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Intrusion Detection Systems in Fog Computing – A Review

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Advances in Cyber Security (ACeS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1487))

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Abstract

With the growing volume of network throughput, packet transmission and security threats and attacks in Fog computing, the study of Intrusion Detection Systems (IDSs) in this environment has grabbed a lot of attention in the computer science field in general, and security field in particular. Since Fog, computing can be depicted as an emerging cloud-like platform holding similar data, information, computation, storage resources and application services, but is principally distinct in that it is decentralized platform. Besides, as aforementioned, Fog Computing is capable of processing huge volume of data locally, operate on premise, that is totally portable, and can be installed on several heterogeneous hardware devices; thus these characteristics make it highly vulnerable for time and location-sensitive applications; and therefore vulnerable to security attacks targeting sensitive data, virtualization technique, segregation, network resources and others. Existing IDSs pose challenges and shortcomings such as consumption of huge computational resources, capricious intrusion categories, and so forth. However, there is a number of prior studies to highlight the existing IDS issues in Fog Computing, but still there is a need to provide more comprehensive review of the most recent studies conducted in the same area to provide a more elaborated clear image for a comprehensive review. Through the inclusive review and advanced organization of this article, a new taxonomy is provided to categorize recent IDSs in Fog Computing.

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References

  1. Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog computing and its role in the internet of things. In: Proceedings of 1st ACM Mobile Cloud Computing Workshop, MCC 2012, pp. 13–15 (2012). https://doi.org/10.1145/2342509.2342513

  2. Sanjalawe, Y., Anbar, M., Al-E’mari, S., Abdullah, R., Hasbullah, I., Aladaileh, M.: Cloud data center selection using a modified differential evolution. Comput. Mater. Continua 69, 3179–3204 (2021). https://doi.org/10.32604/cmc.2021.018546

  3. Iorga, M., Feldman, L., Barton, R., Martin, M.J., Goren, N., Mahmoudi, C.: Fog computing conceptual model. NIST Spec. Publ. 500–325, 1–13 (2018). https://doi.org/10.6028/NIST.SP.500-325

    Article  Google Scholar 

  4. Aladaileh, M.A., Anbar, M., Hasbullah, I.H., Chong, Y.W., Sanjalawe, Y.K.: Detection techniques of distributed denial of service attacks on software-defined networking controller-a review. IEEE Access. 8, 143985–143995 (2020). https://doi.org/10.1109/ACCESS.2020.3013998

    Article  Google Scholar 

  5. Al-E’mari, S., Anbar, M., Sanjalawe, Y., Manickam, S.: A labeled transactions-based dataset on the ethereum network. In: Anbar, M., Abdullah, N., Manickam, S. (eds.) ACeS 2020. CCIS, vol. 1347, pp. 61–79. Springer, Singapore (2021). https://doi.org/10.1007/978-981-33-6835-4_5

    Chapter  Google Scholar 

  6. Vaquero, L.M., Rodero-Merino, L.: Finding your way in the fog: towards a comprehensive definition of fog computing. Comput. Commun. Rev. 44, 27–32 (2014). https://doi.org/10.1145/2677046.2677052

    Article  Google Scholar 

  7. Chiang, M., Zhang, T.: Fog and IoT: an overview of research opportunities. IEEE Internet Things J. 3, 854–864 (2016). https://doi.org/10.1109/JIOT.2016.2584538

    Article  Google Scholar 

  8. Stojmenovic, I., Wen, S.: The fog computing paradigm: scenarios and security issues. In: 2014 Federated Conference on Computer Science and Information Systems, FedCSIS 2014, pp. 1–8 (2014). https://doi.org/10.15439/2014F503

  9. Yi, S., Hao, Z., Qin, Z., Li, Q.: Fog computing: platform and applications. In: Proceedings of 3rd Workshop on Hot Topics in Web Systems and Technologies, HotWeb 2015, pp. 73–78 (2016). https://doi.org/10.1109/HotWeb.2015.22

  10. Chen, X., Wang, L.: Exploring fog computing-based adaptive vehicular data scheduling policies through a compositional formal method - PEPA. IEEE Commun. Lett. 21, 745–748 (2017). https://doi.org/10.1109/LCOMM.2016.2647595

    Article  Google Scholar 

  11. Kitanov, S., Janevski, T.: Fog networking for 5G and IoT. In: 5G Mobile: From Research and Innovations to Deployment Aspects, pp. 45–69 (2017)

