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De-centralized information flow control for cloud virtual machines with hybrid AES-ECC and improved meta-heuristic optimization based optimal key generation

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Abstract

Cloud computing is now used by many enterprises due to its increased computational efficiency, economic effectiveness, as well as flexibility. However, security is currently the main issue impeding the cloud computing platform's growth. Therefore, Decentralized Information Flow Control (DIFC) has been proposed as a suitable remedy for resolving the cloud security problems. Using conventional network access and encryption technology was not practicable in the DIFC to effectively restrict the spread of the tenant's personal data inside the system. Therefore, a novel DIFC framework for cloud virtual machines (VM) is suggested here. The suggested system encapsulates four entities such as central authority (CA), encryption proxy (EP), cloud server (CS), and cloud tenant VM. The EP has implemented the ciphertext data-flow security technique. Encryption is carried out using the newly proposed hybrid “Advanced Encryption Standard (AES)–Elliptic Curve Cryptography (ECC) algorithm”. The hybrid AES-ECC encryption technique uses the proposed Improved Poor Rich Optimization (IPRO) model to compute the optimal key. The implementation of the developed work is evaluated against the existing works for the "Chess, T1014D100K, and Retail datasets”. In particular, for the T1014D100K dataset, the cost function of the suggested model at the 2.5th iteration is 57.14%, 62.05%, 80%, 54.2%, and 56% better than the old models like BOA, SMO, SSA, PRO, and LA correspondingly.

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Data availability

The dataset used for this work are Chess, T1014D100K, and retail dataset.

Abbreviations

AES:

Advanced encryption standard

AOPF-SFS:

Attribute-order-preserving-free-SFS

AT-DIFC+:

Adaptive trust-aware decentralized information flow control

BFA:

Brute force attack

BOA:

Butterfly optimization algorithm

CA:

Central authority

CS:

Cloud server

DIFC:

Decentralized information flow control

ECC:

Elliptic curve cryptography

EP:

Encryption proxy

IFC:

Information flow control

IPRO:

Improved poor rich optimization algorithm

KPA:

Known-plaintext attack

LA:

Lion algorithm

OBL:

Opposition based learning

PRO:

Poor rich optimization algorithm

RSA:

Rivest–Shamir–Adleman cryptosystem

SFS:

Sort-filter-skyline

SMO:

Spider monkey optimization

VM:

Virtual machines

SSA:

Salp Swarm algorithm

TLC-IFC:

Tenant-led ciphertext information flow control

References

  • Ali, R., Ak, P.: A secure three-factor remote user authentication scheme using elliptic curve cryptosystem. In: Proceedings of the international conference on microelectronics, Computing and communication systems, pp. 9–24. Springer, Singapore (2018a)

    Google Scholar 

  • Ali, R., Pal, A.K.: An efficient three factor–based authentication scheme in multiserver environment using ECC. Int. J. Commun. Syst. 31(4), e3484 (2018b)

    Article  Google Scholar 

  • Alqahtani, F., Salahaldeen, D., Predrag, T.T., Frederick, T.S.: Information flow control to secure data in the cloud. In: International Conference on Computational Science and Computational Intelligence (CSCI) (2020)

  • Anandkumar, R., Kalpana, R.: A review on chaos-based image encryption using fractal function. Exam Fractal Image Process. Anal. 23–37 (2020)

  • Anandkumar, R., Kalpana, R.: A Fibonacci p-code traversing and unified chaotic map-based image encryption algorithm. J. Ambient. Intell. Humaniz. Comput. 13, 3713–3727 (2022)

    Article  Google Scholar 

  • Anandkumar, R., Dinesh, K., Obaid, A.J., Malik, P., Sharma, R., Dumka, A., Singh, R., Khatak, S.: Securing e-Health application of cloud computing using hyperchaotic image encryption framework. Comput. Electr. Eng. 100, 107860 (2022)

