An Efficient Cryptanalysis Strategy for Enhancing Security Estimation and Reducing Resource Usage
An Efficient Cryptanalysis Strategy for Enhancing Security Estimation and Reducing Resource Usage |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-7 |
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| Year of Publication : 2025 | ||
| Author : K. Swanthana, S.S. Aravinth | ||
| DOI : 10.14445/22315381/IJETT-V73I7P131 | ||
How to Cite?
K. Swanthana, S.S. Aravinth, "An Efficient Cryptanalysis Strategy for Enhancing Security Estimation and Reducing Resource Usage," International Journal of Engineering Trends and Technology, vol. 73, no. 7, pp.402-422, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I7P131
Abstract
The rapid expansion of blockchain technology has led to increased computational demands, necessitating efficient resource management to ensure optimal performance and security. Traditional resource allocation methods often struggle with high computation costs and inadequate security measures, leading to inefficient use of cloud resources and vulnerability to attacks. This paper presents a novel Chimp-based Optimized Recurrent Diffie-Hellman (CORDH) strategy designed to overcome these challenges by optimizing resource usage in blockchain networks. The proposed CORDH model leverages the Chimp Optimization Algorithm (COA) combined with recurrent frameworks to dynamically allocate computational resources such as Virtual Machines (VMs), CPU, and RAM. This methodology involves training on diverse datasets from healthcare, stock markets, and Network Traffic, followed by applying COA to allocate necessary resources, thus reducing computation costs efficiently. Additionally, the CORDH model enhances system security by implementing robust cryptanalysis strategies to counter brute force and DDoS attacks. Experimental results demonstrate that CORDH significantly improves resource utilization, enhances data security, and outperforms traditional methods in both computational efficiency and resilience to attacks.
Keywords
Cloud Environments, Resource Allocation, Chimp Optimization, Blockchain network, Cryptanalysis, Data security.
References
[1] Moumita Das, Xingyu Tao, and Jack C.P. Cheng, “BIM Security: A Critical Review and Recommendations Using Encryption Strategy and Blockchain,” Automation in Construction, vol. 126, pp. 1-57, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Ehab Zaghloul et al., “Bitcoin and Blockchain: Security and Privacy,” IEEE Internet of Things Journal, vol. 7, no. 10, pp. 10288-10313, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Xiongfei Zhao et al., “Minimizing Block Incentive Volatility through Verkle Tree-Based Dynamic Transaction Storage,” Decision Support Systems, vol. 180, pp. 1-34, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Xin Wang et al., “Blockchain-Enabled Decentralized Edge Intelligence for Trustworthy 6G Consumer Electronics,” IEEE Transactions on Consumer Electronics, vol. 70, no. 1, pp. 1214-1225, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Safak Kayikci, and Taghi M. Khoshgoftaar, “Blockchain Meets Machine Learning: A Survey,” Journal of Big Data, vol. 11, no. 1, pp. 1-29, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Haiao Li, Lina Ge, and Lei Tian, “Survey: Federated Learning Data Security and Privacy-Preserving in Edge-Internet of Things,” Artificial Intelligence Review, vol. 57, no. 5, pp. 1-38, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[7] K. Sasikumar, and Sivakumar Nagarajan, “Comprehensive Review and Analysis of Cryptography Techniques in Cloud Computing,” IEEE Access, vol. 12, pp. 52325-52351, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Muhammad Noman Sohail et al., “Optimizing Industrial IoT Data Security through Blockchain-Enabled Incentive-Driven Game Theoretic Approach for Data Sharing,” IEEE Access, vol. 12, pp. 51176-51192, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Tri Nguyen, Huong Nguyen, and Tuan Nguyen Gia, “Exploring the Integration of Edge Computing and Blockchain IoT: Principles, Architectures, Security, and Applications,” Journal of Network and Computer Applications, vol. 226, pp. 1-24, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Paraskevas Koukaras et al., “Integrating Blockchain in Smart Grids for Enhanced Demand Response: Challenges, Strategies, and Future Directions,” Energies, vol. 17, no. 5, pp. 1-32, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Volodymyr Nakonechnyi et al., “Blockchain Implementation in the Protection System of Banking System during Online Banking Operations,” 2024 35th Conference of Open Innovations Association (FRUCT), Tampere, Finland, pp. 492-500, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Zhili Zhou et al., “Blockchain-Based Secure and Efficient Secret Image Sharing with Outsourcing Computation in Wireless Networks,” IEEE Transactions on Wireless Communications, vol. 23, no. 1, pp. 423-435, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Shams Forruque Ahmed et al., “Insights into Internet of Medical Things (IoMT), Data Fusion, Security Issues