    Google Scholar 

  12. Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., Zhao, W.: A survey on internet of things: architecture, enabling technologies, security and privacy, and applications. IEEE Internet Things J. 4, 1125–1142 (2017). https://doi.org/10.1109/JIOT.2017.2683200

    Article  Google Scholar 

  13. Hu, P., Dhelim, S., Ning, H., Qiu, T.: Survey on fog computing: architecture, key technologies, applications and open issues. J. Netw. Comput. Appl. 98, 27–42 (2017). https://doi.org/10.1016/j.jnca.2017.09.002

    Article  Google Scholar 

  14. Ni, J., Zhang, K., Lin, X., Shen, X.S.: Securing fog computing for internet of things applications: challenges and solutions. IEEE Commun. Surv. Tutor. 20, 601–628 (2018). https://doi.org/10.1109/COMST.2017.2762345

    Article  Google Scholar 

  15. Liu, L., Guo, X., Chang, Z., Ristaniemi, T.: Joint optimization of energy and delay for computation offloading in cloudlet-assisted mobile cloud computing. Wirel. Netw. 25(4), 2027–2040 (2018). https://doi.org/10.1007/s11276-018-1794-0

    Article  Google Scholar 

  16. Amairah, A., Al-Tamimi, B.N., Anbar, M., Aloufi, K.: Cloud computing and internet of things integration systems: a review. Adv. Intell. Syst. Comput. 843, 406–414 (2019). https://doi.org/10.1007/978-3-319-99007-1_39

    Article  Google Scholar 

  17. Wang, H., et al.: Architectural design alternatives based on cloud/edge/fog computing for connected vehicles. IEEE Commun. Surv. Tutor. 22, 2349–2377 (2020). https://doi.org/10.1109/COMST.2020.3020854

    Article  Google Scholar 

  18. Shi, Y., Ding, G., Wang, H., Eduardo Roman, H., Lu, S.: The fog computing service for healthcare. In: 2015 2nd International Symposium on Future Information and Communication Technologies for Ubiquitous HealthCare, Ubi-HealthTech 2015, pp. 70–74 (2015). https://doi.org/10.1109/Ubi-HealthTech.2015.7203325

  19. Mukherjee, M., Shu, L., Wang, D.: Survey of fog computing: fundamental, network applications, and research challenges. IEEE Commun. Surv. Tutor. 20, 1826–1857 (2018). https://doi.org/10.1109/COMST.2018.2814571

    Article  Google Scholar 

  20. Aazam, M., Huh, E.N.: Fog computing and smart gateway based communication for cloud of things. In: Proceedings of 2014 International Conference on Future Internet of Things and Cloud, FiCloud 2014, pp. 464–470 (2014). https://doi.org/10.1109/FiCloud.2014.83

  21. Aazam, M., Huh, E.N.: Fog computing micro datacenter based dynamic resource estimation and pricing model for IoT. In: Proceedings of International Conference on Advanced Information Networking and Applications, AINA, April 2015, pp. 687–694 (2015). https://doi.org/10.1109/AINA.2015.254

  22. Muntjir, M., Rahul, M., Alhumyani, H.A.: An analysis of internet of things (IoT): novel architectures, modern applications, security aspects and future scope with latest case studies. Int. J. Eng. Res. Technol. 6, 422–447 (2017)

    Google Scholar 

  23. Aazam, M., Hung, P.P., Huh, E.N.: Smart gateway based communication for cloud of things. In: 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings, IEEE ISSNIP 2014 (2014). https://doi.org/10.1109/ISSNIP.2014.6827673

  24. Marques, B., MacHado, I., Sena, A., Castro, M.C.: A communication protocol for fog computing based on network coding applied to wireless sensors. In: Proceedings of 29th International Symposium on Computer Architecture and High Performance Computing Work, SBAC-PADW 2017, pp. 109–114 (2017). https://doi.org/10.1109/SBAC-PADW.2017.27

  25. Rodríguez Natal, A., et al.: LISP-MN: mobile networking through LISP. Wirel. Pers. Commun. 70, 253–266 (2013). https://doi.org/10.1007/s11277-012-0692-5

    Article  Google Scholar 

  26. Hassan, M.A., Xiao, M., Wei, Q., Chen, S.: Help your mobile applications with fog computing. In: 2015 12th Annual IEEE International Conference on Sensing, Communication and Networking, SECON Workshop 2015, pp. 49–54 (2015). https://doi.org/10.1109/SECONW.2015.7328146

  27. Kai, K., Cong, W., Tao, L.: Fog computing for vehicular ad-hoc networks: paradigms, scenarios, and issues. J. China Univ. Posts Telecommun. 23, 56–96 (2016). https://doi.org/10.1016/S1005-8885(16)60021-3

