Abstract
The exponential growth of Internet-of-Things (IoT) has raised several data security risks to the Fog–Cloud architecture. The performance and the computation cost of security algorithms hinder providing a secure real-time environment for IoT. This study proposes a novel two-layer cryptosystem, Cryptographic Harris Hawks Optimization (CryptoHHO), for Fog–Cloud architecture that reduces security overheads while maintaining confidentiality, integrity, and availability. The first layer of the proposed CryptoHHO is responsible for generating a highly randomized key using HHO to optimize Shannon entropy incorporation with transfer functions and a binarization mechanism. The second layer of CryptoHHO introduces a novel encipher model for encryption and decryption based on the Shift cipher, XOR operator, and an instance of crossover and mutation. The job execution avenue, i.e., Fog or cloud computing, is selected depending on the size of IoT requests, security sensitivity, and time sensitivity. The performance of CryptoHHO is compared against other emerging bio-inspired cryptographic algorithms. It was found that the CryptoHHO performs better than CryptoSSA, CryptoGWO, CryptoPSO, and CryptoWOA algorithms based on entropy, key generation time, transfer function comparison, execution time, and throughput. Further, the robustness of CryptoHHO is examined by various security analyses like brute-force attack resistivity, confusion-diffusion, CIA achievement, and statistical evaluations suggested by NIST and FIPS.
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References
Atzori L, Iera A, Morabito G (2010) The internet of things: a survey. Comput Networks 54(15):2787–2805. https://doi.org/10.1016/j.comnet.2010.05.010
Modina N, El Azouzi R, De Pellegrini F, Menasche DS, Figueiredo R (2022) Joint traffic offloading and aging control in 5G IoT networks. IEEE Trans Mob Comput. https://doi.org/10.1109/TMC.2022.3154089
Chettri L, Bera R (2020) A comprehensive survey on internet of things (IoT) toward 5G wireless systems. IEEE Internet Things J 7(1):16–32. https://doi.org/10.1109/JIOT.2019.2948888
Thakor VA, Razzaque MA, Khandaker MRA (2021) Lightweight cryptography algorithms for resource-constrained IoT devices: a review, comparison and research opportunities. IEEE Access 9:28177–28193
Gonzales D, Kaplan JM, Saltzman E, Winkelman Z, Woods D (2017) Cloud-trust: a security assessment model for infrastructure as a service (IaaS) clouds. IEEE Trans Cloud Comput 5(3):523–536. https://doi.org/10.1109/TCC.2015.2415794
Karame GO, Soriente C, Lichota K, Capkun S (2019) Securing cloud data under key exposure. IEEE Trans Cloud Comput 7(3):838–849. https://doi.org/10.1109/TCC.2017.2670559
Ghosh R, Longo F, Frattini F, Russo S, Trivedi KS (2014) Scalable analytics for IaaS cloud availability. IEEE Trans Cloud Comput 2(1):57–70. https://doi.org/10.1109/TCC.2014.2310737
Jawed MS, Sajid M (2022) A comprehensive survey on cloud computing: architecture, tools, technologies, and open issues. Int J Cloud Appl Comput 12(1):1–33. https://doi.org/10.4018/IJCAC.308277
Cai H, Gu Y, Vasilakos AV, Xu B, Zhou J (2018) Model-driven development patterns for mobile services in cloud of things. IEEE Trans Cloud Comput 6(3):771–784. https://doi.org/10.1109/TCC.2016.2526007
Tao F, Cheng Y, Da Xu L, Zhang L, Li BH (2014) CCIoT-CMfg: cloud computing and internet of things-based cloud manufacturing service system. IEEE Trans Ind Inform 10(2):1435–1442. https://doi.org/10.1109/TII.2014.2306383
Botta A, de Donato W, Persico V, Pescapé A (2016) Integration of cloud computing and internet of things: a survey. Future Gener Comput Syst 56:684–700. https://doi.org/10.1016/j.future.2015.09.021
