Design Of Intelligent Countermeasure System for Power System Network Security Defense
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Abstract
In an increasingly interconnected world, the convergence of power system networks and biometric-based biomedical applications presents unique challenges for data protection and privacy. This research endeavors to conceptualize and design an intelligent countermeasure system that serves as a robust defense mechanism for enhancing security in this complex ecosystem. The proposed system incorporates biometric authentication techniques to fortify user access controls, implements advanced encryption methods for safeguarding sensitive biomedical data, and intrusion detection and prevention mechanisms to thwart cyber threats. This paper proposed an Integrated Probabilistic Regression Cryptographic Classifier (IPRCC) for data protection and privacy in biometric data for power system devices for biomedical applications. The IPRCC combines probabilistic regression techniques for data analysis with cryptographic methods to fortify the security and privacy of biometric data used within power system devices for biomedical applications. To secure biometric data, IPRCC integrates cryptographic techniques. Cryptography involves encoding information in a way that only authorized parties can decode and understand it. IPRCC incorporates a classifier as part of its security framework. The classifier is used to make decisions or classifications based on the analyzed biometric data. The IPRCC includes enhanced data protection, improved privacy, and increased security for biometric data. The Integrated Probabilistic Regression Cryptographic Classifier (IPRCC) is a sophisticated security system that combines probabilistic regression modeling and cryptographic techniques to protect biometric data used in biomedical applications, especially when integrated with power system devices. Simulation results demonstrated that the proposed IPRCC model exhibits an improved attack detection rate of 99%.
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References
Fidas, C. A., & Lyras, D. (2023). A Review of EEG-Based User Authentication: Trends and Future Research Directions. IEEE Access
Fidas, C. A., Belk, M., Constantinides, A., Portugal, D., Martins, P., Pietron, A. M., ... & Avouris, N. (2023). Ensuring Academic Integrity and Trust in Online Learning Environments: A Longitudinal Study of an AI-centered Proctoring System in Tertiary Educational Institutions. Education Sciences, 13(6), 566.
Constantinides, A., Belk, M., Fidas, C., Beumers, R., Vidal, D., Huang, W., ... & Pitsillides, A. (2023). Security and usability of a personalized user authentication paradigm: Insights from a longitudinal study with three healthcare organizations. ACM Transactions on Computing for Healthcare, 4(1), 1-40.
Alipio, M. I. (2023). Development, evaluation, and analysis of biometric-based bank vault user authentication system through brainwaves. Journal of Ambient Intelligence and Humanized Computing, 14(8), 10165-10179.
Debnath, D., Chettri, S. K., & Dutta, A. K. (2022). Security and privacy issues in internet of things. In ICT Analysis and Applications (pp. 65-74). Springer Singapore.
Akter, S., Reza, F., & Ahmed, M. (2022). Convergence of Blockchain, k-medoids and homomorphic encryption for privacy preserving biomedical data classification. Internet of Things and Cyber-Physical Systems, 2, 99-110.
Ohno-Machado, L., Jiang, X., Kuo, T. T., Tao, S., Chen, L., Ram, P. M., ... & Xu, H. (2023). A hierarchical strategy to minimize privacy risk when linking “De-identified” data in biomedical research consortia. Journal of Biomedical Informatics, 139, 104322.
Kumbhare, A., & Thakur, P. K. (2022). Security and Privacy of Biomedical Data in IoMT. In Cognitive Computing for Internet of Medical Things (pp. 77-104). Chapman and Hall/CRC.
Kim, W., & Seok, J. (2022, February). Privacy-preserving collaborative machine learning in biomedical applications. In 2022 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) (pp. 179-183). IEEE.
Jayaraman, R., Srivastava, A., & Kumar, M. (2022). Blockchain technology for protection of biomedical documents in healthcare society. International Journal of Internet Technology and Secured Transactions, 12(6), 566-582.
Kuo, T. T., Jiang, X., Tang, H., Wang, X., Harmanci, A., Kim, M., ... & Ohno-Machado, L. (2022). The evolving privacy and security concerns for genomic data analysis and sharing as observed from the iDASH competition. Journal of the American Medical Informatics Association, 29(12), 2182-2190.
Acosta, J. N., Falcone, G. J., Rajpurkar, P., & Topol, E. J. (2022). Multimodal biomedical AI. Nature Medicine, 28(9), 1773-1784.
