Advancing cyber threat detection through deep learning in management information systems
*S. ArvindCorresponding authorscarvi@rediffmail.comDepartment of Computer Science & Engineering Hyderabad Institute of Technology and ManagementHyderabad, Telangana, 501401, IndiaView full profile → , Bhuvan Unhelkarbunhelkar@usf.eduMuma College of Business University of South Florida 8350 N. Tamiami Trail SarasotaFlorida, 33620, USA0000-0003-1118-3837View full profile → , S. Siva Shankardrsivashankars@gmail.comDepartment of Computer Science and Engineering KG Reddy College of Engineering and TechnologyHyderabad, Telangana, 500100, India0000-0002-7616-6088View full profile → , Prasun Chakrabartidrprasun.cse@gmail.comDepartment of Computer Science and Engineering Sir Padampat Singhania UniversityUdaipur, Rajasthan, 313601, lndia0000-0001-8062-4144View full profile → , S. V. Devikadrdevikasv@gmail.comDepartment of Electronics and Communications Engineering Hyderabad Institute of Technology and ManagementHyderabad, Telangana, 501401, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 07 Feb 2024
- Published Online:
- 18 Dec 2024
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JDMSC-2014
- Pages:
- 2409–2417
Abstract
Keywords
Subject Classifications
References
[1] M. T. Islam, S. Karunasekera, and R. Buyya, “Performance and cost-efficient spark job scheduling based on deep reinforcement learning in cloud computing environments,” IEEE Transactions on Parallel and Distributed Systems, vol. 33, no. 7, pp. 1695–1710, Jul. (2021).
[2] S. Gore, P. K. Mishra, and S. Gore, “Improvisation of Food Delivery Business by Leveraging Ensemble Learning with Various Algorithms,” in Proc. 2023 Int. Conf. Self Sustainable Artificial Intelligence Systems (ICSSAS), pp. 221–229, Oct. (2023).
[3] S. An, A. Leung, J. B. Hong, T. Eom, and J. S. Park, “Toward automated security analysis and enforcement for cloud computing using graphical models for security,” IEEE Access, vol. 10, pp. 75117–75134 (2022).
[4] B. J. Dange, S. Hamsa, G. Patil, S. Gore, S. Roychowdhury, and S. Karmode, “Grape vision: a CNN-based system for yield component analysis of grape clusters,” Int. J. Intell. Syst. Appl. Eng., vol. 11, no. 9s, pp. 239–244 (2023).
[5] P. Mishra, V. Varadharajan, E. S. Pilli, and U. Tupakula, “VMGuard: A VMI-based security architecture for intrusion detection in cloud environment,” IEEE Transactions on Cloud Computing, vol. 8, no. 3, pp. 957–971 (2018).
[6] S. Puri and M. Agnihotri, “A proactive approach for cyber attack mitigation in cloud network,” in Proc. 2017 Int. Conf. Energy, Commun., Data Analytics Soft Comput. (ICECDS), pp. 171–176, Aug. (2017).
[7] D. Garg and R. Agarwal, “Cyber Attacks in Cloud Computing Environment,” in Proc. 2023 8th Int. Conf. Commun. Electron. Syst. (ICCES), pp. 471–475, Jun. (2023).
[8] P. Kumar, M. Tripathi, A. Nehra, M. Conti, and C. Lal, “SAFETY: Early detection and mitigation of TCP SYN flood utilizing entropy in SDN,” IEEE Transactions on Network and Service Management, vol. 15, no. 4, pp. 1545–1559, Dec. (2018).
[9] M. Choraś and R. Kozik, “Machine learning techniques applied to detect cyber attacks on web applications,” Logic J. IGPL, vol. 23, no. 1, pp. 45–56, Jun. 20232015).
[10] J. Pawlick and Q. Zhu, “Strategic trust in cloud-enabled cyber-physical systems with an application to glucose control,” IEEE Transactions on Information Forensics and Security, vol. 12, no. 12, pp. 2906–2919, Dec. (2017).
[11] T. Gopalakrishnan, M. S. A. R. Anwar, R. S. G. K. Basha, and A. K. Sangaiah, “Deep learning enabled data offloading with cyber attack detection model in mobile edge computing systems,” IEEE Access, vol. 8, pp. 185938–185949 (2020).
[12] C. Hahn, H. Kwon, and J. Hur, “Trustworthy delegation toward securing mobile healthcare cyber-physical systems,” IEEE Internet of Things Journal, vol. 6, no. 4, pp. 6301–6309, Aug. (2018).
[13] B. Agheli, M. Adabitabar Firozja, and H. Garg, “Similarity measure for Pythagorean fuzzy sets and application on multiple criteria decision making,” J. Stat. Manag. Syst., vol. 25, no. 4, pp. 749–769 (2022).
[14] I. Elgendi, M. F. Hossain, A. Jamalipour, and K. S. Munasinghe, “Protecting cyber physical systems using a learned MAPE-K model,” IEEE Access, vol. 7, pp. 90954–90963 (2019).
[15] M. Conti, F. De Gaspari, and L. V. Mancini, “A novel stealthy attack to gather SDN configuration-information,” IEEE Transactions on Emerging Topics in Computing, vol. 8, no. 2, pp. 328–340, Apr. (2018).
[16] A. Hanwa and E. Fouotsa, “Elliptic divisibility sequences over the Edwards model of elliptic curves,” J. Discrete Math. Sci. Cryptogr., vol. 25, no. 8, pp. 2289–2304 (2022).
[17] F. Zhang, H. A. D. E. Kodituwakku, J. W. Hines, and J. Coble, “Multilayer data-driven cyber-attack detection system for industrial control systems based on network, system, and process data,” IEEE Transactions on Industrial Informatics, vol. 15, no. 7, pp. 4362–4369, Jul. (2019).
[18] P. Girdhar, P. Johri, and D. Virmani, “Incept_LSTM: Accession for human activity concession in automatic surveillance,” J. Discrete Math. Sci. Cryptogr., vol. 25, no. 8, pp. 2259–2273 (2022).
[19] H. Karimipour and V. Dinavahi, “Robust massively parallel dynamic state estimation of power systems against cyber-attack,” IEEE Access, vol. 6, pp. 2984–2995 (2017).
[20] J. Yang, C. Zhou, S. Yang, H. Xu, and B. Hu, “Anomaly detection based on zone partition for security protection of industrial cyber-physical systems,” IEEE Transactions on Industrial Electronics, vol. 65, no. 5, pp. 4257–4267, May (2017).
[21] M. Ozay, I. Esnaola, F. T. Y. Vural, S. R. Kulkarni, and H. V. Poor, “Machine learning methods for attack detection in the smart grid,” IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 8, pp. 1773–1786, Aug. (2015).
[22] N. Besharati and M. Mortezaeefar, “Determination of the size of defining set for Steiner triple systems,” J. Discrete Math. Sci. Cryptogr., vol. 25, no. 8, pp. 2217–2235 (2022).