    Article  Google Scholar 

  28. Hong, K., Lillethun, D., Ramachandran, U., Ottenwälder, B., Koldehofe, B.: Mobile fog: a programming model for large-scale applications on the internet of things. In: Proceedings of 2nd, 2013 ACM SIGCOMM Workshop on Mobile Cloud Computing, MCC 2013, pp. 15–20 (2013). https://doi.org/10.1145/2491266.2491270

  29. Zhang, Y., Niyato, D., Wang, P., Kim, D.I.: Optimal energy management policy of mobile energy gateway. IEEE Trans. Veh. Technol. 65, 3685–3699 (2016). https://doi.org/10.1109/TVT.2015.2445833

    Article  Google Scholar 

  30. Jalali, F., Hinton, K., Ayre, R., Alpcan, T., Tucker, R.S.: Fog computing may help to save energy in cloud computing. IEEE J. Sel. Areas Commun. 34, 1728–1739 (2016). https://doi.org/10.1109/JSAC.2016.2545559

    Article  Google Scholar 

  31. Jøsang, A., Ismail, R., Boyd, C.: A survey of trust and reputation systems for online service provision. Decis. Support Syst. 43, 618–644 (2007). https://doi.org/10.1016/j.dss.2005.05.019

    Article  Google Scholar 

  32. Damiani, E., De Capitani Di Vimercati, S., Paraboschi, S., Samarati, P., Violante, F.: A reputation-based approach for choosing reliable resources in peer-to-peer networks. In: Proceedings of ACM Conference on Computer and Communications Security, pp. 207–216 (2002). https://doi.org/10.1145/586110.586138

  33. Han, H., Sheng, B., Tan, C.C., Li, Q., Lu, S.: A measurement based rogue AP detection scheme. In: Proceedings of IEEE INFOCOM, pp. 1593–1601 (2009). https://doi.org/10.1109/INFCOM.2009.5062077

  34. Han, H., Sheng, B., Tan, C.C., Li, Q., Lu, S.: A timing-based scheme for rogue AP detection. IEEE Trans. Parallel Distrib. Syst. 22, 1912–1925 (2011). https://doi.org/10.1109/TPDS.2011.125

    Article  Google Scholar 

  35. Balfanz, D., Smetters, D.K., Stewart, P., Wong, H.C.: Talking to strangers: authentication in ad-hoc wireless networks. In: Proceedings of 9th Annual Network and Distributed System Security Symposium, pp. 7–19 (2002)

    Google Scholar 

  36. Bouzefrane, S., Mostefa, A.F.B., Houacine, F., Cagnon, H.: Cloudlets authentication in nfc-based mobile computing. Proceedings of 2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud 2014, pp. 267–272 (2014). https://doi.org/10.1109/MobileCloud.2014.46

  37. Tsugawa, M., Matsunaga, A., Fortes, J.A.B.: Cloud computing security: what changes with software-defined networking? In: Jajodia, S., Kant, K., Samarati, P., Singhal, A., Swarup, V., Wang, C. (eds.) Secure Cloud Computing, pp. 77–93. Springer, New York (2014). https://doi.org/10.1007/978-1-4614-9278-8_4

    Chapter  Google Scholar 

  38. Gennaro, R., Gentry, C., Parno, B.: Non-interactive verifiable computing: outsourcing computation to untrusted workers. In: Rabin, T. (ed.) CRYPTO 2010. LNCS, vol. 6223, pp. 465–482. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14623-7_25

    Chapter  Google Scholar 

  39. Song, D.X., Wagner, D., Perrig, A.: Practical techniques for searches on encrypted data. In: Proceedings of IEEE Computer Society Symposium on Research in Security and Privacy, pp. 44–55 (2000). https://doi.org/10.1109/secpri.2000.848445

  40. Wang, C., Cao, N., Ren, K., Lou, W.: Enabling secure and efficient ranked keyword search over outsourced cloud data. IEEE Trans. Parallel Distrib. Syst. 23, 1467–1479 (2012). https://doi.org/10.1109/TPDS.2011.282

    Article  Google Scholar 

  41. Cash, D., et al.: Dynamic searchable encryption in very-large databases: data structures and implementation. In: Citeseer (2014)

    Google Scholar 

  42. Cao, N., Wang, C., Li, M., Ren, K., Lou, W.: Privacy-preserving multi-keyword ranked search over encrypted cloud data. IEEE Trans. Parallel Distrib. Syst. 25, 222–233 (2014). https://doi.org/10.1109/TPDS.2013.45

