Large-scale Android malware detection by integrating Blockchain and IPFS for secure virus signature distribution
DOI:
https://doi.org/10.54654/isj.v1i24.1085Keywords:
Virus, Signature, blockchain, Android malware, IPFSTóm tắt
The growing threat of Android malware underscores the limitations of centralized antivirus systems, which face challenges such as latency, single points of failure, and susceptibility to attacks. To address these issues, this paper introduces a decentralized framework leveraging blockchain technology via Hyperledger Fabric and the InterPlanetary File System (IPFS). The system, HypatiaX, provides secure, efficient, and transparent virus signature distribution while ensuring scalable and resilient data storage. By utilizing blockchain for virus signature management and IPFS for decentralized storage, HypatiaX supports real-time updates in distributed environment. Performance evaluations reveal low resource consumption, near-instantaneous query responses, and efficient virus scanning under diverse conditions. Advanced components, including a ledger controller, signature crawler, key manager, and IPFS client, further strengthen decentralized storage, secure key management, and automatic signature updates. This framework demonstrates significant improvements in combating Android malware while addressing the inherent flaws of traditional antivirus solutions.
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. Huang, H.-S., Chang, T.-S., & Wu, J.-Y. (2020). A secure file sharing system based on IPFS and blockchain. Proceedings of the 2nd International Electronics Communication Conference, 96–100.
. Marhane, K., Taif, F., & Namir, A. (2023). Secure sharing of university data using Hyperledger Fabric and IPFS system. Procedia Computer Science, 224, 163–168. Elsevier.
. Milazzo, A. M., Schiatti, L., Giordano, G., & Viale, E. (2018). Antivirus signature distribution with distributed ledger. US Patent 10,063,572, Google Patents.
. Alsaiary, N. M., & Ahmed, S. (2024). Application of blockchain technology in securing mobile applications. AIP Conference Proceedings, 3072(1). AIP Publishing.
. Gupta, S., Thakur, P., Biswas, K., Kumar, S., & Singh, A. P. (2021). Toward a novel decentralized multi-malware detection engine based on blockchain technology. Emerging Technologies in Data Mining and Information Security: Proceedings of IEMIS 2020, Volume 2, 811–819. Springer.
. Fuji, R., Usuzaki, S., Aburada, K., Yamaba, H., Katayama, T., Park, M., Shiratori, N., & Okazaki, N. (2019). Investigation on sharing signatures of suspected malware files using blockchain technology. International Multi Conference of Engineers and Computer Scientists (IMECS), 94–99.
. Abdul Rahman, S. H., Nevin Gabriel, C., Haw, S. C., & Zainuddin, A. A. (2023). Blockchain malware detection tool based on signature technique. Advances in Artificial Intelligence and Machine Learning, 3(4), 1654–1670. Shimur Publications.
. Robert, P., Senkamalavalli, R., Vedanarayanan, V., & Manivannan, D. (2023). Blockchain-based malware detection system for smartphone applications. 2023 8th International Conference on Communication and Electronics Systems (ICCES), 216–221. IEEE.
. Boobalan, P., Keerthana, R., Nandhini, K., & Vignesh, P. (2020). Multi feature detection and signature sharing of Android malware using blockchain. IIRJET, 5(3).
. Kwefati, A. (2021). HuntChain Project: A blockchain-based malware detection tool.
. Hu, Q., Asghar, M. R., & Zeadally, S. (2021). Blockchain-based public ecosystem for auditing security of software applications. Computing, 103(11), 2643–2665. Springer.
. Khellaf, R., & Boudouda, S. (2024). Enhancing mobile enterprise security: A blockchain and agent paradigm-based approach for continuous protection and rapid adaptation. IEEE Access. IEEE.
. Rohith, C., & Kaur, G. (2021). A comprehensive study on malware detection and prevention techniques used by anti-virus. 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM), 429–434. IEEE.
. Lee, D. G. (2021). A study on malicious code detection using blockchain and deep learning. KIPS Transactions on Computer and Communication Systems, 10(2), 39–46. Korea Information Processing Society.
