Enhancing data security in cloud computing: a blockchain-based Feistel cipher encryption and multiclass vector side-channel attack detection approach
Ramakrishna Subbareddy and P. Tamil Selvan
Abstract
Cloud computing (CC) environments offer cost-efficient and flexible resources, appealing to users despite concerns about the reliability of cloud service providers (CSPs) and data privacy. To address these concerns, encrypting data before outsourcing to the CC environment is essential. However, encryption introduces challenges such as data leakage through side-channels in virtual machines (VMs). To address these issues, a Feistel cipher symmetric encryption with multiclass vector (FCSE-MV) side-channel attack detection method was developed, leveraging blockchain technology in CC. Initially, an aggregated Byzantine fault tolerance-based block generation model was employed for efficient block production. Subsequently, the presence or absence of side-channel attacks was determined using an FCSE-MV-based block validation model. Experiments conducted with the SCAAML database in JAVA demonstrated that FCSE-MV improved accuracy and throughput by 17.5% and 19%, respectively, and reduced communication complexity and attack detection time by 24% and 21%, compared to traditional attack detection methods. The proposed FCSE-MV method offers a secure and efficient solution suitable for CC environments.
Keyword
Virtual machine, Block generation, Block validation, Byzantine fault tolerance, Feistel deterministic cipher, Symmetric encryption, Multiclass vector.
Cite this article
Subbareddy R, Selvan PT.Enhancing data security in cloud computing: a blockchain-based Feistel cipher encryption and multiclass vector side-channel attack detection approach. International Journal of Advanced Technology and Engineering Exploration. 2024;11(112):354-372. DOI:10.19101/IJATEE.2023.10101898
Refference
[1]Jimale MA, Zaba MR, Kiah ML, Idris MY, Jamil N, Mohamad MS, et al. Parallel sponge-based authenticated encryption with side-channel protection and adversary-invisible nonces. IEEE Access. 2022; 10:50819-38.
[2]Jin S, Bettati R. Efficient side-channel attacks beyond divide-and-conquer strategy. Computer Networks. 2021; 198:108409.
[3]Chen Z, Wu A, Li Y, Xing Q, Geng S. Blockchain-enabled public key encryption with multi-keyword search in cloud computing. Security and Communication Networks. 2021; 2021:1-11.
[4]Zhang X, Su Y, Qin J. A dynamic searchable symmetric encryption scheme for multiuser with forward and backward security. Security and Communication Networks. 2020; 2020:1-13.
[5]Yan X, Yuan X, Ye Q, Tang Y. Blockchain-based searchable encryption scheme with fair payment. IEEE Access. 2020; 8:109687-706.
[6]Ma M, Shi G, Li F. Privacy-oriented blockchain-based distributed key management architecture for hierarchical access control in the IoT scenario. IEEE Access. 2019; 7:34045-59.
[7]Tran SD, Seok B, Lee C. HANMRE-an authenticated encryption secure against side-channel attacks for nonce-misuse and lightweight approaches. Applied Soft Computing. 2020; 97:106663.
[8]Amitha M, Srivenkatesh M. DDoS attack detection in cloud computing using deep learning algorithms. International Journal of Intelligent Systems and Applications in Engineering. 2023; 11(4):82-90.
[9]Salam MI, Yau WC, Chin JJ, Heng SH, Ling HC, Phan RC, et al. Implementation of searchable symmetric encryption for privacy-preserving keyword search on cloud storage. Human-centric Computing and Information Sciences. 2015; 5:1-6.
[10]Shen J, Deng X, Xu Z. Multi-security-level cloud storage system based on improved proxy re-encryption. EURASIP Journal on Wireless Communications and Networking. 2019; 2019(1):1-12.
[11]Sreelatha G, Babu AV, Midhunchakkarvarthy D. A survey on cloud attack detection using machine learning techniques. International Journal of Computer Applications. 2020; 975:21-7.
[12]Tyagi M, Manoria M, Mishra B. Survey and analysis for achieving the security of data in cloud. International Journal of Applied Engineering Research. 2019; 14(20):3954-9.
[13]Agrawal N, Tapaswi S. Defense mechanisms against DDoS attacks in a cloud computing environment: state-of-the-art and research challenges. IEEE Communications Surveys & Tutorials. 2019; 21(4):3769-95.
[14]Shang Y. Prevention and detection of DDOS attack in virtual cloud computing environment using naive bayes algorithm of machine learning. Measurement: Sensors. 2024; 31:100991.
[15]Goy G, Loiseau A, Gaborit P. A new key recovery side-channel attack on HQC with chosen ciphertext. In international conference on post-quantum cryptography 2022 (pp. 353-71). Cham: Springer International Publishing.