    Article  Google Scholar 

  • Bauereiß, T., Gritti, A.P., Popescu, A., Raimondi, F.: CoSMeDis: a distributed social media platform with formally verified confidentiality guarantees. IEEE Symp. Secur. Privacy (SP) (2017). https://doi.org/10.1109/SP.2017.24

    Article  MATH  Google Scholar 

  • Bhagyashri, A., Gurav, Y.B.: Privacy-preserving public auditing for secure cloud storage. IOSR J. Comput. Eng. (2014). https://doi.org/10.1109/TC.2011.245

    Article  Google Scholar 

  • Bhuarya, P., Chandrakar, P., Ali, R., Sharaff, A.: An enhanced authentication scheme for Internet of Things and cloud based on elliptic curve cryptography. Int. J. Commun. Syst. 34(10), e4834 (2021)

    Article  Google Scholar 

  • Bolodurina, I., Parfenov, D., Shukhman, A.: Approach to the effective controlling cloud computing resources in data centers for providing multimedia services. Int. Sib. Conf. Control Commun. (SIBCON) (2015). https://doi.org/10.1109/SIBCON.2015.7147170

    Article  Google Scholar 

  • Candotti, D., Steel, M.D.W., West, A.C.: Charting the course for Tasmania’s energy cloud roadmap IEEE PES Asia-Pacific power and energy engineering conference (APPEEC). IEEE PES Asia-Pacific Power Energy Eng. Conf. (APPEEC) (2015). https://doi.org/10.1109/APPEEC.2015.7380915

    Article  Google Scholar 

  • Cuzzocrea, A., Panagiotis, K., Akrivi, V.: Effective and efficient skyline query processing over attribute-order-preserving-free encrypted data in cloud-enabled databases. Future Gener. Comput. Syst. 126, 237–251 (2022)

    Article  Google Scholar 

  • Hamid, S., Moosavi, S.H., Bardsiri, V.K.: Poor and rich optimization algorithm: a new human-based and multi populations algorithm. Eng. Appl. Artif. Intell. 86, 165–181 (2019)

    Article  Google Scholar 

  • Khurshid, A., Khan, A.N., Khan, F.G., Ali, M., Shuja, J., Khan, A.U.: Secure-CamFlow: a device-oriented security model to assist information flow control systems in cloud environments for IoTs. Concurr. Comput. 31(8), e4729 (2019)

    Article  Google Scholar 

  • Khan, M.A., Quasim, M.T., Alghamdi, N.S., Khan, M.Y.: A secure framework for authentication and encryption using improved ECC for IoT-based medical sensor data. IEEE Access 8, 52018–52027 (2020). https://doi.org/10.1109/ACCESS.2020.2980739

    Article  Google Scholar 

  • Mane, P.S., Gurav, Y.B.: A survey on information flow control for secure cloud computing. Int. J. Innov. Res. Comput. Commun. Eng 11, 76–89 (2015)

  • Mane, P.S., Gurav, Y.B.: Secure cloud computing using decentralized information flow control. Int. Adv. Res. J. Sci. Eng. Technol 3, 167–170 (2016)

  • Naeem, H.: Detection of malicious activities in internet of things environment based on binary visualization and machine intelligence. Wirel. Pers. Commun. 108, 2609–2629 (2019)

    Article  Google Scholar 

  • Pasquier, T.F.J., Powles, J.E.: Expressing and enforcing location requirements in the cloud using information flow control. IEEE Int. Conf. Cloud Eng. (2015). https://doi.org/10.1109/IC2E.2015.71

    Article  Google Scholar 

  • Pasquier, T.F.J., Singh, J., Eyers, D., Bacon, J.: Camflow: managed data-sharing for cloud services. IEEE Trans. Cloud Comput. 5(3), 472–484 (2017). https://doi.org/10.1109/TCC.2015.2489211

