Highly secure intelligent computer data detection of anomalies

Gupta, Upasana; Singh, Vaishali; Goyal, Dinesh

doi:10.47974/JDMSC-1767

Open Access Research Article

Highly secure intelligent computer data detection of anomalies

^*Upasana GuptaCorresponding authorupasana_gupta31@yahoo.comDepartment of Computer Science & EngineeringLucknow 226020, Uttar PradeshMaharishi University of Information TechnologyIndiaView full profile → , Vaishali Singhsingh.vaishali05@gmail.comDepartment of Computer Science & EngineeringLucknow 226020, Uttar PradeshMaharishi University of Information TechnologyIndiaView full profile → , Dinesh Goyaldinesh.goyal@poornima.orgJaipur, RajasthanPoornima Institute of Engineering and TechnologyIndiaView full profile →

* Corresponding author · click or hover a name for details

pp. 875–884Vol. 26Issue 3April 2023DOI: 10.47974/JDMSC-1767 Crossmark XML

Published Online:: 01 Apr 2023
Article type:: Research Article
Language:: EN
Article no.:: JDMSC-1767
Pages:: 875–884

Abstract

The latest technology is focusing on the detection of different attacks occurring in the cloud and IoT data centers and services. So, there is a need of providing high dimensional security for the data. The appraisal issue prompts the suggestion of a coordinated methodology for sifting the unusual data of interest and perspectives for seeing anticipated exceptions in the new youngster around subspaces of high-layered enormous information. High-layered immense information requires significant computational memory and brings a tremendous computational weight, no matter what the way that continuous figuring sources solid areas are. Thus, the fundamental examination objective of this assessment is to develop an adaptable system for inconsistency recognizing confirmation that arrangements with the issue of the scourge of dimensionality in goliath instructive combinations. The issue of irregularity range has different highlights, and how we depict irregularities, the sort of information, and anticipated surrender can have a critical effect on recognizable confirmation techniques. As a result of these contrasts, issue plans shift enormously, requiring the utilize of different shrewd frameworks to address them.

