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Research Articles

Intelligent Chain: A Safer Decision-Making Framework with Blockchain-Based Incentives

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References

  • Adel, K., Elhakeem, A., & Marzouk, M. (2022). Decentralizing construction AI applications using blockchain technology. Expert Systems with Applications, 194, 116548. https://doi.org/10.1016/j.eswa.2022.116548
  • AlAsqah, M., Moulahi, T., Zidi, S., & Alabdulatif, A. (2022). Leveraging Artificial Intelligence in blockchain-based E-health for safer decision making framework. ” Review. Advance online publication. https://doi.org/10.21203/rs.3.rs-1379110/v1
  • Alhajjar, E., Maxwell, P., & Bastian, N. (2021). Adversarial machine learning in network intrusion detection systems. Expert Systems with Applications, 186, 115782. https://doi.org/10.1016/j.eswa.2021.115782
  • Alrawais, A., Alhothaily, A., Hu, C., & Cheng, X. (2017). Fog computing for the internet of things: Security and privacy issues. IEEE Internet Computing, 21(2), 34–42. https://doi.org/10.1109/MIC.2017.37
  • Ayub, M. A., Johnson, W. A., Talbert, D. A., & Siraj, A. (2020). Model evasion attack on intrusion detection systems using adversarial machine learning [Paper presentation]. 2020 54th Annual Conference on Information Sciences and Systems (CISS) (pp. 1–6). https://doi.org/10.1109/CISS48834.2020.1570617116
  • Balani, N., Chavan, P., & Ghonghe, M. (2022). Design of high-speed blockchain-based sidechaining peer to peer communication protocol over 5G networks. Multimedia Tools and Applications, 81(25), 36699–36713. https://doi.org/10.1007/s11042-021-11604-6
  • Biggio, B., Nelson, B., & Laskov, P. Poisoning attacks against support vector machines. arXiv, Mar. 25, 2013. https://doi.org/10.48550/arXiv.1206.6389
  • Booij, T. M., Chiscop, I., Meeuwissen, E., Moustafa, N., & den Hartog, F. T. H. (2022). ToN_IoT: The role of heterogeneity and the need for standardization of features and attack types in IoT network intrusion data sets. IEEE Internet of Things Journal, 9(1), 485–496. https://doi.org/10.1109/JIOT.2021.3085194
  • Burdekin, M. S., & Lewis, M. R. Aviation Degree Program at the Australian Defence Force Academy.
  • Chang, Y., Fang, C., & Sun, W. (2021). A blockchain-based federated learning method for smart healthcare. Computational Intelligence and Neuroscience, 2021, 4376418. https://doi.org/10.1155/2021/4376418
  • Cristianini, N., & Scholkopf, B. (2002). Support vector machines and kernel methods: The new generation of learning machines. AI Magazine, 23(3), 31. https://doi.org/10.1609/aimag.v23i3.1655
  • Demetrio, L., Biggio, B., Lagorio, G., Roli, F., & Armando, A. (2019). Explaining vulnerabilities of deep learning to adversarial malware binaries. arXiv, January 24. http://arxiv.org/abs/1901.03583
  • Deshpande, K., Girkar, J., & Mangrulkar, R. (2023). Security enhancement and analysis of images using a novel Sudoku-based encryption algorithm. Journal of Information and Telecommunication, 7(3), 270–303. https://doi.org/10.1080/24751839.2023.2183802
  • Goel, A., Agarwal, A., Vatsa, M., Singh, R., & Ratha, N. (2019). DeepRing: protecting deep neural network with blockchain [Paper presentation]. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (pp. 2821–2828). https://doi.org/10.1109/CVPRW.2019.00341
  • Goriss-Hunter, A., Echter, A., Oseni, T., & Firmin, S. (2018). Undoing’ gender: How the School of Science, Engineering and Information Technology (SEIT) Women’s Group works across university and community lines to promote inclusive STEMM. Andragoška Spoznanja, 24(3), 57–72. https://doi.org/10.4312/as.24.3.57-72
