Measuring network security in the cloud : A roadmap for proactive defense

Singha, Anjani Kumar; Kundu, Shakti; Singh, Harsh Pratap; Songare, Lokendra Singh; Tiwari, Pradeep Kumar

doi:10.47974/JDMSC-1964

Open Access Research Article

Measuring network security in the cloud : A roadmap for proactive defense

Anjani Kumar Singhaanjani348@gmail.comDepartment of Computer ApplicationsDelhi-NCR CampusSRM Institute of Science and Technology (Deemed to be University)Ghaziabad, Uttar Pradesh, 201204, IndiaView full profile → , Shakti Kundushaktikundu@gmail.comSchool of Engineering and Technology Computer Science EngineeringBML Munjal University (BMU)Gurugram, Haryana, 122413, IndiaView full profile → , Harsh Pratap Singhdrharshprataps@gmail.comDepartment of Computer Science and EngineeringSri Vaishnav Vidhyapeeth VishwavidyalayaIndore, Madhya Pradesh, 453331, IndiaView full profile → , Lokendra Singh Songarelokendra.songare@gmail.comDepartment of Computer Science and EngineeringMedicaps UniversityIndore, Madhya Pradesh, IndiaView full profile → , ^*Pradeep Kumar TiwariCorresponding authorPradeeptiwari.mca@gmail.comDepartment of Computer Science and ApplicationsVishwanath Karad MIT World Peace University, PunePune, Maharashtra, 411005, IndiaView full profile →

* Corresponding author · click or hover a name for details

pp. 889–902Vol. 27Issue 2-BMarch 2024DOI: 10.47974/JDMSC-1964 Crossmark XML

Published Online:: 12 Apr 2024
Article type:: Research Article
Language:: EN
Article no.:: JDMSC-1964
Pages:: 889–902

Abstract

An exhaustive examination and evaluation of hierarchy network security measuring and optimal active defense in the context of cloud computing is provided in this paper. After collecting all the cloud platform’s security-related data obtained via evaluation, the pertinent security data is summarized and analyzed to create the cloud platform’s safety record index. This index serves as a benchmark for the cloud platform administrators to evaluate the security vulnerabilities associated with their cloud platform. It offers cloud platform administrators a standard against which to assess the security hazards of cloud platforms. We primarily investigate the building of the cloud platform, the building process of the security situational awareness system, and the estimation of the safety circumstance value and use via the cloud platform’s security circumstance understanding system, thereby significantly enhancing the cloud platform’s stability, security, and dependability. By implementing the approach, the limitations of conventional network security management are circumvented, as the latter is predicated solely on historical data and is incapable of perceiving real-time alterations in the security condition of the system. Concurrently, the anticipated outcomes are incorporated into the imprecise decision-making system’s input, enhancing the evaluation’s precision. The approach improves the efficiency and efficacy of predicting the network safety situation in real time, augments the algorithm’s rate of convergence and prediction precision, and prevents overfitting from occurring. Simulation experiments conducted using the internet network safety posture dataset demonstrate that this research approach exhibits superior learning efficiency and lower prediction error compared to conventional machine learning and other deep learning techniques.

Keywords

Computer network security Association rules Data mining Security scenarios Risk assessment

