The Journal of Supercomputing

, Volume 74, Issue 10, pp 5099–5126 | Cite as

Next-generation cybersecurity through a blockchain-enabled federated cloud framework

  • Olumide O. Malomo
  • Danda B. RawatEmail author
  • Moses Garuba


Minimizing the breach detection gap (BDG) for cyber-attacks is a big concern for all organizations and governments. Cyber-attacks are discovered daily, many of which have gone undetected for days to years before the victim organizations detect and deploy the cyber defense. Cyber defense solutions are advancing to combat risks and attacks from traditional to next-generation advanced defense protection solutions. However, many individuals, organizations and businesses continue to be hit by new waves of global cyber-attacks. In this paper, we present a blockchain-enabled federated cloud computing framework that uses the Dempster–Shafer theory to reduce BDG by continuously monitoring and analyzing the network traffics against cyber-attacks. We evaluate the proposed approach using numerical results, and the proposed approach outperforms the traditional approaches.


Federated cloud Blockchain Breach detection gap Federated blockchain cloud computing Dempster–Shafer theory 



This work was supported in part by the U.S. National Science Foundation (NSF) under Grants CNS-1658972 and CNS-1650831, and by the U.S. Department of Homeland Security (DHS) under Grant award number, 2017‐ST‐062‐000003. However, any opinion, finding, and conclusions or recommendations expressed in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the funding agencies. All co-authors have contributed in this paper.