    Article  Google Scholar 

  43. Rial, A., Danezis, G.: Privacy-preserving smart metering. In: Proceedings of the ACM Conference on Computer and Communications Security, Chicago, IL, USA, pp. 49–60 (2011)

    Google Scholar 

  44. Qin, Z., Yi, S., Li, Q., Zamkov, D.: Preserving secondary users’ privacy in cognitive radio networks. In: Proceedings of IEEE INFOCOM, Toronto, ON, Canada, pp. 772–780. Institute of Electrical and Electronics Engineers Inc. (2014)

    Google Scholar 

  45. Novak, E., Li, Q.: Near-Pri: private, proximity based location sharing. In: Proceedings - IEEE INFOCOM, Toronto, ON, Canada, pp. 37–45. Institute of Electrical and Electronics Engineers Inc. (2014)

    Google Scholar 

  46. Lu, R., Liang, X., Li, X., Lin, X., Shen, X.: EPPA: an efficient and privacy-preserving aggregation scheme for secure smart grid communications. IEEE Trans. Parallel Distrib. Syst. 23, 1621–1632 (2012). https://doi.org/10.1109/TPDS.2012.86

    Article  Google Scholar 

  47. McLaughlin, S., McDaniel, P., Aiello, W.: Protecting consumer privacy from electric load monitoring. In: Proceedings of the ACM Conference on Computer and Communications Security, Chicago, IL, USA, pp. 87–98 (2011)

    Google Scholar 

  48. Roman, R., Lopez, J., Mambo, M.: Mobile edge computing, Fog et al.: a survey and analysis of security threats and challenges. Futur. Gener. Comput. Syst. 78, 680–698 (2018). https://doi.org/10.1016/j.future.2016.11.009

  49. Khan, S., Parkinson, S., Qin, Y.: Fog computing security: a review of current applications and security solutions (2017). https://journalofcloudcomputing.springeropen.com/articles/10.1186/s13677-017-0090-3

  50. Sadaf, K., Sultana, J.: Intrusion detection based on autoencoder and isolation forest in fog computing. IEEE Access 8, 167059–167068 (2020). https://doi.org/10.1109/ACCESS.2020.3022855

    Article  Google Scholar 

  51. Khater, B.S., Wahab, A.W.B.A., Idris, M.Y.I.B., Hussain, M.A., Ibrahim, A.A.: A lightweight perceptron-based intrusion detection system for fog computing. Appl. Sci. 9, 178 (2019). https://doi.org/10.3390/app9010178

  52. Bhuvaneswari Amma, N.G., Selvakumar, S.: Anomaly detection framework for internet of things traffic using vector convolutional deep learning approach in fog environment. Futur. Gener. Comput. Syst. 113, 255–265 (2020). https://doi.org/10.1016/j.future.2020.07.020

  53. SaiSindhuTheja, R., Shyam, G.K.: An efficient metaheuristic algorithm based feature selection and recurrent neural network for DoS attack detection in cloud computing environment. Appl. Soft Comput. 100, 106997 (2021). https://doi.org/10.1016/j.asoc.2020.106997

    Article  Google Scholar 

  54. Abdel-Basset, M., Chang, V., Hawash, H., Chakrabortty, R.K., Ryan, M.: Deep-IFS: intrusion detection approach for industrial internet of things traffic in fog environment. IEEE Trans. Ind. Inform. 17, 7704–7715 (2021). https://doi.org/10.1109/TII.2020.3025755

    Article  Google Scholar 

  55. Zhou, X., Li, Y., Liang, W.: CNN-RNN based intelligent recommendation for online medical pre-diagnosis support. IEEE/ACM Trans. Comput. Biol. Bioinform. 18, 912–921 (2021). https://doi.org/10.1109/TCBB.2020.2994780

    Article  Google Scholar 

  56. de Souza, C.A., Westphall, C.B., Machado, R.B., Sobral, J.B.M., Vieira, G.S.: Hybrid approach to intrusion detection in fog-based IoT environments. Comput. Netw. 180, 107417 (2020). https://doi.org/10.1016/j.comnet.2020.107417

  57. Illy, P., Kaddoum, G., Moreira, C.M., Kaur, K., Garg, S.: Securing fog-to-things environment using intrusion detection system based on ensemble learning. In: IEEE Wireless Communications and Networking Conference, WCNC, Marrakesh, Morocco. Institute of Electrical and Electronics Engineers Inc. (2019)

