Abstract
Cloud computing (CC) has become one of the prominent technologies because of the significant utility services, which focus on outsourcing data to companies and individual clients. Intrusion Detection Systems (IDS) can be considered an effective solution to achieve security in the cloud computing environment. Blockchain and intrusion detection can be integrated to accomplish security and privacy in the cloud infrastructure. This research develops a new fuzzy deep neural network (FDNN) with Honey Bader Algorithm (HBA) for privacy-preserving intrusion detection technique, named FDNN-HBAID for cloud environment. The presented FDNN-HBAID system is based on the design of an intrusion detection approach with a blockchain-enabled privacy-preserving scheme. An effective training strategy with the FDNN model is applied for intrusion detection and classification. Moreover, FDNN-HBAID provides maximal-security resistance to alleviate zero-day vulnerability and guarantees integrity throughout the nodes and data confidentiality and authentication. In addition, the training process of the FDNN model is carried out using the HBA for optimal adjustment of the hyperparameters. Besides, the privacy-preserving blockchain and intelligent contract model is designed using the Ethereum library to offer privacy to the distributed IDS engine. The experimental validation on benchmark datasets revealed that the FDNN-HBAID approach had shown the potential to achieve security and privacy in the cloud infrastructure.
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References
Riaz S, Khan AH, Haroon M, Latif S, Bhatti S (2020) Big data security and privacy: current challenges and future research perspective in cloud environment. In: 2020 international conference on information management and technology (ICIMTech). IEEE, pp 977–982
Sgaglione L, Coppolino L, D'Antonio S, Mazzeo G, Romano L, Cotroneo D, Scognamiglio A (2019) Privacy-preserving intrusion detection via homomorphic encryption. In: 2019 IEEE 28th international conference on enabling technologies: infrastructure for collaborative enterprises (WETICE). IEEE, pp 321–326
Shamshirband S, Fathi M, Chronopoulos AT, Montieri A, Palumbo F, Pescapè A (2020) Computational intelligence intrusion detection techniques in mobile cloud computing environments: review, taxonomy, and open research issues. J Inf Secur Appl 55:102582
Goyal R, Manoov R, Sevugan P, Swarnalatha P (2020) Securing the data in cloud environment using parallel and multistage security mechanism. In: Soft computing for problem solving. Springer, Singapore, pp 941–949
Almogren AS (2020) Intrusion detection in Edge-of-Things computing. J Parallel Distrib Comput 137:259–265
Jisna P, Jarin T, Praveen PN (2021) Advanced intrusion detection using deep learning-LSTM network on cloud environment. In: 2021 fourth international conference on microelectronics, signals & systems (ICMSS). IEEE, pp 1–6
Lee SW, Mohammadi M, Rashidi S, Rahmani AM, Masdari M, Hosseinzadeh M (2021) Towards secure intrusion detection systems using deep learning techniques: comprehensive analysis and review. J Netw Comput Appl 187:103111
Fatani A, Dahou A, Al-Qaness MA, Lu S, AbdElaziz M (2022) advanced feature extraction and selection approach using deep learning and aquila optimizer for IoT intrusion detection system. Sensors 22(1):140
Liu C, Gu Z, Wang J (2021) A hybrid intrusion detection system based on scalable K-means+ random forest and deep learning. IEEE Access 9:75729–75740
Thakkar A, Lohiya R (2021) A review on machine learning and deep learning perspectives of IDS for IoT: recent updates, security issues, and challenges. Arch Comput Methods Eng 28(4):3211–3243
Chiba Z, Abghour N, Moussaid K, El Omri A, Rida M (2019) New anomaly network intrusion detection system in cloud environment based on optimised back propagation neural network using improved genetic algorithm. Int J Commun Netw Inf Secur 11(1):61–84
Ghosh P, Biswas S, Shakti S, Phadikar S (2020) An improved intrusion detection system to preserve security in cloud environment. Int J Inf Secur Privacy (IJISP) 14(1):67–80
Balamurugan V, Saravanan R (2019) Enhanced intrusion detection and prevention system on cloud environment using hybrid classification and OTS generation. Clust Comput 22(6):13027–13039
Alkadi O, Moustafa N, Turnbull B (2020) A collaborative intrusion detection system using deep blockchain framework for securing cloud networks. In: Proceedings of SAI intelligent systems conference. Springer, Cham, pp 553–565