Singh S, Sham EE, Vidyarthi DP (2024) Optimizing workload distribution in Fog–Cloud ecosystem: a JAYA based meta-heuristic for energy-efficient applications. Appl Soft Comput 154:111391. https://doi.org/10.1016/j.asoc.2024.111391
Sharma S, Sajid M (2021) Integrated fog and cloud computing issues and challenges. Int J Cloud Appl Comput 11(4):174–193
Alli AA, Alam MM (2020) The fog cloud of things: A survey on concepts, architecture, standards, tools, and applications. Internet of Things (Netherlands). https://doi.org/10.1016/j.iot.2020.100177
Sicari S, Rizzardi A, Coen-Porisini A (2022) Insights into security and privacy towards fog computing evolution. Comput Secur 120:102822. https://doi.org/10.1016/j.cose.2022.102822
Khalid T et al (2021) A survey on privacy and access control schemes in fog computing. Int J Commun Syst 34(2):e4181. https://doi.org/10.1002/dac.4181
Ficco M, Esposito C, Xiang Y, Palmieri F (2017) Pseudo-dynamic testing of realistic edge-fog cloud ecosystems. IEEE Commun Mag 55(11):98–104. https://doi.org/10.1109/MCOM.2017.1700328
Alam M, Shahid M, Mustajab S (2024) Security challenges for workflow allocation model in cloud computing environment: a comprehensive survey, framework, taxonomy, open issues, and future directions. J Supercomput. https://doi.org/10.1007/s11227-023-05873-1
Tabrizchi H, Kuchaki Rafsanjani M (2020) A survey on security challenges in cloud computing: issues, threats, and solutions. J Supercomput 76(12):9493–9532. https://doi.org/10.1007/s11227-020-03213-1
Bacis E, di Vimercati S, Foresti S, Paraboschi S, Rosa M, Samarati P (2020) Securing resources in decentralized cloud storage. IEEE Trans Inf Forensics Secur 15:286–298. https://doi.org/10.1109/TIFS.2019.2916673
Li J, Zhang Y, Ning J, Huang X, Sen Poh G, Wang D (2022) Attribute based encryption with privacy protection and accountability for CloudIoT. IEEE Trans. Cloud Comput. 10(2):762–773. https://doi.org/10.1109/TCC.2020.2975184
Yang P, Xiong N, Ren J (2020) Data security and privacy protection for cloud storage: a survey. IEEE Access 8:131723–131740
“Scopus Advanced Search.” https://www.scopus.com/term/analyzer.uri?sort=plf-f&src=s&sid=e57e7305d9817f54072c5fff2493ae5d&sot=a&sdt=a&sl=66&s=%28%28TITLE-ABS-KEY%28data+security+and+privacy%29%29+AND+%28cloud+computing%29%29&origin=resultslist&count=10&analyzeResults=Analyze+result. Accessed 16 Feb 2024
Shen W, Qin J, Yu J, Hao R, Hu J, Ma J (2021) Data integrity auditing without private key storage for secure cloud storage. IEEE Trans Cloud Comput 9(4):1408–1421. https://doi.org/10.1109/TCC.2019.2921553
Wazid M, Das AK, Kumar N, Vasilakos AV (2019) Design of secure key management and user authentication scheme for fog computing services. Future Gener Comput Syst 91:475–492. https://doi.org/10.1016/j.future.2018.09.017
Ahsan MM, Gupta KD, Nag AK, Poudyal S, Kouzani AZ, Mahmud MAP (2020) Applications and evaluations of bio-inspired approaches in cloud security: a review. IEEE Access 8:180799–180814. https://doi.org/10.1109/ACCESS.2020.3027841
Fausto F, Reyna-Orta A, Cuevas E, Andrade ÁG, Perez-Cisneros M (2020) From ants to whales: metaheuristics for all tastes. Artif Intell Rev 53(1):753–810. https://doi.org/10.1007/s10462-018-09676-2
Mirjalili S, Dong JS, Lewis A (2019) Nature-inspired optimizers: theories, literature reviews and applications, 1st ed. Springer
Sajid M, Mittal H, Pare S, Prasad M (2022) Routing and scheduling optimization for UAV assisted delivery system: a hybrid approach. Appl Soft Comput. https://doi.org/10.1016/j.asoc.2022.109225
Boussaïd I, Lepagnot J, Siarry P (2013) A survey on optimization metaheuristics. Inf Sci (NY) 237:82–117. https://doi.org/10.1016/j.ins.2013.02.041
Sajid M et al (2021) A novel algorithm for capacitated vehicle routing problem for smart cities. Symmetry (Basel). https://doi.org/10.3390/sym13101923