    Article  Google Scholar 

  • Pasquier, T.F.J., Singh, J., Bacon, J., Eyers, D.: Information flow audit for PaaS clouds. IEEE Int. Conf. Cloud Eng. (ICE) (2019). https://doi.org/10.1109/IC2E.2016.19

    Article  Google Scholar 

  • Patra, S., Kalapureddy, M.C.R.: Cloud radar observations of multi-scale variability of cloud vertical structure associated with Indian summer monsoon over a tropical location. Clim. Dyn. 56, 1055–1081 (2021)

    Article  Google Scholar 

  • Phatak, A., Kadikar, R., Vijayan, K., Amutha, B.: Performance analysis of firewall based on SDN and open flow. Int. Conf. Commun. Signal. Process. (ICCSP) (2018). https://doi.org/10.1109/ICCSP.2018.8524164

    Article  Google Scholar 

  • Shahidinejad, A., Ghobaei-Arani, M., Esmaeili, L.: An elastic controller using colored petri nets in cloud computing environment. Clust. Comput. 23, 1045–1071 (2020)

    Article  Google Scholar 

  • Shrinath, A.K.: Information flow control for secure cloud computing. Imp. J. Interdiscip. Res. (2017). https://doi.org/10.1109/TNSM.2013.122313.130423

    Article  Google Scholar 

  • Skandylas, C., Khakpour, N., Andersson, J.: AT-DIFC+: toward adaptive and trust-aware decentralized information flow control. ACM Trans. Auton. Adapt. Syst. (TAAS) 15(4), 1–35 (2021)

    Google Scholar 

  • Solanki, N., Zhu, W., Yen, I., Bastani, F., Rezvani, E.: Multi-tenant access and information flow control for SaaS. IEEE Int. Conf. Web Serv. (ICWS) (2016). https://doi.org/10.1109/ICWS.2016.21

    Article  Google Scholar 

  • Somavanshi, N.R., Gurav, Y.B.: Survey of decentralized information flow controlfor relational database-IFDB. Int. J. Eng. Res. Ind. Appl 8, 27–36 (2015)

  • Somavanshi, N.R., Gurav, Y.B.: Security in cloud computing environment by decentralized information flow control. Int. J. Innov. Res. Comput. Commun. Eng 4(6), 1–3 (2016)

  • Xi, N., Ma, J., Sun, C., Di, Lu., Shen, Y.: Information flow control on encrypted data for service composition among multiple clouds. Distrib. Parallel Databases 36, 511–527 (2019)

    Article  Google Scholar 

  • Zaw, T.M., Thant, M., Bezzateev, S.V.: Database security with AES encryption, elliptic curve encryption and signature. Wave Electr. Appl. Inf. Telecommun. Syst. (WECONF) (2019). https://doi.org/10.1109/WECONF.2019.8840125

    Article  Google Scholar 

  • Zhang, Z., Zhi, Y., Xuehui, D., Wenfa, L., Xingyuan, C., Lei, S.: Tenant-led ciphertext information flow control for cloud virtual machines. IEEE Access (2020). https://doi.org/10.1109/ACCESS.2021.3051061

    Article  Google Scholar 

  • Zhou, L., Zhang, H., Zhang, K., Wang, B., Shen, D., Wang, Y.: Advances in applying cloud computing techniques for air traffic systems. IEEE Int. Conf. Civil Aviat. Saf. Inf. Technol. (2020). https://doi.org/10.1109/ICCASIT50869.2020.9368768

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science & IT, Dr.Babasaheb Ambedkar Marathwada University, Aurangabad, MS, India

    Yogesh B. Gurav & Bankat M. Patil

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  1. Yogesh B. Gurav
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  2. Bankat M. Patil
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Correspondence to Yogesh B. Gurav.

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Gurav, Y.B., Patil, B.M. De-centralized information flow control for cloud virtual machines with hybrid AES-ECC and improved meta-heuristic optimization based optimal key generation. Int J Intell Robot Appl 7, 406–425 (2023). https://doi.org/10.1007/s41315-022-00268-6

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