Subject Classifications

Primary 93A30Secondary 49K15

References

[1] J.-H. Cho, D. P. Sharma, H. Alavizadeh et al., “Toward proactive, adaptive defense: a survey on moving target defense,” IEEE Communications Surveys & Tutorials, vol. 22,no. 1, pp. 709–745 (2020).
[2] N. Agrawal and S. Tapaswi, “Defense mechanisms against DDoS attacks in a cloud computing environment: state-of the-art and research challenges,” IEEE Communications Surveys & Tutorials, vol. 21, no. 4, pp. 3769–3795 (2019).
[3] A. K. Singha, S. Zubair, A. Malibari, N. Pathak, S. Urooj, and N. Sharma ,” Design of ANN Based Non-Linear Network Using Interconnection of Parallel Processor,” Computer Systems Science & Engineering, 46(3)(2023).
[4] S. Zubair, A. K. Singha, N. Pathak, N. Sharma, S. Urooj, and S. R. Larguech ,” Performance Enhancement of Adaptive Neural Networks Based on Learning Rate,” Computers, Materials & Continua, 74(1)(2023).
[5] C. Hongsong, Z. Yongpeng, C. Yongrui, and B. Bhargava, “Security threats and defensive approaches in machine learning system under big data environment,” Wireless Personal Communications, vol. 117, no. 4, pp. 3505–3525 (2021).
[6] C. Zhou, B. Hu, Y. Shi, Y. C. Tian, X. Li, and Y Zhao, “A uniﬁed architectural approach for cyberattack-resilient in- dustrial control systems,” Proceedings of the IEEE, vol. 109, no. 4, pp. 517–541 (2020).
[7] S. S. Rupra and A. Omamo, “A cloud computing security assessment framework for small and medium enterprises [J],” Journal of Information Security, vol. 11, no. 4, pp. 201–224 (2020).
[8] P. Kumari and P. Kaur, “A survey of fault tolerance in cloud computing,” Journal of King Saud University - Computer and Information Sciences, vol. 33, no. 10, pp. 1159–1176 (2021).
[9] G. Kalnoor and S. Gowrishankar, “IoT-based smart envi- ronment using intelligent intrusion detection system,” Soft Computing, vol. 25, no. 17, pp. 11573–11588 (2021).
[10] M. Imthiyas, S. Wani, R. A. K. A. Abdulghafor, A. A. Ibrahim, and A. H. Mohammad, “DDoS mitigation: a review of content delivery network and its DDoS defence techniques,” International Journal of Pervasive Computing and Communications, vol. 6, no. 2, pp. 67–76 (2020).
[11] R. Mythili, N. Parthiban, and V. Kavitha,” Construction of heterogeneous medical knowledge graph from electronic health records,” Journal of Discrete Mathematical Sciences and Cryptography, 25(4), 921-930 (2022).
[12] Y. Wu, H. N. Dai, and H. Wang, “Convergence of blockchain and edge computing for secure and scalable IIoT critical infrastructures in industry 4.0[J],” IEEE Internet of Things Journal, vol. 8, no. 4, pp. 2300–2317 (2020).
[13] R. Gupta, and S. K. Malik ,” An analysis of SPARQL usage for information retrieval in heterogeneous domains through various tools,” Journal of Discrete Mathematical Sciences and Cryptography, 25(4), 1031-1040 (2022).
[14] A. K. Singha, N. Pathak, N. Sharma, A. Gandhar, S., Urooj, S. Zubair, and G. Nagalaxmi ,” An Experimental Approach to Diagnose Covid-19 Using Optimized CNN,” Intelligent Automation & Soft Computing, 34(2) (2022).
[15] M. Zhu, A. H. Anwar, Z. Wan, J.-H. Cho, C. A. Kamhoua, and M. P. Singh, “A survey of defensive deception: approaches using game theory and machine learning,” IEEE Communications Surveys & Tutorials, vol. 23, no. 4, pp. 2460–2493 (2021).
[16] L. Pallavi, A. Jagan, and B. T. Rao, “ERMO2 algorithm: an energy eﬃcient mobility management in mobile cloud computing system for 5G heterogeneous networks,” International Journal of Electrical and Computer Engineering, vol. 9, no. 3, pp. 1957–1967 (2019).
[17] M. H. Hassan, S. A. Mostafa, H. Mahdin et al., “Mobile ad-hoc network routing protocols of time-critical events for search and rescue missions,” Bulletin of Electrical Engineering and Informatics, vol. 10, no. 1, pp. 192–199 (2021).
[18] S. Ramasamy and R. K. Gnanamurthy, “Cluster based multi layer user authentication data center storage architecture for big data security in cloud computing,” Journal of Internet Technology, vol. 21, no. 1, pp. 159–171 (2020).
[19] I. Odun-Ayo, R. Goddy-Worlu, V. Geteloma, and E. Grant, “A systematic mapping study of cloud, fog, and edge/mobile devices management, hierarchy models and business models,” Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 2, pp. 91–101 (2018).
[20] A. K. Singha, and S. Zubair ,” Combination of Optimization Methods in a Multistage Approach for a Deep Neural Network Model,” International Journal of Information Technology, 1-

Views: 357Downloads: 120Citations: 1