A Novel Algorithm of Safe-Route Traversal of Data for Designing the Secured Smart City Infrastructures

A Novel Algorithm of Safe-Route Traversal of Data for Designing the Secured Smart City Infrastructures
  IJETT-book-cover           
  
© 2023 by IJETT Journal
Volume-71 Issue-5
Year of Publication : 2023
Author : Arpit Chhabra, Niraj Singhal, Syed vilayat Ali Rizvi
DOI : 10.14445/22315381/IJETT-V71I5P229

How to Cite?

Arpit Chhabra, Niraj Singhal, Syed vilayat Ali Rizvi, "A Novel Algorithm of Safe-Route Traversal of Data for Designing the Secured Smart City Infrastructures," International Journal of Engineering Trends and Technology, vol. 71, no. 5, pp. 272-281, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I5P229

Abstract
Here, a special feature-rich safe route data traversal algorithm research model has been developed for use in data transfer preparation tasks. The characteristics include the format for three-dimensional data (memory is only one dimension). However, the method expertly transforms it into a 3-D entity (cube string construction), which is essential for establishing a novel data traversal model that deviates from conventional wisdom and has multiple DRDs. In this paper, a method for decrypting, encrypting data that calls for multiple DRDs. The DRDs include the following: a string containing cube data is called a cube string. Delete List (a list that contains cube string moves), Character Position List (List of all character's initial positions in the cube string, including the plain string) because it requires multiple dependencies to crack or decode the data, these dependencies make it extremely difficult for hackers to predict the nature of data traversal.

Keywords
Security, Learning algorithm, Rubik Cube, Multiple Data-Retrieval Dependencies (DRDs).

References
[1] Yan Lin Aung, Martín Ochoa, and Jianying Zhou, “ATLAS: A Practical Attack Detection and Live Malware Analysis System for IoT Threat Intelligence,” International Conference on Information Security, vol. 13640, pp. 319-338, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Niraj Singhal, and Arpit Chhabra, “A Novel Learning Approach of Adaptive Cyber Defense System for Smart Cities,” Electronic Systems and Intelligent Computing, vol. 860, pp. 465-471, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Jinjin Liang, and Yong Nie, “Application of Machine Learning Algorithms in Analysis of Learners’ Behaviour Data,” SSRG International Journal of Computer Science and Engineering, vol. 6, no. 10, pp. 13-17, 2019.
[CrossRef] [Publisher Link]
[4] Hannu Turtiainen, Andrei Costin, and Timo Hämäläinen, “Defensive Machine Learning Methods and the Cyber Defence Chain,” Artificial Intelligence and Cybersecurity, pp. 147-163, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Deepak A. Vidhate, and Parag Kulkarni, “Single Agent Learning Algorithms for Decision making in Diagnostic Applications,” SSRG International Journal of Computer Science and Engineering , vol. 3, no. 5, pp. 46-52 , 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[6] ZebaNaaz, Kauser Fatima, and C.Atheeq, "Performance Based Comparison Study of RSA and Chaotic Maps in MANET," SSRG International Journal of Electrical and Electronics Engineering, vol. 4, no. 2, pp. 17-22, 2017.
[CrossRef] [Publisher Link]
[7] Manav Bansal, Arpit Chhabra, and Niraj Singhal, "Smart City-Shrewd Vehicle Versatility Utilizing IOT," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 29-36, 2022.
[CrossRef] [Publisher Link]
[8] Arpit Chhabra et al., “A New Cryptographic Algorithm for Safe Route Transversal of Data in Smart Cities using Rubik Cube,” International Journal of Computer Science and Network Security, vol. 22, no. 8, pp. 113–122, 2022.
[CrossRef] [Publisher Link]
[9] Shakti Chourasiya, and Suvrat Jain, "A Study Review on Supervised Machine Learning Algorithms," SSRG International Journal of Computer Science and Engineering , vol. 6, no. 8, pp. 16-20, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[10] D. Rajavel, and S. P. Shantharajah, “Cryptography Based on Combination of Hybridization and Cube’s Rotation,” International Journal of Computational Intelligence and Informatics, vol. 1, no. 4, pp. 294-299, 2012.
[Google Scholar] [Publisher Link]
[11] S.Jagadeesan et al., "High Level Secure Messages Based on Steganography and Cryptography," International Journal of Engineering Trends and Technology, vol. 68, no. 2, pp. 142-145, 2020.
[CrossRef] [Publisher Link]
[12] Dalibor Dobrilović, “Networking Technologies for Smart Cities: An Overview,” Interdisciplinary Description of Complex Systems: INDECS, vol. 16, no. 3-A, pp. 408-416, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Vasiliki Demertzi, Stavros Demertzis, and Konstantinos Demertzis, “An Overview of Cyber Threats, Attacks, and Countermeasures on the Primary Domains of Smart Cities,” Applied Sciences, vol. 13, no. 2, pp. 790, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[14] K. Jino Abisha et al., "Detection of Twitter Spam's using Machine Learning Algorithm," SSRG International Journal of Computer Science and Engineering , vol. 6, no. 3, pp. 10-13, 2019.
[CrossRef] [Publisher Link]
[15] Adel S. Elmaghraby, and Michael M. Losavio, “Cyber security challenges in Smart Cities: Safety, security and privacy,” Journal of Advanced Research, vol. 5, no. 4, pp. 491-497, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[16] J. Jhanavi, and M.Dakshayini, "Blockchain Implementation for Storage,” SSRG International Journal of Mobile Computing and Application, vol. 5, no. 2, pp. 9-12, 2018.
[CrossRef] [Publisher Link]
[17] Carmen Rotună et al., "Smart City Ecosystem Using Blockchain Technology," Informatica Economica, vol. 23, no. 4, pp. 41-50, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Manvi Chahar, and Savita, "Implementation and Classification of Anomalous Detection with Varying Parameters," SSRG International Journal of Computer Science and Engineering, vol. 6, no. 4, pp. 16-18, 2019.
[CrossRef] [Publisher Link]
[19] Elvira Ismagilova et al., “Security, Privacy and Risks within Smart Cities: Literature Review and Development of a Smart City Interaction Framework,” Information Systems Frontiers, vol. 24, no. 2, pp. 393-414, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Nikhat Naaz Aslam Shaikh, and Vaishali Bagade, "Performance Evaluation and Detection of Grey, Warm, Flooding, Misrouting & Modification of Attacks in Vanet," SSRG International Journal of Electronics and Communication Engineering, vol. 8, no. 4, pp. 10-17, 2021.
[CrossRef] [Publisher Link]
[21] Sana Aurangzeb et al., “CyberSecurity for Autonomous Vehicles Against Malware Attacks in Smart-Cities,” Research Square, 2022.
[Google Scholar] [Publisher Link]
[22] Mahdi Khosravy et al., "Social IoT Approach to Cyber Defense of a Deep-Learning-Based Recognition System in Front of Media Clones Generated by Model Inversion Attack," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 5, pp. 2694-2704, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Sara N. Matheu et al., "Federated Cyberattack Detection for Internet of Things-Enabled Smart Cities,” Computer, vol. 55, no. 12, pp. 65-73, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Arpit Chhabra, and Niraj Singhal, “Indicator Based Cyber Threats Detection for Data of Smart Cities Using Bio-Inspired Artificial Algae,” International Journal of Advanced Research in Engineering and Technology (IJARET), vol. 11, no. 11, pp. 1530-1536, 2020.
[CrossRef] [Google Scholar] [Publisher Link]