Implementing AI-Driven Anomaly Detection for Cyber-security in Healthcare Networks

Sarat Kahinde Akinade

Abstract


Healthcare organizations are increasingly relying on artificial intelligence (AI) to enhance security measures in response to the growing threat of cyber-attacks and the importance of safeguarding sensitive patient information. This study delves into the impact of AI on healthcare security, with a focus on five crucial areas: anomaly detection, predictive analytics, access control, threat intelligence, and incident response. The results demonstrate that AI can effectively pinpoint unusual patterns or behaviors that may indicate a security breach through anomaly detection. By analyzing historical security breaches and predicting future attacks, AI offers a proactive approach through predictive analytics. Furthermore, AI can streamline user access to patient data by automatically managing permissions based on established rules, thereby enhancing access control. By monitoring and analyzing threat intelligence feeds, AI enables healthcare organizations to stay abreast of the latest security threats and vulnerabilities, bolstering threat intelligence efforts. In incident response, AI proves invaluable by issuing real-time alerts, automating responses to specific incidents, and providing valuable insights into the root causes of security breaches. Ultimately, this study underscores the critical role that AI can play in fortifying healthcare security and safeguarding patient data against cyber threats.


Full Text:

PDF

References


Aderonmu, A. I., Adegnandjou, R. H., Masonta, M. T., & Mzyece, M. (2013). VHF spectrum monitoring using Meraka cognitive radio platform. In K. Jonas, I. A. Rai, & M. Tchuente (Eds.), e-Infrastructure and e-Services for Developing Countries. AFRICOMM 2012. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (Vol. 119). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41178-6_6

Aderonmu, A. I., Magagula, L. A., & Djouani, K. (2014). Spectrum selection technique to reduce the number of channel switching for dynamic spectrum access in cognitive radio networks. In Proceedings of the IASTED International Conference Modelling and Simulation (AfricaMS 2014) (pp. September 1-3, 2014). Gaborone, Botswana.

Aderonmu, A. I., & Ajayi, O. O. (2024). Artificial intelligence-based spectrum allocation strategies for dynamic spectrum access in 5G and IMS networks.

Böse, B., Avasarala, B., Tirthapura, S., Chung, Y., & Steiner, D. (2017). Detecting insider threats using RADISH: A system for real-time anomaly detection in heterogeneous data streams. IEEE Systems Journal, 11(2), 471-482.

Cannoy, S. D., & Salam, A. F. (2010). A framework for health care information assurance policy and compliance. Communications of the ACM, 53(3), 126-131.

Chen, Y., Nyemba, S., Zhang, W., & Malin, B. (2012). Specializing network analysis to detect anomalous insider actions. Security Informatics, 1(1), 1-24.

Connolly, L. Y., Gathegi, J., & Tygar, D. J. (2017). Organisational culture, procedural countermeasures, and employee security behaviour. Information & Computer Security. https://doi.org/10.1108/ICS-03-2017-0013

Gafny, M. A., Shabtai, A., Rokach, L., & Elovici, Y. (2010). Detecting data misuse by applying context-based data linkage.

Health IT Security. (2019). The 10 biggest healthcare data breaches of 2019, so far. @SecurityHIT.

Humer, C., & Finkle, J. (2014, September 24). Your medical record is worth more to hackers than your credit card. Reuters.

Islam, S., Hasan, M., Wang, X., Germack, H. D., & Noor-E-Alam. (2018). A systematic review on healthcare analytics: Application and theoretical perspective of data mining. Healthcare (Basel), 6(2), 54.

Predd, J., Pfleeger, S. L., Hunker, J., & Bulford, C. (2018, August 5). Insiders behaving badly. IEEE Journals & Magazine.

Safa, N. S., Sookhak, M., Von Solms, R., Furnell, S., Ghani, N. A., & Herawan, T. (2015). Information security conscious care behaviour formation in organizations. Computers & Security, 53, 65-78.

Telo, J. (2016). AI for enhanced healthcare security: An investigation of anomaly detection, predictive analytics, access control, threat intelligence, and incident response.

Tetz, E. (2018). Network firewalls: Perimeter defense - dummies.

Verizon. (2019). Data breaches report.

Walker-Roberts, S., Hammoudeh, M., & Dehghantanha, A. (2018). A systematic review of the availability and efficacy of countermeasures to internal threats in healthcare critical infrastructure. IEEE Access, 6, 25167-25177.

Yeng, P. K., Yang, B., & Snekkenes, E. A. (2019). Framework for healthcare staffs’ information security practice analysis: Psycho-socio-cultural context. Journal of Medical Internet Research.

Zhang, H., Mehotra, S., Liebovitz, D., Gunter, C., & Malin, B. (2013). Mining deviations from patient care pathways via electronic medical record system audits. ACM Transactions on Management Information Systems (TMIS), 4.


Refbacks

  • There are currently no refbacks.