Balkan Journal of Electrical and Computer Engineering

2147-284X 2147-284X

MUSA YILMAZ

10.17694/bajece.1625208

Computer Software

Bilgisayar Yazılımı

Blockchain-Integrated Framework for Data Security: An Application Based on IoT Data and Deep Learning

https://orcid.org/0000-0002-3272-1078

Demirol

Doygun

BINGOL UNIVERSITY

https://orcid.org/0000-0002-6113-4649

Daş

Resul

FIRAT UNIVERSITY

https://orcid.org/0000-0002-2901-2342

Özdem

Mehmet

Türk Telekom

https://orcid.org/0009-0004-3216-9852

Cansel

Ceren Nur

https://orcid.org/0000-0003-2271-7865

Hanbay

Davut

INONU UNIVERSITY

03 30 2025

13 1 27 38 01 27 2025 03 05 2025

2013

Balkan Journal of Electrical and Computer Engineering

The rapid development of the IoT (Internet of Things) ecosystem leads to the creation of big data environments that require real-time analysis. In this comprehensive data ecosystem, anomaly detection and data security emerge as critical requirements. This paper presents a comprehensive approach that integrates a deep learning model developed for anomaly detection in IoT network traffic and a blockchain-based data storage structure designed to ensure data integrity. In the research, network traffic data of a sample device from the N-BaIoT dataset is used. The developed deep learning model was able to classify attack and normal traffic patterns with high accuracy. Data security is ensured with Fernet encryption algorithm, while data integrity is protected using blockchain technology. Experimental results show that the proposed system achieves significant performance metrics in terms of both anomaly detection accuracy and data security verification. The proposed framework contributes to the development of more secure and reliable IoT systems by providing an innovative solution to anomaly detection and data security challenges in IoT environments.

Internet of Things (IoT) Deep Learning Network Security Blockchain Data Integrity Anomaly Detection.

[1] N. M. Adams, “Perspectives on data mining,” International Journal of Market Research, vol. 52, no. 1, 2010.

[2] D. Talia, “Clouds for scalable big data analytics,” Computer, vol. 46, no. 5, pp. 98–101, 2013.

[3] D. Demirol, R. Das, and D. Hanbay, “B¨uy¨uk veri ¨uzerine perspektif bir bakıs¸,” in 2019 International Artificial Intelligence and Data Processing Symposium (IDAP), 2019, pp. 1–9.

[4] A. Abouzeid, K. Bajda-Pawlikowski, D. Abadi, A. Silberschatz, and A. Rasin, “HadoopDB: An architectural hybrid of mapreduce and DBMS technologies for analytical workloads,” Proceedings of the VLDB Endowment, vol. 2, no. 1, pp. 922–933, aug 2009.

[5] V. Subramaniyaswamy, V. Vijayakumar, R. Logesh, and V. Indragandhi, “Unstructured data analysis on big data using map reduce,” Procedia Computer Science, vol. 50, pp. 456–465, 2015.

[6] S. F. Oliveira, K. F¨urlinger, and D. Kranzlm¨uller, “Trends in computation, communication and storage and the consequences for data-intensive science,” in Proceedings of the 14th IEEE International Conference on High Performance Computing and Communications, HPCC-2012 - 9th IEEE International Conference on Embedded Software and Systems, ICESS-2012. IEEE, jun 2012, pp. 572–579.

[7] F. Chang, J. Dean, S. Ghemawat, W. C. Hsieh, D. A. Wallach, M. Burrows, T. Chandra, A. Fikes, and R. E. Gruber, “Bigtable: A distributed storage system for structured data,” ACM Transactions on Computer Systems, vol. 26, no. 2, pp. 1–26, 2008.

[8] L. George, HBase: The Definitive Guide. O’Reilly Media, Inc., 2016.

[9] A. Lakshman and P. Malik, “Cassandra - a decentralized structured storage system,” Operating Systems Review (ACM), vol. 44, pp. 35–40, 2010.

[10] V. Abramova and J. Bernardino, “Nosql databases: Mongodb vs cassandra,” in C3S2E ’13: Proceedings of the International C* Conference on Computer Science and Software Engineering, 2013, pp. 14–22.