[16]Spreitzer R, Moonsamy V, Korak T, Mangard S. Systematic classification of side-channel attacks: a case study for mobile devices. IEEE Communications Surveys & Tutorials. 2017; 20(1):465-88.
[17]Pasha MJ, Rao KP, Mallareddy A, Bande V. LRDADF: an AI enabled framework for detecting low-rate DDoS attacks in cloud computing environments. Measurement: Sensors. 2023; 28:100828.
[18]Wang W, Du X, Shan D, Qin R, Wang N. Cloud intrusion detection method based on stacked contractive auto-encoder and support vector machine. IEEE Transactions on Cloud Computing. 2020; 10(3):1634-46.
[19]Karabulut E, Aysu A. Falcon down: breaking falcon post-quantum signature scheme through side-channel attacks. In 58th ACM/IEEE design automation conference 2021 (pp. 691-6). IEEE.
[20]Olanrewaju RF, Khan BU, Kiah ML, Abdullah NA, Goh KW. Decentralized blockchain network for resisting side-channel attacks in mobility-based IoT. Electronics. 2022; 11(23):1-22.
[21]Ramachandran D, Albathan M, Hussain A, Abbas Q. Enhancing cloud-based security: a novel approach for efficient cyber-threat detection using GSCSO-IHNN model. Systems. 2023; 11(10):1-30.
[22]Ramzan M, Shoaib M, Altaf A, Arshad S, Iqbal F, Castilla ÁK, et al. Distributed denial of service attack detection in network traffic using deep learning algorithm. Sensors. 2023; 23(20):1-24.
[23]Aliyu AA, Liu J. Blockchain-based smart farm security framework for the internet of things. Sensors. 2023; 23(18):1-13.
[24]Assiri FY, Ragab M. Optimal deep-learning-based cyberattack detection in a blockchain-assisted IoT environment. Mathematics. 2023; 11(19):1-16.
[25]Attou H, Mohy-eddine M, Guezzaz A, Benkirane S, Azrour M, Alabdultif A, et al. Towards an intelligent intrusion detection system to detect malicious activities in cloud computing. Applied Sciences. 2023; 13(17):1-19.
[26]Mostafa AM, Ezz M, Elbashir MK, Alruily M, Hamouda E, Alsarhani M, et al. Strengthening cloud security: an innovative multi-factor multi-layer authentication framework for cloud user authentication. Applied Sciences. 2023; 13(19):1-24.
[27]Singh A, Mushtaq Z, Abosaq HA, Mursal SN, Irfan M, Nowakowski G. Enhancing ransomware attack detection using transfer learning and deep learning ensemble models on cloud-encrypted data. Electronics. 2023; 12(18):1-31.
[28]Wang Y, Zheng W, Liu Z, Wang J, Shi H, Gu M, et al. A federated network intrusion detection system with multi-branch network and vertical blocking aggregation. Electronics. 2023; 12(19):1-14.
[29]Shah K, Jadav NK, Tanwar S, Singh A, Pleșcan C, Alqahtani F, et al. AI and blockchain-assisted secure data-exchange framework for smart home systems. Mathematics. 2023; 11(19):1-23.
[30]Bahaa A, Sayed A, Elfangary L, Fahmy H. A novel hybrid optimization enabled robust CNN algorithm for an IoT network intrusion detection approach. Plos one. 2022; 17(12):e0278493.
[31]Li A, Yi S. Intelligent intrusion detection method of industrial internet of things based on CNN-BiLSTM. Security and Communication Networks. 2024; 2024:1-9.
[32]Aldweesh A, Derhab A, Emam AZ. Deep learning approaches for anomaly-based intrusion detection systems: a survey, taxonomy, and open issues. Knowledge-Based Systems. 2020; 189:105124.
[33]Alkadi O, Moustafa N, Turnbull B, Choo KK. A deep blockchain framework-enabled collaborative intrusion detection for protecting IoT and cloud networks. IEEE Internet of Things Journal. 2020; 8(12):9463-72.
[34]Bagui S, Wang X, Bagui S. Machine learning based intrusion detection for IoT botnet. International Journal of Machine Learning and Computing. 2021; 11(6):399-406.
[35]Da CKA, Papa JP, Lisboa CO, Munoz R, De AVH. Internet of things: a survey on machine learning-based intrusion detection approaches. Computer Networks. 2019; 151:147-57.
[36]Atul DJ, Kamalraj R, Ramesh G, Sankaran KS, Sharma S, Khasim S. A machine learning based IoT for providing an intrusion detection system for security. Microprocess. Microsystems. 2021; 82:103741.
[37]Vargas H, Lozano-garzon C, Montoya GA, Donoso Y. Detection of security attacks in industrial IoT networks: a blockchain and machine learning approach. Electronics. 2021; 10(21):1-18.
