Advertisement

Fog Computing pp 205-225 | Cite as

Context-Aware Intelligent Systems for Fog Computing Environments for Cyber-Threat Intelligence

  • Arif Sari
Chapter

Abstract

The capture and deployment of intelligence to build strong defense is more holistic and a more focused approach to security for private and government organizations, due to diversified activities of threat actors and malicious users. It is essential for the private sector and government organizations to use technologies such as Cloud, Fog and Edge Computing that provide storing, processing, and transferring vast amounts of public and private data that can be used to generate classified and unclassified threat intelligence. Fog Computing is a platform that uses computation, storage, and application services similar to the Cloud Computing paradigm but fundamentally different due to its decentralized architecture. It is an emerging technology that gives the Cloud a companion to handle the exabytes of data generated daily from the devices in the Internet of things (IoT) environment. In this scenario, the context-aware intelligent systems of Fog Computing that analyze the IoT data become crucial weapons to detect, mitigate, and prevent any possible cyber-threats, much earlier. The IoT environments generate vast amount of data that requires different context-aware intelligent systems to analyze this volumetric and complex data for security proposes to provide cyber-threat intelligence (CTI). The CTI is a collection of intelligence combining open-source intelligence (OSINT), social media intelligence (SOCMINT), measurement and signature intelligence (MASINT), human intelligence (HUMINT), and technical intelligence (TECHINT) from deep and dark Web. All these intelligence systems have functions that are based on specific context-aware intelligence mechanisms. In this chapter, sensitive data collection and analysis methods and techniques are elaborated through several context-aware intelligence systems to expose the use of Fog Computing for analysis of edge networks data for cyber-threat intelligence.

Keywords

Fog computing Edge computing Cloud computing Context-Aware intelligence Cyber-Threat intelligence Data analysis Cyber-threat Internet of things IoT 