[11] M. Armbrust, A. Fox, D. Patterson, N. Lanham, B. Trushkowsky, J. Trutna, and H. Oh, “Scads: Scale-independent storage for social computing applications,” in CIDR 2009 - 4th Biennial Conference on Innovative Data Systems Research, January 2009.

[12] K. Pattnaik and B. Mishra, “Introduction to big data analysis,” in Techniques and Environments for Big Data Analysis, 2016, pp. 1–20.

[13] R. Cattell, “Scalable sql and nosql data stores,” SIGMOD Record, vol. 39, pp. 12–27, May 2010.

[14] S. Sivasubramanian, “Amazon dynamodb: A seamlessly scalable nonrelational database service,” in Proceedings of the 2012 International Conference on Management of Data - SIGMOD ’12, 2012, pp. 729– 730.

[15] U. Vyas and P. Kuppusamy, DynamoDB Applied Design Patterns. Packt Publishing Ltd., 2014.

[16] R. Paul, “An introduction to building realtime apps with rethinkdb,” March 2018, accessed: 2025-01-15. [Online]. Available: https://jaxenter. com/building-realtime-apps-rethinkdb-115254.html

[17] OrientDB, “Orientdb nosql models,” 2021. [Online]. Available: http://orientdb.com/docs/3.0.x/gettingstarted/

[18] B. Iordanov, “Hypergraphdb: A generalized graph database,” in Web- Age Information Management. WAIM 2010 Workshops, ser. 6185 LNCS, 2010, pp. 25–36.

[19] D. Dominguez-Sal, P. Urb´on-Bayes, A. Gim´enez-Va˜n´o, S. G´omez- Villamor, N. Mart´ınez-Baz´an, and J. Larriba-Pey, “Survey of graph database performance on the hpc scalable graph analysis benchmark,” in Web-Age Information Management. WAIM 2010 Workshops, ser. 6185 LNCS, 2010, pp. 37–48.

[20] S. Ghemawat, H. Gobioff, and S. Leung, “The google file system,” SIGOPS Oper. Syst. Rev., vol. 37, pp. 29–43, 2003.

[21] N. Gemayel, “Analyzing google file system and hadoop distributed file system,” Research Journal of Information Technology, vol. 8, pp. 66–74, 2016.

[22] O. Kisi, J. Shiri, S. Karimi, and R. M. Adnan, Big Data in Engineering Applications. Springer Singapore, May 2018, vol. 44.

[23] E. J. Khatib, R. Barco, P. Munoz, I. D. La Bandera, and I. Serrano, “Self-healing in mobile networks with big data,” IEEE Communications Magazine, vol. 54, no. 1, pp. 114–120, jan 2016.

[24] B. Das, “A deep learning model for identification of diabetes type 2 based on nucleotide signals,” Neural Computing and Applications, vol. 22, no. 1, pp. 1–5, 2022.

[25] Q. V. Pham, D. C. Nguyen, T. Huynh-The, W. J. Hwang, and P. N. Pathirana, “Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts,” IEEE Access, vol. 8, pp. 130 820–130 839, 2020.

[26] A. Haleem, M. Javaid, I. H. Khan, and R. Vaishya, “Significant Applications of Big Data in COVID-19 Pandemic,” Indian Journal of Orthopaedics, vol. 54, no. 4, pp. 526–528, jul 2020.

[27] J. H. Lee, R. Phaal, and S. H. Lee, “An integrated service-devicetechnology roadmap for smart city development,” Technological Forecasting and Social Change, vol. 80, no. 2, pp. 286–306, feb 2013.

[28] C. L. Stimmel, Building smart cities: Analytics, ICT, and design thinking. Auerbach Publications, aug 2015.

[29] C. T. Yin, Z. Xiong, H. Chen, J. Y. Wang, D. Cooper, and B. David, “A literature survey on smart cities,” Science China Information Sciences, vol. 58, no. 10, pp. 1–18, oct 2015.