. Denysiuk, D., Geidarova, O., Kapustian, M., Lysenko, S., & Sachenko, A. (2023). Blockchain-based deep learning algorithm for detecting malware. IntelITSIS, 529–538.
. Kumar, R., Wang, W., Kumar, J., Yang, T., & Ali, W. (2021). Collective intelligence: Decentralized learning for Android malware detection in IoT with blockchain. arXiv preprint arXiv:2102.13376.
. Martin, G., Spencer, D., Hair, A., K, D., Laudanna, S., P, V., & Visaggio, C. A. (2022). Mobile malware detection using consortium blockchain. Artificial Intelligence for Cybersecurity, 137–160. Springer.
. Gu, J., Sun, B., Du, X., Wang, J., Zhuang, Y., & Wang, Z. (2018). Consortium blockchain-based malware detection in mobile devices. IEEE Access, 6, 12118–12128. IEEE.
. Sheela, S., Shalini, S., Harsha, D., Chandrashekar, V. T., & Goyal, A. (2023). Decentralized malware attacks detection using blockchain. ITM Web of Conferences, 53, 03002. EDP Sciences.
. Cui, Y., Sun, Y., Lin, Z., Ma, B., & Li, Y. (2023). Potentially unwanted app detection for blockchain-based Android app marketplace. IEEE Internet of Things Journal, 10(24), 21154–21167. IEEE.
. Gupta, S., Thakur, P., Biswas, K., Kumar, S., & Singh, A. P. (2021). Developing a blockchain-based and distributed database-oriented multi-malware detection engine. Machine Intelligence and Big Data Analytics for Cybersecurity Applications, 249–275. Springer.
. Wressnegger, C., Freeman, K., Yamaguchi, F., & Rieck, K. (2017). Automatically inferring malware signatures for anti-virus assisted attacks. Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security, 587–598.
. Senanayake, J., Kalutarage, H., Petrovski, A., Piras, L., & Al-Kadri, M. O. (2024). Defendroid: Real-time Android code vulnerability detection via blockchain federated neural network with XAI. Journal of Information Security and Applications, 82, 103741. Elsevier.
. Park, J. H., Singh, S. K., Salim, M. M., Azzaoui, A. E., & Park, J. H. (2022). Ransomware-based cyber attacks: A comprehensive survey. Journal of Internet Technology, 23(7), 1557–1564.
. Kalphana, K. R., Aanjankumar, S., Surya, M., Ramadevi, M. S., Ramela, K. R., Anitha, T., Nagaprasad, N., & Krishnaraj, R. (2024). Prediction of android ransomware with deep learning model using hybrid cryptography. Scientific Reports, 14(1), 22351. Nature Publishing Group UK London.
. Hyperledger. (2024). Hyperledger Fabric. Retrieved from [https://github.com/hyperledger/fabric] (Accessed: Sep 10, 2024).
. Cisco Talos. (2024). ClamAV. Retrieved from [https://github.com/Cisco-Talos/clamav] (Accessed: Sep 10, 2024).
. Bhatia, A. (2020). Collection of android malware samples. Retrieved from [https://github.com/ashishb/android-malware] (Accessed: Oct 20, 2024).
. Divested Computing Group. (2024). Hypatia. Retrieved from [https://f-droid.org/en/packages/us.spotco.malwarescanner/] (Accessed: Sep 10, 2024).
. Tuan, H. M., Hai, T. H. ., & Thu, P. H. (2023). A new study for global dynamics and numerical simulation of a discrete-time computer virus propagation model. Journal of Science and Technology on Information Security, 3(20), 35-42. https://doi.org/10.54654/isj.v3i20.982
. Toan, N. N. ., Dung, L. T., & Thang, D. Q. (2022). Static Feature Selection for IoT Malware Detection. Journal of Science and Technology on Information Security, 1(15), 74-84. https://doi.org/10.54654/isj.v1i15.844.
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