Keywords

Data stream Virus detection Parameter types Learning types

Subject Classifications

Primary 93A30Secondary 49K15

References

[1] Kumar, S., Srivastava, P. K., Srivastava, Singh, D., & Goyal, D. Chaos based image encryption security in cloud computing. Journal of Discrete Mathematical Sciences and Cryptography, 25(4), 1041-1051 (2022).
[2] Kamble, S., Saini, D. K. J., Verma, S., Tiwari, A., & Goyal, D. Detection and tracking of moving cloud services from video using saliency map model. Journal of Discrete Mathematical Sciences and Cryptography, 25(4), 1083-1092 (2022).
[3] Kumar, S., Dubey, K. K., Gautam, Kumar, V., & Mamodiya, U. Detection of recurring vulnerabilities in computing services. Journal of Discrete Mathematical Sciences and Cryptography, 25(4), 1063-1071 (2022).
[4] Kumar, S., Kumar, S., Ranjan, N., Tiwari & Rafsanjani, M. K. Digital watermarking-based cryptosystem for cloud resource provisioning. International Journal of Cloud Applications and Computing (IJCAC), 12(1), 1-20 (2022).
[5] Aggarwal CC. Managing and mining sensor data. Berlin: Springer Science & Business Media (2013).
[6] Jiang F, Leung CK, Pazdor AG. Big data mining of social networks for friend recommendation. In: Advances in social networks analysis and mining (ASONAM), 2016 IEEE/ACM international conference on. IEEE. pp. 921-2 (2016).
[7] Gartner I. Big data definition. https://www.gartner.com/it-glossary/big-data/. Accessed 14 Feb 2020.
[8] Zhai Y, Ong Y-S, Tsang IW. The emerging “big dimensionality”. IEEE ComputIntell Mag. 9(3) : 14-26 (2014).
[9] Thudumu S, Branch P, Jin J, Singh JJ. Adaptive clustering for outlier identification in high-dimensional data. In: International conference on algorithms and architectures for parallel processing. Springer. pp. 215-28 (2019).
[10] Erfani SM, Rajasegarar S, Karunasekera S, Leckie C. High-dimensional and large-scale anomaly detection using a linear one-class svm with deep learning. Pattern Recogn. 58 : 121-34 (2016).
[11] Hodge V, Austin J. A survey of outlier detection methodologies. ArtifIntell Rev. 22(2) : 85-126 (2004).
[12] Aggarwal CC, Philip SY. An effective and efficient algorithm for high-dimensional outlier detection. VLDB J. 14(2) : 211-21 (2005).
[13] Tiwari, A., & Garg, R. Reservation System for Cloud Computing Resources (RSCC): Immediate Reservation of the Computing Mechanism. International Journal of Cloud Applications and Computing (IJCAC), 12(1), 1-22 (2022).
[14] Tiwari, A., & Garg, R. Orrs Orchestration of a Resource Reservation System Using Fuzzy Theory in High-Performance Computing: Lifeline of the Computing World. International Journal of Software Innovation (IJSI), 10(1), 1-28 (2022).
[15] Agrawal S, Agrawal J. Survey on anomaly detection using data mining techniques. Procedia Comput Sci. 60 : 708-13 (2015).
[16] Akoglu L, Tong H, Koutra D. Graph based anomaly detection and description: a survey. Data Mining Knowl Discov. 29(3) : 626-88 (2015).
[17] Chandola V, Banerjee A, Kumar V. Anomaly detection: a survey. ACM ComputSurv. 41(3) : 15 (2009).
[18] Tiwari, A., & Sharma, R. M. OCC: A Hybrid Multiprocessing Computing Service Decision Making Using Ontology System. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 16(4), 96-116 ((2021)).
[19] Tiwari, A., Sharma, V., & Mahrishi, M. Service adaptive broking mechanism using MROSP algorithm. In Advanced Computing, Networking and Informatics-Volume 2, pp. 383-391 (2014). Springer, Cham.
[20] Tiwari, A., Sharma, R. M., & Garg, R. Emerging ontology formulation of optimized internet of things (IOT) services with cloud computing. In Soft Computing: Theories and Applications, pp. 31-52 (2020). Springer, Singapore.
[21] Patcha A, Park J-M. An overview of anomaly detection techniques: existing solutions and latest technological trends. Comput Netw. 51(12) : 3448-70 (2007).
[22] Jiang M, Cui P, Faloutsos C. Suspicious behavior detection: current trends and future directions. IEEE Intell Syst. 31(1) : 31-9 (2016).
[23] Sorzano COS, Vargas J, Montano AP. A survey of dimensionality reduction techniques. arXiv preprint arXiv:1403.2877, (2014).
[24] Gama J. Knowledge discovery from data streams. London: Chapman and Hall/CRC (2010).
[25] Gupta M, Gao J, Aggarwal CC, Han J. Outlier detection for temporal data: a survey. IEEE Trans Knowl Data Eng. 26(9) : 2250-67 (2014).
[26] Heydari A, ali Tavakoli M, Salim N, Heydari Z. Detection of review spam: a survey. Expert Syst Appl. 42(7) : 3634-42 (2015).
[27] Rangaiah, Y. V., Sharma, A. K., Bhargavi, T., Chopra, M., Mahapatra, C., & Tiwari, A. A Taxonomy towards Blockchain based Multimedia content Security. In 2022 2nd International Conference on Innovative Sustainable Computational Technologies (CISCT), pp. 1-4, (2022, December). IEEE.
[28] Rohinidevi, V. V., Srivastava, P. K., Dubey, N., Tiwari, S., & Tiwari, A. A Taxonomy towards fog computing Resource Allocation. In 2022 2nd International Conference on Innovative Sustainable Computational Technologies (CISCT), pp. 1-5, (2022, December). IEEE.
[29] Singh, N. K., Jain, A., Arya, S., Gonzales, W. E. G., Flores, J. E. A., & Tiwari, A. Attack Detection Taxonomy System in cloud services. In 2022 2nd International Conference on Innovative Sustainable Computational Technologies (CISCT), pp. 1-5, (2022, December). IEEE.
[30] Chouhan, A., Tiwari, A., Diwaker, C., & Sharma, A. Efficient Opportunities and Boundaries towards Internet of Things (IoT) Cost Adaptive Model. In 2022 IEEE Delhi Section Conference (DELCON), pp. 1-5 (2022, February) IEEE.

Views: 359Downloads: 130Citations: 0