[30] S. R., P. (2015, aug) 8 innovative examples of big data usage in india. [Online]. Available: https://www.dqindia.com/ 8-innovative-examples-of-big-data-usage-in-india/

[31] M. Assefi, E. Behravesh, G. Liu, and A. P. Tafti, “Big data machine learning using apache spark MLlib,” in Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017, vol. 2018- January. IEEE, dec 2017, pp. 3492–3498.

[32] A. Oussous, F. Z. Benjelloun, A. Ait Lahcen, and S. Belfkih, “Big Data technologies: A survey,” Journal of King Saud University - Computer and Information Sciences, vol. 30, no. 4, pp. 431–448, oct 2018.

[33] W. Fang, X. Z. Wen, Y. Zheng, and M. Zhou, “A Survey of Big Data Security and Privacy Preserving,” IETE Technical Review (Institution of Electronics and Telecommunication Engineers, India), vol. 34, no. 5, pp. 544–560, sep 2017.

[34] P. Samarati and L. Sweeney, “Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression,” Technical Report, SRI International Computer Science Laboratory, 1 1998.

[35] A. Machanavajjhala, D. Kifer, J. Gehrke, and M. Venkitasubramaniam, “l-diversity: Privacy beyond k-anonymity,” ACM Transactions on Knowledge Discovery from Data, vol. 1, no. 1, p. 3, mar 2007.

[36] L. Ninghui, L. Tiancheng, and S. Venkatasubramanian, “t-Closeness: Privacy beyond k-anonymity and l-diversity,” in Proceedings - International Conference on Data Engineering. IEEE, apr 2007, pp. 106–115.

[37] H. Y. Tran and J. Hu, “Privacy-preserving big data analytics a comprehensive survey,” Journal of Parallel and Distributed Computing, vol. 134, pp. 207–218, dec 2019.

[38] C. Dwork, “Differential privacy,” in Lecture Notes in Computer Science, 2006, vol. 4052 LNCS, pp. 1–12.

[39] C. C. Aggarwal and P. S. Yu, A General Survey of Privacy-Preserving Data Mining Models and Algorithms. Boston, MA: Springer US, 2008, pp. 11–52.

[40] P. Ram Mohan Rao, S. Murali Krishna, and A. P. Siva Kumar, “Privacy preservation techniques in big data analytics: a survey,” Journal of Big Data, vol. 5, no. 1, 2018.

[41] M. A. Chamikara, P. Bertok, D. Liu, S. Camtepe, and I. Khalil, “Efficient data perturbation for privacy preserving and accurate data stream mining,” Pervasive and Mobile Computing, vol. 48, pp. 1–19, aug 2018.

[42] K. Chen and L. Liu, “A random rotation perturbation approach to privacy preserving data classification,” International Conference on Data Mining, 2005.

[43] Chen, Keke and Liu, Ling, “Geometric data perturbation for privacy preserving outsourced data mining,” Knowledge and Information Systems, vol. 29, no. 3, pp. 657–695, dec 2011.

[44] M. Z. G¨und¨uz, D. Demirol, R. Das¸, and K. Hanbay, “Frameworks for smart grid cyber security analysis,” in Cyber Security Solutions for Protecting and Building the Future Smart Grid, D. Asija, R. Viral, R. Das¸, and G. Tuna, Eds. Elsevier, 2025, pp. 191–214.

[45] S. Venkatraman and R. Venkatraman, “Big data security challenges and strategies,” AIMS Mathematics, vol. 4, no. 3, pp. 860–879, 2019.

[46] M. Zhao E and Y. Geng, “Homomorphic Encryption Technology for Cloud Computing,” Procedia Computer Science, vol. 154, pp. 73–83, 2019.

[47] A. Sahai and B. Waters, “Fuzzy identity-based encryption,” in Advances in Cryptology – EUROCRYPT 2005, R. Cramer, Ed. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005, pp. 457–473.

[48] J. Bethencourt, A. Sahai, and B. Waters, “Ciphertext-Policy Attribute- Based Encryption,” in 2007 IEEE Symposium on Security and Privacy (SP ’07). IEEE, may 2007, pp. 321–334.