References

  1. 1.
    Mutemwa M, Mtsweni J, Mkhonto N (2017) Developing a cyber threat intelligence sharing platform for South African organisations. In: 2017 Conference on Information Communication Technology and Society (ICTAS), Umhlanga, pp 1–6.  https://doi.org/10.1109/ictas.2017.7920657
  2. 2.
    El-Sayed H et al (2018) Edge of things: the big picture on the integration of edge, IoT and the cloud in a distributed computing environment. In: IEEE Access, vol. 6, pp 1706–1717, 2018  https://doi.org/10.1109/access.2017.2780087CrossRefGoogle Scholar
  3. 3.
    Bonomi F, Milito R, Natarajan P, Zhu J (2014) Fog Computing: a platform for internet of things and analytics. Springer International Publishing, Cham, pp 169–186Google Scholar
  4. 4.
    Aazam M, Huh EN (2014) Fog Computing and smart gateway based communication for cloud of things. In: International conference on future internet of things and cloudGoogle Scholar
  5. 5.
    Dastjerdi AV, Buyya R (2016) Fog Computing: helping the internet of things realize its potential. Computer 49(8):112–116CrossRefGoogle Scholar
  6. 6.
    Luan TH, Gao L, Li Z, Xiang Y, Sun L (2015) Fog Computing: focusing on mobile users at the edge. CoRR, vol. abs/1502.01815Google Scholar
  7. 7.
    Al-Shuwaili A, Simeone O (2017) Energy-efficient resource allocation for mobile edge computing-based augmented reality applications. IEEE Wireless Commun Lett 6(3):398–401CrossRefGoogle Scholar
  8. 8.
    Deliu I, Leichter C, Franke K (2017) Extracting cyber threat intelligence from hacker forums: support vector machines versus convolutional neural networks. In: 2017 IEEE international conference on big data (Big Data), Boston, MA, pp 3648–3656.  https://doi.org/10.1109/bigdata.2017.8258359
  9. 9.
    Guido D (2011) A case study of intelligence-driven defense. In: IEEE security & privacy, vol. 9, no. 6, pp 67–70.  https://doi.org/10.1109/msp.2011.158CrossRefGoogle Scholar
  10. 10.
    Oman D (2017) Social media intelligence. In: Dover R et al (eds) The Palgrave handbook of security, risk and intelligence, pp 355–371.  https://doi.org/10.1057/978-1-137-53675-4_20
  11. 11.
    Steele RD (1997) Open source intelligence, what is it? why it is important to the military? Open Source Solutions Inc. International Public Information Clearing House, pp 329–341Google Scholar
  12. 12.
    Omand D (2010) Securing the state. Hurst, LondonGoogle Scholar
  13. 13.
    Omand D, Bartlett J, Miller C (2012) Introducing Social Media Intelligence (SOCMINT). Intell Nat Secur 27(6):801–823.  https://doi.org/10.1080/02684527.2012.716965CrossRefGoogle Scholar
  14. 14.
    Omand D (2015) Understanding digital intelligence and the norms that might govern it. CIGI, Ottawa and Chatham House, LondonGoogle Scholar
  15. 15.
    Omand D Bartlett J, Miller C (2012) Introducing Social Media Intelligence (SOCMINT). Intell Nat Secur 27(6): 801–823CrossRefGoogle Scholar
  16. 16.
    Butler R (2004) Review of intelligence on weapons of mass destruction. UK House of Commons, HC 898, July 14Google Scholar
  17. 17.
    Obama B (2014) Remarks by the President on review of signals intelligence. Department of Justice, Washington, DC, 17 Jan. www.whitehouse.gov/the-press-office/2014/01/17/remarks-president-review-signals-intelligence
  18. 18.
    Dudczyk J, Kawalec A, Cyrek J (2008) Applying the distance and similarity functions to radar signals identification. In: 2008 International radar symposium, Wroclaw, pp 1–4.  https://doi.org/10.1109/irs.2008.4585771
  19. 19.
    Sene DE, Caldwell WT, Grigsby JA, George JD, Evans HE, Emmerich CJ (1999) Calibration stars. In: 1999 IEEE aerospace conference. Proceedings (Cat. No.99TH8403), vol 4, Snowmass at Aspen, CO, pp 297–306.  https://doi.org/10.1109/aero.1999.792098
  20. 20.
    Omand D (2006) Ethical guidelines in using secret intelligence for public security. Cambridge Rev Int Aff 19(4):613–628CrossRefGoogle Scholar
  21. 21.
    Dastjerdi AV, Gupta H, Calheiros RN, Ghosh SK, Buyya R (2016) Fog Computing: principles, architectures, and applications. CoRR, vol.abs/1601.02752Google Scholar
  22. 22.
    Stojmenovic I, Wen S (2014) The Fog Computing paradigm: scenarios and security issues. In: 2014 federated conference on FedCSIS. IEEEGoogle Scholar
  23. 23.
    Zao JK, Gan TT, You CK, Méndez SJR, Chung CE, Te Wang Y, Mullen T, Jung TP (2014) Augmented brain computer interaction based on Fog Computing and linked data. In: 2014 international conference on Intelligent Environments (IE). IEEE, pp 374–377Google Scholar
  24. 24.
    Loke SW (2015) The internet of flying-things: opportunities and challenges with airborne Fog Computing and mobile cloud in the clouds. CoRR, vol. abs/1507.04492Google Scholar
  25. 25.
    Misra P, Simmhan Y, Warrior J (2015) Towards a practical architecture for india centric internet of things: an India-centric view. IEEE IoT NewslettGoogle Scholar
  26. 26.
    Al-Badarneh J, Jararweh Y, Al-Ayyoub M, Al-Smadi M, Fontes R (2017) Software defined storage for cooperative mobile Edge Computing systems. In: Proceedings of 4th international conference on Software Defined Systems (SDS), pp 174–179Google Scholar
  27. 27.
    Vallati C, Virdis A, Mingozzi E, Stea G (2016) Mobile-edge computing come home connecting things in future smart homes using LTE device to-device communications. IEEE Consum Electron Magn 5(4):77–83CrossRefGoogle Scholar
  28. 28.
    Ahmed A, Ahmed E (2016) A survey on mobile edge computing. In: 2016 10th International Conference on Intelligent Systems and Control (ISCO). IEEE, pp 1–8Google Scholar
  29. 29.
    Shahid MA, Sharif M (2015) Cloud computing security models, architectures, issues and challenges: a survey. Smart Comput Rev 5:602–616CrossRefGoogle Scholar
  30. 30.
    Stantchev V, Barnawi A, Ghulam S, Schubert J, Tamm G (2015) Smart items, fog and cloud computing as enablers of servitization in healthcare. Sens Transducers 185(2):121Google Scholar
  31. 31.
    Amin SM, Wollenberg BF (2005) Toward a smart grid: power delivery for the 21st century. IEEE Power Energ Magn 3(5):34–41CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Management Information Systems, School of Applied SciencesGirne American UniversityCanterburyUK

Personalised recommendations