Manickam M, Ramaraj N, Chellappan C (2019) A combined PFCM and recurrent neural network-based intrusion detection system for cloud environment. Int J Bus Intell Data Min 14(4):504–527
Thirumalairaj A, Jeyakarthic M (2020) Hybrid cuckoo search optimization based tuning scheme for deep neural network for intrusion detection systems in cloud environment. J Res Lepidoptera 51(2):209–224
Chiba Z, Abghour N, Moussaid K, Rida M (2019) Intelligent approach to build a Deep Neural Network based IDS for cloud environment using a combination of machine learning algorithms. Comput Secur 86:291–317
Alazab M, Soman KP, Poornachandran P, Al-Nemrat A, Venkatraman S (2019) Deep learning approach for intelligent intrusion detection system. IEEE Access 7:41525–41550. https://doi.org/10.1109/ACCESS.2019.2895334
Lansky J et al (2021) Deep learning-based intrusion detection systems: a systematic review. IEEE Access 9:101574–101599. https://doi.org/10.1109/ACCESS.2021.3097247
Saheed YK, Abiodun AI, Misra S, Holocene MK, Colomo-Palacios R (2022) A machine learning-based intrusion detection for detecting internet of things network attacks. Alex Eng J 61(12):9395–9409. https://doi.org/10.1016/j.aej.2022.02.063
Ahmad Z, Khan AS, Shiang CW, Abdullah J, Ahmad F (2020) Network intrusion detection system: a systematic study of machine learning and deep learning approaches. Trans Emerg Telecommun Technol. https://doi.org/10.1002/ett.4150
Weihua Xu, Kehua Y, Wentao Li, Weiping D (2022) An emerging fuzzy feature selection method using composite entropy-based uncertainty measure and data distribution. IEEE Trans Emerg Topics Comput Intell. https://doi.org/10.1109/TETCI.2022.3171784
Weihua Xu, Wentao Li (2016) Granular computing approach to two-way learning based on formal concept analysis in fuzzy datasets. IEEE Trans Cybern 46(2):366–379
Wentao Li, Zhou Haoxiang Xu, Weihua WX, Witold P (2022) Interval dominance-based feature selection for interval-valued ordered data. IEEE Trans Neural Netw Learn Syst (Early Access). https://doi.org/10.1109/TNNLS,3184120
Weihua Xu, Yuan YK, Wentao Li (2022) Dynamic updating approximations of local generalised multigranulation neighborhood rough set. Appl Intell 52(8):9148–9173
Li W, Xu W, Zhang X, Zhang J (2022) Updating approximations with dynamic objects based on local multigranulation rough sets in ordered information systems. Artif Intell Rev 55(3):1821–1855
Rahman MA, Asyhari AT, Wen OW, Ajra H, Ahmed Y, Anwar F (2021) Effective combining of feature selection techniques for machine learning-enabled IoT intrusion detection. Multimed Tools Appl 80(20):31381–31399
Bhardwaj A, Mangat V, Vig R (2020) Hyperband Tuned deep neural network with well posed stacked sparse AutoEncoder for detection of DDoS attacks in cloud. IEEE Access 8:181916–181929
Deng Y, Ren Z, Kong Y, Bao F, Dai Q (2016) A hierarchical fused fuzzy deep neural network for data classification. IEEE Trans Fuzzy Syst 25(4):1006–1012
Hashim FA, Houssein EH, Hussain K, Mabrouk MS, Al-Albany W (2022) Honey Badger Algorithm: new metaheuristic algorithm for solving optimisation problems. Math Comput Simul 192:84–110
Alkadi O, Moustafa N, Turnbull B, Choo KKR (2020) A deep blockchain framework-enabled collaborative intrusion detection for protecting IoT and cloud networks. IEEE Internet Things J 8(12):9463–9472
Rene Beulah J, Prathiba L, Murthy GLN, FantinIrudaya Raj E, Arulkumar N (2022) Blockchain with deep learning-enabled secure healthcare data transmission and diagnostic model. Int J Model Simul Sci Comput. https://doi.org/10.1142/S1793962322410069
Bhukya RR, Hardas BM, Ch T et al (2022) An automated word embedding with parameter tuned model for web crawling. Intell Autom Soft Comput 32(3):1617–1632
Mayuri AVR, NileshShelke GLN (2022) An efficient low complexity compression based optimal homomorphic encryption for secure fiber optic communication. Optik 252:168545. https://doi.org/10.1016/j.ijleo.2021.168545
SH Parikh, AG Sandesara, C Bhatt (2022) Network intrusion detection using linear and ensemble ml modeling. In: Implementing data analytics and architectures for next generation wireless communications, pp 27–50. https://doi.org/10.4018/978-1-7998-6988-7.ch003
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Jain, D.K., Ding, W. & Kotecha, K. Training fuzzy deep neural network with honey badger algorithm for intrusion detection in cloud environment. Int. J. Mach. Learn. & Cyber. 14, 2221–2237 (2023). https://doi.org/10.1007/s13042-022-01758-6
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DOI: https://doi.org/10.1007/s13042-022-01758-6