[49] T. Bouabana-Tebibel and A. Kaci, “Parallel search over encrypted data under attribute based encryption on the Cloud Computing,” Computers & Security, vol. 54, pp. 77–91, oct 2015.

[50] M. Ali, J. Mohajeri, M.-R. Sadeghi, and X. Liu, “A fully distributed hierarchical attribute-based encryption scheme,” Theoretical Computer Science, vol. 815, pp. 25–46, may 2020.

[51] M. Z. Gunduz and R. Das, “Cyber-security on smart grid: Threats and potential solutions,” Computer Networks, vol. 169, p. 107094, 2020.

[52] H. Satılmıs¸ and S. Akleylek, “Iot g¨uvenli˘gi ˙Ic¸in kullanılan makine ¨O ˘grenimi ve derin ¨Og˘renme modelleri U¨ zerine bir derleme,” Bilis¸im Teknolojileri Dergisi, vol. 14, no. 4, p. 457–481, 2021.

[53] A. G¨okdemr and A. C¸ alhan, “Nesnelerin interneti ortamlarında derin o¨g˘renme ve makine o¨g˘renmesi tabanlı anomali tespiti,” Gazi U¨ niversitesi M¨uhendislik Mimarlık Fak¨ultesi Dergisi, vol. 37, no. 4, p. 1945–1956, 2022.

[54] L. E. Dai, X. Wang, and S. B. Xu, “A deep learning based anomaly detection model for iot networks,” in Proceedings of the 2nd International Conference on Internet of Things, Communication and Intelligent Technology, J. Dong, L. Zhang, and D. Cheng, Eds. Singapore: Springer Nature Singapore, 2024, pp. 187–196.

[55] Y. Mirsky, T. Golomb, and Y. Elovici, “Lightweight collaborative anomaly detection for the iot using blockchain,” Journal of Parallel and Distributed Computing, vol. 145, pp. 75–97, 2020.

[56] T. Golomb, Y. Mirsky, and Y. Elovici, “Ciota: Collaborative iot anomaly detection via blockchain,” CoRR, vol. abs/1803.03807, 2018. [Online]. Available: http://arxiv.org/abs/1803.03807

[57] A. Diro, N. Chilamkurti, V.-D. Nguyen, and W. Heyne, “A comprehensive study of anomaly detection schemes in iot networks using machine learning algorithms,” Sensors, vol. 21, no. 24, 2021.

[58] M. Emec¸ and M. H. O¨ zcanhan, “A hybrid deep learning approach for intrusion detection in iot networks,” Advances in Electrical and Computer Engineering, vol. 22, no. 1, pp. 3–12, 2022.

[59] D. Li, W. Peng, W. Deng, and F. Gai, “A blockchain-based authentication and security mechanism for iot,” in 2018 27th International Conference on Computer Communication and Networks (ICCCN), 2018, pp. 1–6.

[60] T. Xu, Z. Fu, M. Yu, J. Wang, H. Liu, and T. Qiu, “Blockchain based data protection framework for iot in untrusted storage,” in 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 2021, pp. 813–818.

[61] G. F. T¨urker and K. Tanyeri, “Blokzincir teknolojisi ile nesnelerin ˙Interneti tabanlı (iot) sistemlerin veri g¨uvenli˘ginin sa˘glanması,” Gazi U¨ niv. Fen Bilim. Derg. C Tasar. ve Teknol., May 2023.

[62] S. Almarri and A. Aljughaiman, “Blockchain technology for iot security and trust: A comprehensive slr,” Sustainability, vol. 16, no. 23, 2024. [Online]. Available: https://www.mdpi.com/2071-1050/16/23/10177

[63] E. U. Haque, A. Shah, J. Iqbal, S. S. Ullah, R. Alroobaea, and S. Hussain, “A scalable blockchain based framework for efficient IoT data management using lightweight consensus,” Sci. Rep., vol. 14, no. 1, p. 7841, Apr. 2024.

[64] A. M. Ruzbahani, “Ai-protected blockchain-based iot environments: Harnessing the future of network security and privacy,” 2024. [Online]. Available: https://arxiv.org/abs/2405.13847