearly unavoidable, and it should be the first and the last reaction for detecting irregularities in behavior that may indicate undesirable acts or attacks. The paper aims at understanding how AI/ML approaches can give more sophisticated and versatile interventions for finding out anomalies in cloud-attached medical machines. Moreover, this research details robust AI/ML methods such as the adversarial machine learning and reinforcement learning for a perfect threat mitigation. These techniques which activates machine learning models to learn from data continuing to adjust to new evolving threats and then to establish intelligent and proactive threat response systems. The data experiment, which focuses on relevant data sets, reveals that it is the AI/ML techniques that possess the upper hand over traditional methods when it comes to identifying anomalies and defending against threats for cloud-connected medical devices. Such finding expresses much significance for the healthcare industry, as it gives room for the inclusion of AI/ML techniques into the security systems of the medical devices, which are all connected to the cloud. Through the employment of these strategies, healthcare units will become better able to detect and halt any form of threat and as a consequence patients’ data will be protected, devices will continue operating effectively, and eventually patients’ safety and healthcare units will benefit and gain trust from patients.
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References
Systematic review on supervised and unsupervised machine learning algorithms for data science. Supervised and unsupervised learning for data science, 3- 21.
Brown, G. (2010). Ensemble Learning. Encyclopedia of machine learning, 312, 15-19.
Butpheng, C., Yeh, K. H., & Xiong, H. (2020). Security and privacy in IoT-cloud-based e- health systems—A comprehensive review. Symmetry, 12(7), 1191.
Calabrese, M., Cimmino, M., Fiume, F., Manfrin, M., Romeo, L., Ceccacci, S., ... & Kapetis, D. (2020). SOPHIA: An event- based IoT and machine learning architecture for predictive maintenance in industry 4.0. Information, 11(4), 202.
Dang, L. M., Piran, M. J., Han, D., Min, K., & Moon, H. (2019). A survey on internet of things and cloud computing for healthcare. Electronics, 8(7), 768.
Das, S., Dey, A., Pal, A., & Roy, N. (2015).
Applications of artificial intelligence in machine learning: review and prospect. International Journal of Computer Applications, 115(9).
Elmrabit, N., Zhou, F., Li, F., & Zhou, H. (2020, June). Evaluation of machine learning algorithms for anomaly detection. In 2020 international conference on cyber security and protection of digital services (cyber security) (pp. 1-8). IEEE.
Elsayed, M. A., & Zulkernine, M. (2020). PredictDeep: security analytics as a service for anomaly detection and prediction. IEEEAccess, 8, 45184-45197.
Gabriel Michau, Olga Fink. (2021). Unsupervised transfer learning for anomaly detection: Application to complementary operating condition transfer. Science direct. https://www.sciencedirect.com/science/a rticle/pii/S0950705121000794
Garg, S., Kaur, K., Kumar, N., Kaddoum, G., Zomaya, A.Y., & Ranjan, R. (2019). A Hybrid Deep Learning-Based Model for Anomaly Detection in Cloud Datacenter Networks. IEEE Transactions on Network and Service Management, 16, 924-935.
Goldstein, M., & Uchida, S. (2016). A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data. PloS one, 11(4), e0152173.
González-Granadillo, G., González-Zarzosa, S., & Diaz, R. (2021). Security information and event management (SIEM): analysis, trends, and usage in critical infrastructures. Sensors, 21(14), 4759.
Liang, D., Krishnan, R. G., Hoffman, M. D., & Jebara, T. (2018, April). Variational autoencoders for collaborative filtering. In Proceedings of the 2018 world wide web conference (pp. 689-698).
Lu, Y., & Da Xu, L. (2018). Internet of Things (IoT) cybersecurity research: A review of current research topics. IEEE Internet of Things Journal, 6(2), 2103- 2115.
Naeem, M., Rizvi, S. T. H., & Coronato, A. (2020). A gentle introduction to reinforcement learning and its application in different fields. IEEE access, 8, 209320- 209344.
Neftci, E. O., & Averbeck, B. B. (2019). Reinforcement learning in artificial and biological systems. Nature Machine Intelligence, 1(3), 133-143.
Papernot, N., Mc Daniel, P., Jha, S., Fredrikson, M., Celik, Z. B., & Swami, A. (2016, March). The limitations of deep learning in adversarial settings. In 2016 IEEE European symposium on security and privacy (EuroS&P) (pp. 372-387). IEEE. Pawar, K., & Attar, V. Z. (2020). Assessment of auto encoder architectures for data representation. Deep learning: concepts and architectures, 101-132.
Raschka, S., Patterson, J., & Nolet, C. (2020). Machine learning in python: Main developments and technology trends in data science, machine learning, and artificial intelligence. Information, 11(4), 193.
Rosenberg, I., Shabtai, A., Elovici, Y., & Rokach, L. (2021). Adversarial machine learning attacks and defense methods in the cyber security domain. ACM Computing Surveys (CSUR), 54(5), 1-36.
Samaila, M. G., Neto, M., Fernandes, D. A., Freire, M. M., & Inácio, P. R. (2018). Challenges of securing Internet of Things devices: A survey. Security and Privacy, 1(2), e20.
Sato, J. R., Rondina, J. M., & Mourão- Miranda, J. (2012). Measuring abnormal brains: building normative rules in neuroimaging using one-class support vector machines. Frontiers in neuroscience, 6, 34006.
Serackis, A., & Jankauskas, M. (2022). Exploring the limits of early predictive maintenance applying anomaly detection technique.
Skowronski, M., Kale, K., Borzak, S., & Chait, R. (2018). Cloud Connected Non- Invasive Medical Device for Instant Left Ventricular Dysfunction Assessment via Any Smartphone. Iproceedings, 4(2), e11880.
Sridhar, S., & Govindarasu, M. (2014). Model-based attack detection and mitigation for automatic generation control. IEEE Transactions on Smart Grid, 5(2), 580-591.
Thanh, Hoang & Tran, Lang. (2018). An approach to reduce data dimension in building effective Network Intrusion Detection Systems. EAI Endorsed Transactions on Context-aware Systems and Applications. 6. 162633.10.4108/eai.13-7-2018.162633.
Usama, M., Qadir, J., Raza, A., Arif, H., Yau, K. L. A., Elkhatib, Y., ... & Al-Fuqaha, A. (2019). Unsupervised machine learning for networking: Techniques, applications and research challenges. IEEE access, 7, 65579-65615.
Wang, S., Balarezo, J. F., Kandeepan, S., Al- Hourani, A., Chavez, K. G., & Rubinstein, B. (2021). Machine learning in network anomaly detection: A survey. IEEE Access, 9, 152379-152396.
Wang, W., Sun, D., Jiang, F., Chen, X., & Zhu, (2022). Research and challenges of reinforcement learning in cyber defense decision-making for intranet security. Algorithms, 15(4), 134.
Zounemat-Kermani, M., Batelaan, O., Fadaee, M., & Hinkelmann, R. (2021). Ensemble machine learning paradigms in hydrology: A review. Journal of Hydrology, 598, 126266.
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