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82. https://doi.org/10.1109/4235.585893
Sajid M, Raza Z (2016) Energy-aware stochastic scheduling model with precedence constraints on DVFS-enabled processors. Turkish J Electr Eng Comput Sci 24(5):4117–4128. https://doi.org/10.3906/elk-1505-112
Chunka C, Goswami RS, Banerjee S (2019) A novel approach to generate symmetric key in cryptography using genetic algorithm (GA). Adv Intell Syst Comput 755:713–724. https://doi.org/10.1007/978-981-13-1951-8_64
Jawed MS, Sajid M (2023) Enhancing the cryptographic key using sample entropy and whale optimization algorithm. Int J Inf Technol. https://doi.org/10.1007/s41870-023-01526-x
Kunhare N, Tiwari R, Dhar J (2022) Intrusion detection system using hybrid classifiers with meta-heuristic algorithms for the optimization and feature selection by genetic algorithm. Comput Electr Eng. https://doi.org/10.1016/j.compeleceng.2022.108383
Jawed MS, Sajid M (2022) Cryptanalysis of lightweight block ciphers using metaheuristic algorithms in cloud of things (CoT). In: 2022 International Conference on Data Analytics for Business and Industry (ICDABI), pp 165–169. https://doi.org/10.1109/ICDABI56818.2022.10041583
Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Future Gener Comput Syst 97:849–872. https://doi.org/10.1016/j.future.2019.02.028
Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27(3):379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
Mirjalili S, Lewis A (2013) S-shaped versus V-shaped transfer functions for binary particle swarm optimization. Swarm Evol Comput 9:1–14
Lanza-Gutierrez JM, Crawford B, Soto R, Berrios N, Gomez-Pulido JA, Paredes F (2017) Analyzing the effects of binarization techniques when solving the set covering problem through swarm optimization. Expert Syst Appl 70:67–82. https://doi.org/10.1016/j.eswa.2016.10.054
Kumar M et al (2023) A smart privacy preserving framework for industrial IoT using hybrid meta-heuristic algorithm. Sci Rep. https://doi.org/10.1038/s41598-023-32098-2
Tahir M, Sardaraz M, Mehmood Z, Muhammad S (2021) CryptoGA: a cryptosystem based on genetic algorithm for cloud data security. Cluster Comput 24(2):739–752. https://doi.org/10.1007/s10586-020-03157-4
Irshad RR et al (2023) A multi-objective bee foraging learning-based particle swarm optimization algorithm for enhancing the security of healthcare data in cloud system. IEEE Access 11:113410–113421. https://doi.org/10.1109/ACCESS.2023.3265954
Jawed MS, Sajid M (2022) XECryptoGA: a metaheuristic algorithm-based block cipher to enhance the security goals. Evol Syst. https://doi.org/10.1007/s12530-022-09462-0
Balashunmugaraja B, Ganeshbabu TR (2022) Privacy preservation of cloud data in business application enabled by multi-objective red deer-bird swarm algorithm. Knowl-Based Syst. https://doi.org/10.1016/j.knosys.2021.107748
Alroobaea R, Arul R, Rubaiee S, Alharithi FS, Tariq U, Fan X (2022) AI-assisted bio-inspired algorithm for secure IoT communication networks. Cluster Comput 25(3):1805–1816. https://doi.org/10.1007/s10586-021-03520-z
Sun Y, Lin F, Zhang N (2018) A security mechanism based on evolutionary game in fog computing. Saudi J Biol Sci 25(2):237–241. https://doi.org/10.1016/j.sjbs.2017.09.010
Singh S, Vidyarthi DP (2023) An integrated approach of ML-metaheuristics for secure service placement in Fog–Cloud ecosystem. Internet of Things (Netherlands). https://doi.org/10.1016/j.iot.2023.100817
Dubey K, Sharma SC, Kumar M (2022) A secure IoT applications allocation framework for integrated Fog–Cloud environment. J Grid Comput. https://doi.org/10.1007/s10723-021-09591-x
Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191. https://doi.org/10.1016/j.advengsoft.2017.07.002