[38]Awotunde JB, Chakraborty C, Adeniyi AE. Intrusion detection in industrial internet of things network-based on deep learning model with rule-based feature selection. Wireless Communications and Mobile Computing. 2021; 2021:1-7.
[39]Jothi B, Pushpalatha M. WILS-TRS—a novel optimized deep learning based intrusion detection framework for IoT networks. Personal and Ubiquitous Computing. 2023; 27(3):1285-301.
[40]Qureshi KN, Rana SS, Ahmed A, Jeon G. A novel and secure attacks detection framework for smart cities industrial internet of things. Sustainable Cities and Society. 2020; 61:102343.
[41]Kasongo SM, Sun Y. Performance analysis of intrusion detection systems using a feature selection method on the UNSW-NB15 dataset. Journal of Big Data. 2020; 7(1):105.
[42]Otoum Y, Liu D, Nayak A. DL‐IDS: a deep learning–based intrusion detection framework for securing IoT. Transactions on Emerging Telecommunications Technologies. 2022; 33(3):e3803.
[43]Kumar V, Das AK, Sinha D. UIDS: a unified intrusion detection system for IoT environment. Evolutionary Intelligence. 2021; 14(1):47-59.
[44]Latif S, Idrees Z, Zou Z, Ahmad J. DRaNN: a deep random neural network model for intrusion detection in industrial IoT. In international conference on UK-China emerging technologies 2020 (pp. 1-4). IEEE.
[45]Long J, Liang W, Li KC, Wei Y, Marino MD. A regularized cross-layer ladder network for intrusion detection in industrial internet of things. IEEE Transactions on Industrial Informatics. 2022; 19(2):1747-55.
[46]Zolanvari M, Teixeira MA, Gupta L, Khan KM, Jain R. Machine learning-based network vulnerability analysis of industrial internet of things. IEEE Internet of Things Journal. 2019; 6(4):6822-34.
[47]Abdel-basset M, Chang V, Hawash H, Chakrabortty RK, Ryan M. Deep-IFS: intrusion detection approach for industrial internet of things traffic in fog environment. IEEE Transactions on Industrial Informatics. 2020; 17(11):7704-15.
[48]Elrawy MF, Awad AI, Hamed HF. Intrusion detection systems for IoT-based smart environments: a survey. Journal of Cloud Computing. 2018; 7(1):1-20.
[49]Mudassir M, Unal D, Hammoudeh M, Azzedin F. Detection of botnet attacks against industrial IoT systems by multilayer deep learning approaches. Wireless Communications and Mobile Computing. 2022; 2022:1-12.
[50]Vishwakarma M, Kesswani N. DIDS: a deep neural network based real-time intrusion detection system for IoT. Decision Analytics Journal. 2022; 5:100142.
[51]Tang X, Guo C, Choo KK, Liu Y, Li L. A secure and trustworthy medical record sharing scheme based on searchable encryption and blockchain. Computer Networks. 2021; 200:108540.
[52]Shukla D, Chakrabarti S, Sharma A. Blockchain-based cyber-security enhancement of cyber–physical power system through symmetric encryption mechanism. International Journal of Electrical Power & Energy Systems. 2024; 155:109631.
[53]Premkumar R, Priya SS. Service constraint NCBQ trust orient secure transmission with IoT devices for improved data security in cloud using blockchain. Measurement: Sensors. 2022; 24:100486.
[54]Prasad SN, Rekha C. Block chain based IAS protocol to enhance security and privacy in cloud computing. Measurement: Sensors. 2023; 28:100813.
[55]Ragu G, Ramamoorthy S. A blockchain-based cloud forensics architecture for privacy leakage prediction with cloud. Healthcare Analytics. 2023; 4:100220.
[56]Tu S, Yu H, Badshah A, Waqas M, Halim Z, Ahmad I. Secure internet of vehicles (IoV) with decentralized consensus blockchain mechanism. IEEE Transactions on Vehicular Technology. 2023; 72(9):11227-36.
[57]Yang X, Chen A, Wang Z, Li S. Cloud storage data access control scheme based on blockchain and attribute-based encryption. Security and Communication Networks. 2022; 1-12.
[58]Gao H, Luo S, Ma Z, Yan X, Xu Y. BFR-SE: a blockchain-based fair and reliable searchable encryption scheme for IoT with fine-grained access control in cloud environment. Wireless Communications and Mobile Computing. 2021; 2021:1-21.
[59]Yu C, Yang W, Xie F, He J. Technology and security analysis of cryptocurrency based on blockchain. Complexity. 2022:1-15.
[60]Kim J, Nakashima M, Fan W, Wuthier S, Zhou X, Kim I, et al. A machine learning approach to anomaly detection based on traffic monitoring for secure blockchain networking. IEEE Transactions on Network and Service Management. 2022; 19(3):3619-32.