  • He, D., & Zeadally, S. (2015). An analysis of RFID authentication schemes for internet of things in healthcare environment using elliptic curve cryptography. IEEE Internet of Things Journal, 2(1), 72–83. https://doi.org/10.1109/JIOT.2014.2360121
  • Homayoun, S., Dehghantanha, A., Parizi, R. M., & Choo, K.-K. R. (2019, May). A blockchain-based framework for detecting malicious mobile applications in app stores [Paper presentation]. 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) (pp. 1–4). https://doi.org/10.1109/CCECE.2019.8861782
  • Hong, S., Chandrasekaran, V., Kaya, Y., Dumitraş, T., & Papernot, N. (2020). On the effectiveness of mitigating data poisoning attacks with gradient shaping. arXiv, February 27. https://doi.org/10.48550/arXiv.2002.11497
  • Hu, B., Tang, W., & Xie, Q. (2022). A two-factor security authentication scheme for wireless sensor networks in IoT environments. Neurocomputing, 500, 741–749. https://doi.org/10.1016/j.neucom.2022.05.099
  • Ikram, M., Beaume, P., & Kaafar, M. (2022, August). DaDiDroid: An obfuscation resilient tool for detecting android malware via weighted directed call graph modelling [Paper presentation]. Presented at the 16th International Conference on Security and Cryptography (pp. 211–219). https://www.scitepress.org/Link.aspx?doi=10.5220/0007834602110219 https://doi.org/10.5220/0007834602110219
  • Kammoun, M., Elleuchi, M., Abid, M., & BenSaleh, M. S. (2020). FPGA-based implementation of the SHA-256 hash algorithm [Paper presentation]. 2020 IEEE International Conference on Design & Test of Integrated Micro & Nano-Systems (DTS) (pp. 1–6). https://doi.org/10.1109/DTS48731.2020.9196134
  • Kapoor, V., Abraham, V. S., & Singh, R. (2008). Elliptic curve cryptography. Ubiquity, 2008(May), 1–8. https://doi.org/10.1145/1386853.1378356
  • Khandelwal, P., Johari, R., Gaur, V., & Vashisth, D. (2021). Blockchain technology based smart contract agreement on REMIX IDE [Paper presentation]. 2021 8th International Conference on Signal Processing and Integrated Networks (SPIN) (pp. 938–942). https://doi.org/10.1109/SPIN52536.2021.9565983
  • Liu, F., Fan, H.-Y., & Qi, J.-Y. (2022). Blockchain technology, cryptocurrency: Entropy-based perspective. Entropy, 24(4), 557. https://doi.org/10.3390/e24040557
  • Mhatre, M., Kashid, H., Jain, T., & Chavan, P. (2022). BCPIS: Blockchain-based counterfeit product identification system. Journal of Applied Security Research, 18(4), 740–765. https://doi.org/10.1080/19361610.2022.2086784
  • Mohanta, B. K., Jena, D., Satapathy, U., & Patnaik, S. (2020). Survey on IoT security: Challenges and solution using machine learning, artificial intelligence and blockchain technology. Internet of Things, 11, 100227. https://doi.org/10.1016/j.iot.2020.100227
  • Mondal, K. K., & Guha Roy, D. (2021). IoT data security with machine learning blockchain: Risks and countermeasures. In A. Makkar & N. Kumar (Eds.), Deep learning for security and privacy preservation in IoT, Signals and Communication Technology (pp. 49–81). Springer. https://doi.org/10.1007/978-981-16-6186-0_3
  • Muzammal, M., Qu, Q., & Nasrulin, B. (2019). Renovating blockchain with distributed databases: An open source system. Future Generation Computer Systems, 90, 105–117. https://doi.org/10.1016/j.future.2018.07.042
  • Özkan, K., Işık, Ş., & Kartal, Y. (2018, March). Evaluation of convolutional neural network features for malware detection [Paper presentation]. 2018 6th International Symposium on Digital Forensic and Security (ISDFS) (pp. 1–5). https://doi.org/10.1109/ISDFS.2018.8355390
