Ftdho Zfnet: Block chain based Fractional Tasmanian Devil Harris optimization enabled deep learning using attack detection and mitigation
Vol 7, Issue 1, 2024
VIEWS - 179 (Abstract)
Abstract
Block chain technology is regarded for enhancing the characteristics of security because of decentralized design, safe distributed storage, and privacy. However, in recent times the present situation of block chain technology has experienced some crisis that may delay the quick acceptance and utilization in real-time applications. To conquer this subdues, a blockchain based system for attack detection and mitigation with Deep Learning (DL) named Fractional Tasmanian Devil Harris Optimization_Zeiler and Fergus network (FTDHO_ZFNet) is introduced. In this investigation, the entities utilized are owner, block chain, server, trusted authority and user. Here, authentication phase is done by means of Ethereum block chain by Key Exchange module and privacy preserved data sharing and communication is also done. Then, recorded log file creation is executed by the below mentioned stages. At first, a log file is generated with the basis of communication to record the events. After wards, the features are extracted by BoT-IoT database. Then, feature fusion is done by overlap coefficient utilizing Deep Q-Network (DQN). Moreover, data augmentation (DA) is doneusing bootstrapping method. At last, attack detection is observed by ZFNet tuned by FTDHO. Here, FTDHO is unified by Fractional Tasmanian Devil Optimization (FTDO) and Harris Hawks Optimization (HHO). Additionally, FTDO is integrated by Fractional Calculus (FC) concept and Tasmanian devil optimization (TDO). Furthermore, attack mitigation is performed. The performance measures applied for FTDHO_ZFNet are accuracy, and True Negative rate (TNR), observed supreme values with 92.9%, 93.8% and 92.9%.
Keywords
Full Text:
PDFReferences
1. Sanda O, Pavlidis M, Seraj S, et al. Long-Range attack detection on permissionless blockchains using Deep Learning. Expert Systems with Applications. 2023; 218: 119606. doi: 10.1016/j.eswa.2023.119606
2. Dai Q, Zhang B, Dong S. Eclipse attack detection for BC network layer based on deep feature extraction. Wireless Communications and Mobile Computing; 2022.
3. Jia B, Liang Y. Anti-D chain: A lightweight DDoS attack detection scheme based on heterogeneous ensemble learning in blockchain. China Communications. 2020; 17(9): 11–24. doi: 10.23919/jcc.2020.09.002
4. Jiang S, Yang L, Gao X, et al. BSD-Guard: A Collaborative Blockchain-Based Approach for Detection and Mitigation of SDN-Targeted DDoS Attacks. Chen Y, ed. Security and Communication Networks. 2022; 2022: 1–16. doi: 10.1155/2022/1608689
5. Sivaganesan DD. A data driven trust mechanism based on blockchain in IoT sensor networks for detection and mitigation of attacks. Journal of Trends in Computer Science and Smart Technology. 2021; 3(1): 59–69.
6. Albakri A, Alabdullah B, Alhayan F. Blockchain-Assisted Machine Learning with Hybrid Metaheuristics-Empowered Cyber Attack Detection and Classification Model. Sustainability. 2023; 15(18): 13887.
7. Javed M, Tariq N, Ashraf M, et al. Securing Smart Healthcare Cyber-Physical Systems against Blackhole and Greyhole Attacks Using a Blockchain-Enabled Gini Index Framework. Sensors. 2023; 23: 9372.
8. Jones CB, Kingsley DJ. Decentralized Blockchain With Convolutional Neural Network Model For Security Attack Mitigation”, ICTACT Journal on Communication Technology. 2023; 14(1).
9. Lian Z, Zeng Q, Su C. Privacy-preserving Blockchain-based Global Data Sharing for Federated Learning with Non-IID Data. 2022 IEEE 42nd International Conference on Distributed Computing Systems Workshops (ICDCSW). Published online July 2022. doi: 10.1109/icdcsw56584.2022.00044.
10. Xia Q, Sifah E, Smahi A, et al. BBDS: Blockchain-Based Data Sharing for Electronic Medical Records in Cloud Environments. Information. 2017; 8(2): 44. doi: 10.3390/info8020044
11. Sasaki H, Horiuchi T, Kato S. A study on vision-based mobile robot learning by deep Q-network. 2017 56th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE). Published online September 2017. doi: 10.23919/sice.2017.8105597
12. Jin B, Yang J, Huang X, et al. Deep deformable Q-Network. Proceedings of the International Conference on Web Intelligence. Published online August 23, 2017. doi: 10.1145/3106426.3109426
13. Brownlee J. A Gentle Introduction to the Bootstrap Method. Available online: https://machinelearningmastery.com/a-gentle-introduction-to-the-bootstrap-method/ (accessed on 12 January 2023).
14. Satapathy SC, Joshi A. Information and Communication Technology for Intelligent Systems (ICTIS 2017)—Volume 2. Springer International Publishing; 2018. doi: 10.1007/978-3-319-63645-0
15. Dehghani M, Hubalovsky S, Trojovsky P. Tasmanian Devil Optimization: A New Bio-Inspired Optimization Algorithm for Solving Optimization Algorithm. IEEE Access. 2022; 10: 19599–19620. doi: 10.1109/access.2022.3151641
16. Bhaladhare PR, Jinwala DC. A Clustering Approach for the l-Diversity Model in Privacy Preserving Data Mining Using Fractional Calculus-Bacterial Foraging Optimization Algorithm. Advances in Computer Engineering. 2014; 2014: 1–12. doi: 10.1155/2014/396529
17. Heidari AA, Mirjalili S, Faris H, et al. Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems. 2019; 97: 849–872. doi: 10.1016/j.future.2019.02.028
18. Venkata Rao R. Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations. Published online 2016: 19–34. doi: 10.5267/j.ijiec.2015.8.004
19. Abdollahzadeh B, Soleimanian Gharehchopogh F, Mirjalili S. Artificial gorilla troops optimizer: A new nature‐inspired metaheuristic algorithm for global optimization problems. International Journal of Intelligent Systems. 2021; 36(10): 5887–5958. doi: 10.1002/int.22535
20. Reddy S, Shyam GK. A machine learning based attack detection and mitigation using a secure SaaS framework. Journal of King Saud University—Computer and Information Sciences. 2022; 34(7): 4047–4061. doi: 10.1016/j.jksuci.2020.10.005
21. Wu Y, Wei D, Feng J. Network Attacks Detection Methods Based on Deep Learning Techniques: A Survey. Security and Communication Networks. 2020; 2020: 1–17. doi: 10.1155/2020/8872923
22. Hassanzadeh A, Stoleru R, Chen J. Efficient flooding in Wireless Sensor Networks secured with neighborhood keys. 2011 IEEE 7th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). Published online October 2011. doi: 10.1109/wimob.2011.6085415
23. Anita. N, Vijayalakshmi. M. Blockchain Security Attack: A Brief Survey. 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT). Published online July 2019. doi: 10.1109/icccnt45670.2019.8944615
24. Anderson L, Holz R, Ponomarev A, et al. New kids on the block: an analysis of modern blockchains. Available online: https://arxiv.org/abs/1606.06530 (accessed on 21 January 2022).
25. BOT-IOT dataset. Available online: https://ieee-dataport.org/documents/bot-iot-dataset (accessed on 16 October 2019).
DOI: https://doi.org/10.24294/csma.v7i1.5224
Refbacks
- There are currently no refbacks.
License URL: https://creativecommons.org/licenses/by/4.0/
This site is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.