    Google Scholar 

  58. Kumar, P., Gupta, G.P., Tripathi, R.: An ensemble learning and fog-cloud architecture-driven cyber-attack detection framework for IoMT networks. Comput. Commun. 166, 110–124 (2021). https://doi.org/10.1016/j.comcom.2020.12.003

    Article  Google Scholar 

  59. Pani, A.K.: An efficient algorithmic technique for feature selection in IoT based intrusion detection system. Indian J. Sci. Technol. 14, 76–85 (2021). https://doi.org/10.17485/ijst/v14i1.2057

  60. Chekired, D.A., Khoukhi, L., Mouftah, H.T.: Fog-based distributed intrusion detection system against false metering attacks in smart grid. In: IEEE International Conference on Communications, Shanghai, China. Institute of Electrical and Electronics Engineers Inc. (2019)

    Google Scholar 

  61. Lawal, M.A., Shaikh, R.A., Hassan, S.R.: An anomaly mitigation framework for IoT using fog computing. Electronics 9, 1–24 (2020). https://doi.org/10.3390/electronics9101565

    Article  Google Scholar 

  62. Huang, T., Lin, W., Xiong, C., Pan, R., Huang, J.: An ant colony optimization-based multiobjective service replicas placement strategy for fog computing. IEEE Trans. Cybern. 1–14 (2020). https://doi.org/10.1109/tcyb.2020.2989309

  63. Zedadra, O., Guerrieri, A., Jouandeau, N., Spezzano, G., Seridi, H., Fortino, G.: Swarm intelligence-based algorithms within IoT-based systems: a review. J. Parallel Distrib. Comput. 122, 173–187 (2018). https://doi.org/10.1016/j.jpdc.2018.08.007

    Article  Google Scholar 

  64. Hwaitat, A.K.A.L., Manaseer, S., Al-Sayyed, R.M.H., Almaiah, M.A., Almomani, O.: An investigator digital forensics frequencies particle swarm optimization for detection and classification of APT attack in fog computing environment (IDF-FPSO). J. Theor. Appl. Inf. Technol. 98, 937–952 (2020)

    Google Scholar 

  65. Rahman, G., Wen, C.C.: Mutual authentication security scheme in fog computing. Int. J. Adv. Comput. Sci. Appl. 10, 443–451 (2019). https://doi.org/10.14569/IJACSA.2019.0101161

  66. Kesavamoorthy, R., Ruba Soundar, K.: Swarm intelligence based autonomous DDoS attack detection and defense using multi agent system. Clust. Comput. 22(4), 9469–9476 (2018). https://doi.org/10.1007/s10586-018-2365-y

    Article  Google Scholar 

  67. Alanazi, S.T., Anbar, M., Karuppayah, S., Al-Ani, A.K., Sanjalawe, Y.K.: Detection techniques for DDoS attacks in cloud environment: review paper. In: Piuri, V., Balas, V.E., Borah, S., Syed Ahmad, S.S. (eds.) Intelligent and Interactive Computing. LNNS, vol. 67, pp. 337–354. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-6031-2_34

    Chapter  Google Scholar 

  68. Al Hwaitat, A.K., et al.: Improved security particle swarm optimization (PSO) algorithm to detect radio jamming attacks in mobile networks. Int. J. Adv. Comput. Sci. Appl. 11, 614–625 (2020). https://doi.org/10.14569/IJACSA.2020.0110480

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Authors and Affiliations

  1. National Advanced IPv6 Centre of Excellence (NAv6), Universiti Sains Malaysia (USM), 11800, George Town, Penang, Malaysia

    Fadi Abu Zwayed, Mohammed Anbar, Yousef Sanjalawe & Selvakumar Manickam

  2. Computer Science Department, Northern Border University, KSA, Arar, Saudi Arabia

    Yousef Sanjalawe

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  1. Hodeidah University, Hodeidah, Yemen

    Nibras Abdullah

  2. Universiti Sains Malaysia, Penang, Malaysia

    Selvakumar Manickam

  3. Universiti Sains Malaysia, Penang, Malaysia

    Mohammed Anbar

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Zwayed, F.A., Anbar, M., Sanjalawe, Y., Manickam, S. (2021). Intrusion Detection Systems in Fog Computing – A Review. In: Abdullah, N., Manickam, S., Anbar, M. (eds) Advances in Cyber Security. ACeS 2021. Communications in Computer and Information Science, vol 1487. Springer, Singapore. https://doi.org/10.1007/978-981-16-8059-5_30

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