  • Pampattiwar, K. N., & Chavan, P. V. (2023). CBSOACH: design of an efficient consortium blockchain-based selective ownership and access control model with vulnerability resistance using hybrid decision engine. International Journal of Computational Science and Engineering, 26(2), 129. https://doi.org/10.1504/IJCSE.2023.129744
  • Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z. B., & Swami, A. (2016, March). The limitations of deep learning in adversarial settings [Paper presentation]. 2016 IEEE European Symposium on Security and Privacy (EuroS&P) (pp. 372–387). https://doi.org/10.1109/EuroSP.2016.36
  • Patel, C. (2022). IoT privacy preservation using blockchain. Information Security Journal: A Global Perspective, 31(5), 566–581. https://doi.org/10.1080/19393555.2021.1919795
  • Pawlicki, M., Choraś, M., & Kozik, R. (2020). Defending network intrusion detection systems against adversarial evasion attacks. Future Generation Computer Systems, 110, 148–154. https://doi.org/10.1016/j.future.2020.04.013
  • Rahman, A., Hossain, M. S., Alrajeh, N. A., & Alsolami, F. (2021). Adversarial examples—Security threats to COVID-19 deep learning systems in medical IoT devices. IEEE Internet of Things Journal, 8(12), 9603–9610. https://doi.org/10.1109/JIOT.2020.3013710
  • Sheng, Z., Mahapatra, C., Zhu, C., & Leung, V. C. M. (2015). Recent advances in industrial wireless sensor networks toward efficient management in IoT. IEEE Access, 3, 622–637. https://doi.org/10.1109/ACCESS.2015.2435000
  • Singh, J., Wazid, M., Das, A. K., Chamola, V., & Guizani, M. (2022). Machine learning security attacks and defense approaches for emerging cyber physical applications: A comprehensive survey. Computer Communications, 192, 316–331. https://doi.org/10.1016/j.comcom.2022.06.012
  • Singh, S. K., Rathore, S., & Park, J. H. (2020). BlockIoTIntelligence: A blockchain-enabled intelligent IoT architecture with artificial intelligence. Future Generation Computer Systems, 110, 721–743. https://doi.org/10.1016/j.future.2019.09.002
  • Singh, S., Sharma, P. K., Yoon, B., Shojafar, M., Cho, G. H., & Ra, I.-H. (2020). Convergence of blockchain and artificial intelligence in IoT network for the sustainable smart city. Sustainable Cities and Society, 63, 102364. https://doi.org/10.1016/j.scs.2020.102364
  • Wan, J., Tang, S., Shu, Z., Li, D., Wang, S., Imran, M., & Vasilakos, A. (2016). Software-defined industrial internet of things in the context of industry 4.0. IEEE Sensors Journal, 16(20), 1. https://doi.org/10.1109/JSEN.2016.2565621
  • Weerasinghe, S., Alpcan, T., Erfani, S. M., & Leckie, C. (2021). Defending support vector machines against data poisoning attacks. IEEE Transactions on Information Forensics and Security, 16, 2566–2578. https://doi.org/10.1109/TIFS.2021.3058771
  • Weng, J., Weng, J., Zhang, J., Li, M., Zhang, Y., & Luo, W. (2019). DeepChain: Auditable and privacy-preserving deep learning with blockchain-based incentive. IEEE Transactions on Dependable and Secure Computing, 18(5), 1. https://doi.org/10.1109/TDSC.2019.2952332
  • Yuan, Y., & Wang, F.-Y. (2018). Blockchain and cryptocurrencies: Model, techniques, and applications. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(9), 1421–1428. https://doi.org/10.1109/TSMC.2018.2854904
  • Zhu, M., Yu, H., Liu, Z., Shen, B., Jiang, L., & Cai, H. (2022). An intelligent collaboration framework of IoT applications based on event logic graph. Future Generation Computer Systems, 137, 31–41. https://doi.org/10.1016/j.future.2022.06.017

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