Implementing adaptive security measures in intelligent networks within the gaming industry to prevent cyber threats
Yazdani Hasanyazdani.hasan@niu.edu.inDepartment of Computer Sciences Noida International University Greater Noida, Uttar Pradesh, 203201, IndiaView full profile → , *Sujata AryaCorresponding authorsujataarya@symlaw.ac.inSymbiosis Centre for Advanced Legal Studies and Research (SCALSAR) Symbiosis Law School Symbiosis International (Deemed University)Pune, Maharashtra, 411014, IndiaView full profile → , Varinder Singh Ranar.varinder@cu.ac.aeCollege of Business City University Ajman Al Tallah 2Ajman, 00000, United Arab EmiratesView full profile → , Ganesh Bhutkarganesh.bhutkar@vit.eduDepartment of Computer Engineering Vishwakarma Institute of TechnologyPune, Maharashtra, 411037, IndiaView full profile → , Thokala Venkata Subba Raovsubbarao11@gmail.comDepartment of Science & Humanities KITS Akshar Institute of TechnologyGuntur, Andhra Pradesh, 522019, IndiaView full profile → , B. Kalyanikalyanib1@gmail.comDepartment of Information Technology KKR and KSR Institute of Technology and SciencesGuntur, Andhra Pradesh, 522017, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 09 Apr 2025
- Published Online:
- 16 Feb 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JDMSC-2644
- Pages:
- 1049–1057
Abstract
Keywords
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References
[1] C. Sánchez-Zas, X. Larriva-Novo, V. A. Villagrá, M. S. Rodrigo, and J. I. Moreno, “Design and evaluation of unsupervised machine learning models for anomaly detection in streaming cybersecurity logs,” Mathematics, vol. 10, pp. 4043 (2022).
[2] F. Alrowais, S. Althahabi, S. S. Alotaibi, A. Mohamed, M. A. Hamza, and R. Marzouk, “Automated machine learning enabled cybersecurity threat detection in Internet of Things environment,” Computer Systems Science and Engineering, vol. 45, pp. 687–700 (2023).
[3] D. Goyal, F. Sheth, P. Mathur, and A. K. Gupta, “Discrete mathematical models for enhancing cybersecurity: A mathematical and statistical analysis of machine learning approaches in phishing attack detection,” J. Discrete Math. Sci. Cryptogr., vol. 27, pp. 569–599 (2024).
[4] R. Alluhaibi, “Quantum machine learning for advanced threat detection in cybersecurity,” Int. J. Saf. Secur. Eng., vol. 14, pp. 875–883 (2024).
[5] H. N. Fakhouri, F. M. Awaysheh, S. Alawadi, M. Alkhalaileh, and F. Hamad, “Four vector intelligent metaheuristic for data optimization,” Computing, vol. 106, pp. 2321–2359 (2024).
[6] W. Luo, X. Lin, C. Li, S. Yang, and Y. Shi, “Benchmark functions for CEC 2022 competition on seeking multiple optima in dynamic environments,” arXiv preprint, arXiv:2201.00523 (2022).
[7] F. Zhang, S. Wu, and P. Cen, “The past, present and future of the pangolin in mainland China,” Glob. Ecol. Conserv., vol. 33, pp. e01995 (2022).
[8] R. N. Wadibhasme, A. U. Chaudhari, P. Khobragade, H. D. Mehta, R. Agrawal, and C. Dhule, “Detection and prevention of malicious activities in vulnerable network security using deep learning,” in Proc. 2024 Int. Conf. Innov. Challenges Emerg. Technol. (ICICET), pp. 1–6 (Jun. 2024).
[9] H. Fakhouri, F. Hamad, and A. Alawamrah, “Success history intelligent optimizer,” J. Supercomput., vol. 78, pp. 6461–6502 (2022).
[10] S. Mohapatra and P. Mohapatra, “American zebra optimization algorithm for global optimization problems,” Sci. Rep., vol. 13, pp. 5211 (2023).
[11] L. Abualigah, D. Yousri, M. A. Elaziz, A. Ewees, M. Al-Qaness, and A. Gandomi, “Aquila optimizer: A novel meta-heuristic optimization algorithm,” Comput. Ind. Eng., vol. 157, pp. 107250 (2021).
[12] S. M. Shabeer, K. L. Keerthi, D. P. Aditya, K. Priyanka, and L. Abhinay, “Stylometric author identification via CNN-BiLSTM architecture on syntactic text patterns,” Int. J. Adv. Comput. Eng. Commun. Technol., vol. 14, no. 1, pp. 1–9 (2025).
[13] J. Roberts and J. Clarke, “Quantum computing for cryptanalysis: Breaking modern cryptographic protocols,” Int. J. Adv. Electr. Comput. Eng., vol. 13, no. 2, pp. 24–32 (2025).