Ahmed, B. K. A., Mahdi, R. D., Mohamed, T. I., Jaleel, R. A., Salih, M. A., & Zahra, M. M. A. (2022). A novel secure artificial bee colony with advanced encryption standard technique for biomedical signal processing. Periodicals of Engineering and Natural Sciences, 10(1), 288-294.
Saxena, A., MISRA, D., Ganesamoorthy, R., Gonzales, J. L. A., Almashaqbeh, H. A., & Tripathi, V. (2022, April). Artificial Intelligence Wireless Network Data Security System For Medical Records Using Cryptography Management. In 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) (pp. 2555-2559). IEEE.
Machnoor, M., Kosta, P., Monge, M., & Lazzi, G. (2022). Rectifier Design for Highly Loaded Inductive Wireless Power Transfer Systems for Biomedical Applications. IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology, 6(4), 574-579.
Vakhter, V., Soysal, B., Schaumont, P., & Guler, U. (2022). Threat modeling and risk analysis for miniaturized wireless biomedical devices. IEEE Internet of Things Journal, 9(15), 13338-13352.
Shukla, P., Akanbi, O., Atuah, A. S., Aljaedi, A., Bouye, M., & Sharma, S. (2022). Cryptography-Based Medical Signal Securing Using Improved Variation Mode Decomposition with Machine Learning Techniques. Computational Intelligence and Neuroscience, 2022.
Prakash, A., Avasthi, S., Kumari, P., & Rawat, M. (2023). Puneet Garg 18 Modern healthcare system: unveiling the possibility of quantum computing in medical and biomedical zones. Quantum-Safe Cryptography Algorithms and Approaches: Impacts of Quantum Computing on Cybersecurity, 249.
Karawanich, K., Kumngern, M., Chimnoy, J., & Prommee, P. (2022). A four-scroll chaotic generator based on two nonlinear functions and its telecommunications cryptography application. AEU-International Journal of Electronics and Communications, 157, 154439.
Shajin, F. H., & Rajesh, P. (2022). FPGA realization of a reversible data hiding scheme for 5G MIMO-OFDM system by chaotic key generation-based paillier cryptography along with LDPC and its side channel estimation using machine learning technique. Journal of Circuits, Systems and Computers, 31(05), 2250093.
Botes, M., & Lenzini, G. (2022, June). When cryptographic ransomware poses cyber threats: Ethical challenges and proposed safeguards for cybersecurity researchers. In 2022 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW) (pp. 562-568). IEEE.
Zhang, H., Chen, J., Zhang, L. Y., Fu, C., Gravina, R., Fortino, G., & Lv, Z. (2022). Low-Cost and Confidential ECG Acquisition Framework Using Compressed Sensing and Chaotic Systems for Wireless Body Area Network. IEEE journal of biomedical and health informatics, 26(12), 5783-5792.
Alhayani, B. S., Hamid, N., Almukhtar, F. H., Alkawak, O. A., Mahajan, H. B., Kwekha-Rashid, A. S., ... & Alkhayyat, A. (2022). Optimized video internet of things using elliptic curve cryptography based encryption and decryption. Computers and Electrical Engineering, 101, 108022.
Alhayani, B. S., Hamid, N., Almukhtar, F. H., Alkawak, O. A., Mahajan, H. B., Kwekha-Rashid, A. S., ... & Alkhayyat, A. (2022). Optimized video internet of things using elliptic curve cryptography based encryption and decryption. Computers and Electrical Engineering, 101, 108022.
Kouhalvandi, L., Matekovits, L., & Peter, I. (2022). Magic of 5G Technology and Optimization Methods Applied to Biomedical Devices: A Survey. Applied Sciences, 12(14), 7096.
Mittal, S., Bansal, A., Gupta, D., Juneja, S., Turabieh, H., Elarabawy, M. M., ... & Bitsue, Z. K. (2022). Using identity-based cryptography as a foundation for an effective and secure cloud model for e-health. Computational Intelligence and Neuroscience, 2022.
Kumari, S., Asha, C. N., Rajashekhar, U., & Viswanath, K. (2022). Performance Analysis of Cloud-based Health Care Data Privacy System Using Hybrid Techniques. International Journal of Biology and Biomedical Engineering, 16, 46-63.