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95—International Conference on Neural Networks, vol 4, pp 1942–1948. https://doi.org/10.1109/ICNN.1995.488968
Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008
Kumar U (2023) Soil moisture prediction, kaggle.com. https://www.kaggle.com/datasets/uttamkumar15802/soil-moisture-prediction. Accessed 22 May 2023
Arora A, Chakraborty P, Bhatia MPS (2022) Intervention of wearables and smartphones in real time monitoring of sleep and behavioral health: an assessment using adaptive neuro-fuzzy technique. Arab J Sci Eng 47(2):1999–2024. https://doi.org/10.1007/s13369-021-06078-5
Cortez P, Morais A (2007) A data mining approach to predict forest fires using meteorological data. In: Proceedings of 13th Port Conference Artificial Intelligence, pp 512–523, [Online]. Available: http://www.dsi.uminho.pt/~pcortez/fires.pdf
Ananth R (2023) Weather prediction. kaggle.com, 2023. https://www.kaggle.com/datasets/ananthr1/weather-prediction. Accessed 22 May 2023
Rachakonda L, Mohanty SP, Kougianos E (2020) Good-eye: a device for automatic prediction and detection of elderly falls in smart homes. In: Proceedings of 2020 6th IEEE International Symposium Smart Electronic Systems iSES, pp 202–203. https://doi.org/10.1109/iSES50453.2020.00051
Bommela NR (2021) Health monitoring system, kaggle.com, 2021. https://www.kaggle.com/datasets/nraobommela/health-monitoring-system. Accessed 22 May 2023
Competition CP (2022) Smart home temperature, kaggle.com. https://www.kaggle.com/competitions/smart-homes-temperature-time-series-forecasting/data. Accessed 22 May 2023
Kadiwal A (2021) Water quality. kaggle.com. https://www.kaggle.com/datasets/adityakadiwal/water-potability. Accessed 22 May 2023
De Vito S, Fattoruso G, Pardo M, Tortorella F, Di Francia G (2012) Semi-supervised learning techniques in artificial olfaction: a novel approach to classification problems and drift counteraction. IEEE Sens J 12(11):3215–3224. https://doi.org/10.1109/JSEN.2012.2192425
Stolfi DH, Alba E, Yao X (2017) Predicting car park occupancy rates in smart cities. Lecture Notes in Computational Science (including Subser. Lecture Notes Artificial Intelligence Lecture Notes Bioinformatics), vol. 10268 LNCS, pp. 107–117, 2017, https://doi.org/10.1007/978-3-319-59513-9_11
Barker E (2020) Recommendation for key management. National Institute of Standards and Technology. https://doi.org/10.6028/nist.sp.800-57pt1r5
Diffie W, Hellman ME (1977) Special feature exhaustive cryptanalysis of the NBS data encryption standard. Computer (Long Beach, CA) 10(6):74–84. https://doi.org/10.1109/C-M.1977.217750
NIST (2001) Announcing the Advanced Encryption Standard (AES) [electronic resource]. Computer Security Division, Information Technology Laboratory, National Institute of Standards and Technology Gaithersburg, MD
Mahajan A (2014) Burp suite essential. Packt Publishing Limited
Bassham LE, et al (2010) SP 800–22 Rev. 1a. a statistical test suite for random and pseudorandom number generators for cryptographic applications. National Institute of Standards & Technology, Gaithersburg, MD, USA
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MS and MSJ were contributed to conceptualization; MSJ was contributed to methodology, formal analyses, data curation, and original draft preparation; MS was contributed to writing—review and editing, supervision.
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Jawed, M.S., Sajid, M. CryptoHHO: a bio-inspired cryptosystem for data security in Fog–Cloud architecture. J Supercomput 80, 15834–15867 (2024). https://doi.org/10.1007/s11227-024-06055-3
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DOI: https://doi.org/10.1007/s11227-024-06055-3