and Potential Solutions,” Information Fusion, vol. 102, pp. 1-20, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Jiaxiang Zhang et al., “Adaptive Resource Allocation for Blockchain-Based Federated Learning in Internet of Things,” IEEE Internet of Things Journal, vol. 10, no. 12, pp. 10621-10635, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Basim Aljabhan, and Muath A. Obaidat, “Privacy-Preserving Blockchain Framework for Supply Chain Management: Perceptive Craving Game Search Optimization (PCGSO),” Sustainability, vol. 15, no. 8, pp. 1-23, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Selvarajan Shitharth et al., “Federated Learning Optimization: A Computational Blockchain Process with Offloading Analysis to Enhance Security,” Egyptian Informatics Journal, vol. 24, no. 4, pp. 1-12, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Qingqing Xie, Fan Dong, and Xia Feng, “HLOChain: A Hierarchical Blockchain Framework with Lightweight Consensus and Optimized Storage for IoT,” Security and Communication Networks, vol. 2023, pp. 1-14, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Yedida Venkata Rama Subramanya Viswanadham, and Kayalvizhi Jayavel, “A Framework for Data Privacy Preserving in Supply Chain Management Using Hybrid Meta-Heuristic Algorithm with Ethereum Blockchain Technology,” Electronics, vol. 12, no. 6, pp. 1-29, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Tao Liu, Yi Yuan, and Zhongyang Yu, “An Intelligent Optimization Control Method for Enterprise Cost Under Blockchain Environment,” IEEE Access, vol. 11, pp. 3597-3606, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Basel Halak, Yildiran Yilmaz, and Daniel Shiu, “Comparative Analysis of Energy Costs of Asymmetric vs Symmetric Encryption-Based Security Applications,” IEEE Access, vol. 10, pp. 76707-76719, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Tanweer Alam, Arif Ullah, and Mohamed Benaida, “Deep Reinforcement Learning Approach for Computation Offloading in Blockchain-Enabled Communications Systems,” Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 8, pp. 9959-9972, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[22] P.S. Akshatha, and S.M. Dilip Kumar, “MQTT and Blockchain Sharding: An Approach to User-Controlled Data Access with Improved Security and Efficiency,” Blockchain: Research and Applications, vol. 4, no. 4, pp. 1-13, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Oleksandr Kuznetsov et al., “Enhanced Security and Efficiency in Blockchain with Aggregated Zero-Knowledge Proof Mechanisms,” IEEE Access, vol. 12, pp. 49228-49248, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[24] R. Aiyshwariya Devi, and A.R. Arunachalam, “Enhancement of IoT Device Security Using an Improved Elliptic Curve Cryptography Algorithm and Malware Detection Utilizing Deep LSTM,” High-Confidence Computing, vol. 3, no. 2, pp. 1-14, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Saurabh Singhal et al., “Energy Efficient Resource Allocation in Cloud Environment Using Metaheuristic Algorithm,” IEEE Access, vol. 11, pp. 126135-126146, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Chirag Chandrashekar et al., “MCWOA Scheduler: Modified Chimp-Whale Optimization Algorithm for Task Scheduling in Cloud Computing,” Computers, Materials & Continua, vol. 78, no. 2, pp. 2593-2616, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Zhe Sun et al., “A Data Attack Detection Framework for Cryptography-Based Secure Aggregation Methods in 6G Intelligent Applications,” Electronics, vol. 13, no. 11, pp. 1-22, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Akanksha Goel, and S. Neduncheliyan, “An Intelligent Blockchain Strategy for Decentralized Healthcare Framework,” Peer-to-Peer Networking and Applications, vol. 16, no. 2, pp. 846-857, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Ahmed A.M. Sharadqh et al., “Hybrid Chain: Blockchain Enabled Framework for Bi-Level Intrusion Detection and Graph-Based Mitigation for Security Provisioning in Edge Assisted IOT Environment,” IEEE Access, vol. 11, pp. 27433-27449, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Xuan-Ha Nguyen et al., “Realguard: A Lightweight Network Intrusion Detection System for IoT Gateways,” Sensors, vol. 22, no. 2, pp. 1-18, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[31] A. Mullai, and K. Mani, “Enhancing the Security in RSA and Elliptic Curve Cryptography Based on Addition Chain Using Simplified Swarm Optimization and Particle Swarm Optimization for Mobile Devices,” International Journal of Information Technology, vol. 13, no. 2, pp. 551-564, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Rizk M. Rizk-Allah et al., “On the Cryptanalysis of a Simplified AES Using a Hybrid Binary Grey Wolf Optimization,” Mathematics, vol. 11, no. 18, pp. 1-16, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[33] Abdul Rehman, and Omar Alharbi, “Bioinspired Blockchain Framework for Secure and Scalable Wireless Sensor Network Integration in Fog-Cloud Ecosystems,” Computers, vol. 14, no. 1, pp. 1-16, 2025.
[CrossRef] [Google Scholar] [Publisher Link]