  1. 1.
    Wang Y-M (2009) Security challenges in an increasingly connected worldGoogle Scholar
  2. 2.
    Government Accountability Office (GAO), Center for Science, Technology, and Engineering Report to Congressional Requesters: Internet of Things Status and implications of an increasingly connected worldGoogle Scholar
  3. 3.
    FTC Staff Reporting, “Internet of Things: Privacy and Security in a Connected World”Google Scholar
  4. 4.
    EndGame (2016) Mind the Detection Gap: Three things SOC teams must consider for earliest detection of unknown threatsGoogle Scholar
  5. 5.
    Hutchins EM, Cloppert MJ, Amin RM (2011) Intelligence-driven computer network defense informed by analysis of adversary campaigns and intrusion kill chains. Lead Iss Inf Warf Secur Res 1:80Google Scholar
  6. 6.
    SANS Institute (2014) Killing advanced threats in their tracks: an intelligent approach to attack preventionGoogle Scholar
  7. 7.
    Silvey L (2016) Cybersecurity and data breach: impact on business in IllinoisGoogle Scholar
  8. 8.
    Kaspersky Lab. (2017) Damage control: the cost of security breaches IT security risks special report seriesGoogle Scholar
  9. 9.
    Germano JH, Goldman ZK (2014) After the breach: cybersecurity liability riskGoogle Scholar
  10. 10.
    Experis (2014) Security breaches: is anyone safe?Google Scholar
  11. 11.
    Valdetero J, Zetoony D, Cave B (2014) Data security breaches incident preparedness and responseGoogle Scholar
  12. 12.
    Ponemon Institute (2011) Reputation impact of a data breachGoogle Scholar
  13. 13.
    NTT Com Security (2016) Security Breaches—what’s the real cost to your business? Risk:Value ReportGoogle Scholar
  14. 14.
    Sungard Availability Services, “The consequences of a Cyber Security Breach” Retrieved from
  15. 15.
    Gold S (2011) Advanced evasion techniquesGoogle Scholar
  16. 16.
    Phan B Seven key features to help you stop advanced evasion techniques at the firewall Senior Security Architect, McAfeeGoogle Scholar
  17. 17.
    Matrosov A, Rodionov E (2013) Advanced evasion techniques by Win32/GapzGoogle Scholar
  18. 18.
    OECD (2010) The changing consumer and market landscapeGoogle Scholar
  19. 19.
    KPMG (2017) The changing landscape of disruptive technologiesGoogle Scholar
  20. 20.
    Stratton AM, Wong KW (1997) Issues essential to world web marketGoogle Scholar
  21. 21.
    Kehrli J (2016) Blockchain explainedGoogle Scholar
  22. 22.
    Narayanan A, Miller A (2016) Cryptocurrencies, blockchains, and smart contracts; hardware for deep learningGoogle Scholar
  23. 23.
    Lemieux VL (2018) Trusting records: is blockchain technology the answer?Google Scholar
  24. 24.
    Dinh TT, Wang J, Chen G, Liu R, Ooi BC, Tan K (2017) BLOCKBENCH: a framework for analyzing private blockchainGoogle Scholar
  25. 25.
    Li W, Fedorov S, Sforzin A, Karame GO Towards scalable and private industrial blockchainsGoogle Scholar
  26. 26.
    Emmadi N, Narumanchi H (2017) Reinforcing immutability of permissioned blockchains with keyless signatures. InfrastructureGoogle Scholar
  27. 27.
    Stiller B, Bocek T Blockchains and smart contracts—a valuable alternative for distributed data basesGoogle Scholar
  28. 28.
    Digitalogy (2017) All you need to know about blockchain!Google Scholar
  29. 29.
    Tapscott D, Tapscott A (2017) How blockchain will change organizationGoogle Scholar
  30. 30.
    Ding CH, Nutanong S, Buyya R Peer-to-peer networks for content sharingGoogle Scholar
  31. 31.
    De Gruyter (2017) Blockchain revolutionGoogle Scholar
  32. 32.
    Norta A (2015) Creation of smart-contracting collaborations for decentralized autonomous organizationGoogle Scholar
  33. 33.
    Monax (2017) Explainer–blockchain. Retrieve from
  34. 34.
    Liang X, Shetty S, Tosh D, Kamhoua C, Kwiat K, Njilla L (2017) ProvChain: a blockchain-based data provenance architecture in cloud environment with enhanced privacy and availabilityGoogle Scholar
  35. 35.
    Pilkington M (2015) Blockchain technology: principles and applicationsGoogle Scholar
  36. 36.
    Hull R (2017) Blockchain: distributed event-based processing in a data-centric worldGoogle Scholar
  37. 37.
    Gervais A, Karame GO, Wust K (2016) On the security and performance of proof of work blockchainsGoogle Scholar
  38. 38.
    Larimer D (2013) Transactions as proof-of-stakeGoogle Scholar
  39. 39.
    Milutinovic M, Wu H, He H, Kanwal M (2016) Proof of luck: an efficient blockchain consensus protocolGoogle Scholar
  40. 40.
    Cachin C (2016) Architecture of the hyperledger blockchain fabricGoogle Scholar
  41. 41.
    Mazieres D (2016) The stellar consensus protocol: a federated model for internet-level consensusGoogle Scholar
  42. 42.
    Baliga A (2017) Understanding blockchain consensus modelsGoogle Scholar
  43. 43.
    ComputerWeekly. Nearly a third of malware attacks are zero-day exploits. Retrieved from
  44. 44.
    Digital-Guardian (2017) 91% Of cyber attacks start with a phishing email: here’s how to protect against phishing. Retrieved from
  45. 45.
    Sentz K, Ferson S (2002) Combination of Evidence in Dempster–Shafer theory, April 2002Google Scholar
  46. 46.
    Horneman A, Dell N (2014) Smart collection and storage method for network traffic dataGoogle Scholar
  47. 47.
    He J (2015) Dempster–Shafer theory of evidenceGoogle Scholar
  48. 48.
    Rawat DB, Njilla L, Kwiat K, Kamhoua CA (2018) iShare: Blockchain Based Privacy-aware Multi-Agent Information Sharing Games for Cybersecurity. In: Proceedings of the 2018 International Conference on Computing, Networking and Communications (ICNC): Communications and Information Security Symposium. Maui, Hawaii, USA, March 5–8, 2018Google Scholar
  49. 49.
    Rawat DB, Alshaikhi A (2018) “Leveraging Distributed Blockchain-based Scheme for Wireless Network Virtualization with Security and QoS Constraints.” In: Proceedings of the 2018 International Conference on Computing, Networking and Communications (ICNC): Communications and Information Security Symposium, Maui, Hawaii, USA, March 5–8, 2018Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Data Science and Cybersecurity Center (DSC2), College of Engineering and Architecture, Department of Electrical Engineering and Computer ScienceHoward UniversityWashingtonUSA

Personalised recommendations