A Design for Cyberattack Detection in Online Financial Sector Management Through Hyperparameter Optimization and Enhanced Whale Optimization
DOI:
https://doi.org/10.69996/ijari.2026009Keywords:
Enhanced Whale Optimization Algorithm (EWOA), Invasive Weed Optimization (IWO), Transition Probability Matrix (TPM), Markov Chain (MC), Online Financial Sector ManagementAbstract
Gravitational waves are ripples in the curvature of space-time that propagate at the speed of light in a vacuum and represent a major prediction of Einstein’s General Theory of Relativity. The weak interaction of gravitational waves with matter allows them to propagate virtually unscattered, making them important tools for studying astrophysical phenomena that are difficult to observe through electromagnetic radiation. However, this same property also makes them extremely difficult to detect because the amplitudes reaching Earth are extremely small. This study investigates theoretical aspects of gravitational wave data analysis, focusing on the detection of signals from inspiraling compact binaries and rigidly rotating non-axisymmetric pulsars. The study discusses gravitational waves in the linearized theory, astrophysical sources of gravitational waves, stochastic backgrounds, and gravitational wave detectors including resonant mass detectors and laser interferometric detectors. Laser interferometric detectors such as LIGO, VIRGO, GEO600, and TAMA are designed to achieve extremely high sensitivity levels in strain measurements. Advanced detectors aim to measure strains as small as ~10⁻²², requiring mitigation of thermal vibrations, seismic activity, and gravity gradients. The study also highlights burst, continuous, and stochastic sources of gravitational waves, including supernovae, compact binary inspirals, coalescing binaries, rapidly rotating neutron stars, and primordial stochastic backgrounds
References
[1] S. Mishra, “Exploring the impact of AI-based cyber security financial sector management,” Applied Sciences, 13(10), 2023.
[2] A.B. Hajira Be and S. Gokulraj, “Cyber Attacks Classification Using Supervised Machine Learning Techniques,” JSIHS, 3(1), 2025, 57–67.
[3] A. Q. Md, D. Jaiswal, J. Daftari, S. Haneef, C. Iwendi, and S. K. Jain, “Efficient dynamic phishing safeguard system using neural boost phishing protection,” Electronics, 11(19), 2022.
[4] Ankush D. Sawarkar and Anjali Deepak Hazari, “IoT Forensic Cyber Activities Detection and Prevention with Automated Machine Learning Model,” JSIHS, 2(2), 2024, 1–15.
[5] A. Gusev, “Domestic private banking solutions can be quite successful as an effective protection against whaling-style cyber attacks which are used as a basis for more complex targeted phishing,” Procedia Computer Science, 213, 2022, 391–399.
[6] P.-H. Nguyen, T.-V. Pham, L.-A. T. Nguyen, H.-A. T. Pham, T.-H. T. Nguyen, and T.-G. Vu, “Assessing cybersecurity risks and prioritizing top strategies in Vietnam’s finance and banking system using strategic decision-making models-based neutrosophic sets and Z number,” Heliyon, 10(19), 2024.
[7] O. E. Akinbowale, H. E. Klingelhöfer, M. F. Zerihun, and P. Mashigo, “Development of a policy and regulatory framework for mitigating cyberfraud in the South African banking industry,” Heliyon, 10(1), 2024.
[8] E. M. Alrawhani, A. Romli, and M. A. Al-Sharafi, “Evaluating the role of protection motivation theory in information security policy compliance: Insights from the banking sector using PLS-SEM approach,” Journal of Open Innovation: Technology, Market, and Complexity, 11(1), 2025.
[9] M. Asmar and A. Tuqan, “Integrating machine learning for sustaining cybersecurity in digital banks,” Heliyon, 10(17), 2024.
[10] S. A. A. A. Alsaidi, R. R. N. Al Ogaili, Z. A. A. Alyasseri, D. Al-Shammary, A. Ibaida, and A. Slowik, “Multi-objective Markov-enhanced adaptive whale optimization cybersecurity model for binary and multi-class malware cyberthreat classification,” Journal of Electronic Science and Technology, 23(4), 2025.
[11] S. Lokuliyana, A. G. A. Kalupahanage, H. M. S. D. Herath, D. Siriwardana, D. N. Bulathsinhala, and H. M. T. M. Herath, “Enhancing IoT resilience: Machine learning techniques for autonomous anomaly detection and threat mitigation,” Procedia Computer Science, 254, 2025, 68–77.
[12] M. Hawedi, A. Albaghdadi, E. Alwheshe, A. Altalibe, and N. A. A. M. Yahiad, “CA-MLIDS: A novel context-aware machine learning-based intrusion detection system for phishing attack detection,” Procedia Computer Science, 265, 2025, 91–98.
[13] M. Ragab, M. Basheri, N. N. Albogami, A. Subahi, O. A. Abdulkader, H. Alaidaros, H. Mousa, and A. A.-M. AL-Ghamdi, “Artificial intelligence driven cyberattack detection system using integration of deep belief network with convolution neural network on industrial IoT,” Alexandria Engineering Journal, 110, 2025, 438–450.
[14] R. Ravi Kanth and T. Prem Jacob, “Evolutionary gravitational neocognitron neural network espoused blockchain-based intrusion detection framework for enhancing cybersecurity in a cloud computing environment,” Ain Shams Engineering Journal, 16(12), 2025.
[15] A. Prasad, S. Chandra, W. M. Alenazy, G. Ali, S. Shah, and M. ElAffendi, “AndroMD: An Android malware detection framework based on source code analysis and permission scanning,” Results in Engineering, 28, 2025.
Published
Issue
Section
License
Copyright (c) 2026 International Journal of Advance Research and Innovation(IJARI, ISSN: 2347-3258)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Fringe Global Scientific Press publishes all the papers under a Creative Commons Attribution-Non-Commercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/) license. Authors have the liberty to replicate and distribute their work. Authors have the ability to use either the whole or a portion of their piece in compilations or other publications that include their own work. Please see the licensing terms for more information on reusing the work.
