Blockchain for Cloud Security: Reducing Vulnerabilities to Phishing Attacks

By: Soo Nee Kee^1,2

¹Universiti Malaya, Kuala Lumpur, Malaysia.

²International Center for AI and Cyber Security Research and Innovations, Asia University, Taiwa,n Email: nee.kee2001.nks@gmail.com

Abstract

Cloud computing is the delivery of computing services, including servers, storage, software and applications. Cloud computing enables users to access and utilize computing resources without owning a premise physically, providing scalability, flexibility and cost savings solutions. By using cloud computing, users can deploy their own websites or applications, conduct testing, analyze data, and collaborate with others in real-time. However, it also introduces vulnerabilities to phishing attacks. Therefore, an architecture that integrates blockchain technology and cloud computing is proposed to enhance cloud security.

Keywords: Blockchain, Cloud, Phishing, LSTM, FCN, BP, Deep Learning

Introduction

Blockchain is a decentralized storage that distributes data into blocks and each block connects previous blocks using cryptographic chain, making it not possible to tamper data. In addition, it offers traceability, immutability and transparency to the system, which can avoid malicious changes, guarantees the correctness of the data, allow users to observe and maintain the entire view of system and foster user trust. [1] There are various types of blockchain, including off-chain and blockchain-as-a-service. Off-chain is used to handle enormous amounts of data while tokens, reputations, and incentives can all be managed using blockchain-as-a-service. [1]

Techniques

Integration of blockchain with cloud computing can effectively detect phishing attacks due to its traceability and immutability. The system will check vulnerabilities before storing the safe contents into cloud. When cloud users upload data to the cloud, the system will extract the URLs and check with the whitelist. Whitelist is a dataset of Alexa’s most popular website URLs. If the URLs are in the whitelist means that the contents are safe and without any phishing vulnerabilities. By using whitelist can increase the efficiency of the system. After that, Long Short-Term Memory (LSTM), Fully Convolutional Network (FCN) and Back Propagation (BP) are used to feature extraction. [2] LSTM and FCN process transaction features while BP process state features and account features. Concatenation will be conducted to aggregate those three features. The features will then be passed into neural networks to conduct classification. Once phishing is detected, a block that contains malicious URLs will be created. Once the block passes through the mining process, it will be added to the blockchain. The mined block becomes visible to all users to alert users to be more aware of the phishing vulnerabilities. [3] The safe data will be encrypted by using attribute-based encryption with a set of access policies set by the data owner. This allows authorized users who possess match attributes to gain access to the data. [4] This further enhances the security of the cloud system. The encrypted data will then be stored in the cloud.

Conclusion

In conclusion, blockchain technology with neural networks can enhance the security of cloud computing. The use of whitelist has significantly improved efficiency. Neural networks such as LSTM, FCN and BP are used for effective features extraction and URLs classification. By leveraging the capabilities, the model can identify malicious and phishing URLs with greater accuracy. Simultaneously, blockchain technology provides traceability and immutability storage that can significantly enhance security of cloud computing by alerting users with the phishing contents.

Reference

1. “Exploring the integration of edge computing and blockchain IoT: Principles, architectures, security, and applications – ScienceDirect.” Accessed: Oct. 04, 2024. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1084804524000614
2. T. Wen, Y. Xiao, A. Wang, and H. Wang, “A novel hybrid feature fusion model for detecting phishing scam on Ethereum using deep neural network,” Expert Syst. Appl., vol. 211, p. 118463, Jan. 2023, doi: 10.1016/j.eswa.2022.118463.
3. R. Karthika, C. Valliyammai, and M. Naveena, “Phish Block: A Blockchain Framework for Phish Detection in Cloud,” Jun. 2022, doi: 10.32604/csse.2023.024086.
4. “A Secure Data Sharing Platform Using Blockchain and Interplanetary File System.” Accessed: Oct. 04, 2024. [Online]. Available: https://www.mdpi.com/2071-1050/11/24/7054
5. Keesari, T., Goyal, M. K., Gupta, B., Kumar, N., Roy, A., Sinha, U. K., … & Goyal, R. K. (2021). Big data and environmental sustainability based integrated framework for isotope hydrology applications in India. Environmental Technology & Innovation, 24, 101889.
6. A. Gaurav, B. B. Gupta, C. -H. Hsu, D. Peraković and F. J. García Peñalvo, “Deep Learning Based Approach for Secure Web of Things (WoT),” 2021 IEEE International Conference on Communications Workshops (ICC Workshops), Montreal, QC, Canada, 2021, pp. 1-6, doi: 10.1109/ICCWorkshops50388.2021.9473677.

Cite As

Kee S.N. (2024) Blockchain for Cloud Security: Reducing Vulnerabilities to Phishing Attacks, Insights2Techinfo, pp.1

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