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HIoTPOT: Surveillance on IoT Devices against Recent Threats

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Abstract

Honeypot Internet of Things (IoT) (HIoTPOT) keep a secret eye on IoT devices and analyzes the various recent threats which are dangerous to IoT devices. In this paper, implementation of a research honeypot is presented which is used to learn the recent tactics and ethics used by black hat community to attack on IoT devices. As IoT is open and easy for accessing, all the intruders are highly attracted towards IoT. Recently Telnet based attacks are very famous on IoT devices to get easy access and attack on other devices. To reduce these kinds of threats, it is necessary to know in details about intruder, therefore the aim of this research work is to implement novel based secret eye server known as HIoTPOT which will make the IoT environment more safe and secure.

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References

  1. Manogaran, G., Lopez, D., Thota, C., Abbas, K. M., Pyne, S., & Sundarasekar, R. (2017). Big data analytics in healthcare internet of things. In Innovative healthcare systems for the 21st century (pp. 263-284). Springer International Publishing.

  2. Lopez, D., & Manogaran, G. (2016). Data architecture for climate change and disease dynamics. In R. S. Tomar et al. (Eds.), The human element of big data: issues, analytics, and performance. Baca Raton: CRC Press.

    Google Scholar 

  3. Manogaran, G., & Lopez, D. (2017). Disease surveillance system for big climate data processing and dengue transmission. International Journal of Ambient Computing and Intelligence, 8(2), 1–25.

    Article  Google Scholar 

  4. Manogaran, G., & Lopez, D. (2017). Spatial cumulative sum algorithm with big data analytics for climate change detection. Computers & Electrical Engineering.

  5. Manogaran, G., & Lopez, D. (2017). A Gaussian process based big data processing framework in cluster computing environment. Cluster Computing, 1–16.

  6. Lopez, D., Manogaran, G., & Jagan, J. (2017). Modelling the H1N1 influenza using mathematical and neural network approaches. Biomedical Research, 28(8), 1–5.

    Google Scholar 

  7. Lopez, D., Gunasekaran, M., Murugan, B. S., Kaur, H., and Abbas, K. M. (2014, October). “Spatial BigData analytics of influenza epidemic in Vellore, India, In Proceedings 2014 IEEE International Conference onBig Data (pp. 19–24). IEEE.

  8. Lopez, D. & Gunasekaran, M. (2015). Assessment of Vaccination Strategies Using Fuzzy MulticriteriaDecision Making. In Proceedings proceedings of the fifth international conference on fuzzy and neurocomputing (FANCCO-2015) (pp. 195–208). Springer International.

  9. Mukherjee, B., Heberlein, L. T., & Levitt, K. N. (1994). Network intrusion detection. IEEE Network, 8(3), 26–41.

    Article  Google Scholar 

  10. Zhao, M., Grossklags, J., & Chen, K. (2014) An exploratory study of white hat behaviors in a web vulnerability disclosure program. In Proceedings of the 2014 ACM workshop on security information workers (pp. 51–58).

  11. Lopez, D., & Sekaran, G. (2016). Climate change and disease dynamics—a big data perspective. International Journal of Infectious Diseases, 45, 23–24.

    Article  Google Scholar 

  12. Manogaran, G., Vijayakumar, V., Varatharajan, R., Kumar, P. M., Sundarasekar, R., & Hsu, C. H. Machine learning based big data processing framework for cancer diagnosis using hidden markov model and gm clustering. Wireless Personal Communications, 1–18.

  13. Varatharajan, R., Manogaran, G., & Priyan, M. K. (2017). A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing. Multimedia Tools and Applications, 1–21.

  14. McGrew, R. (2006) Experiences with honeypot systems: Development, deployment, and analysis. In HICSS’06. Proceedings of the 39th annual Hawaii international conference on system sciences (Vol. 9, p. 220a).

  15. Reddy, M. P., Reddy, K. S., Reddy, M. I. S., & Sreenivasulu, G. Host based information gathering honeypots for network security. In Editor. Board, p. 369.

  16. Thota, C., Sundarasekar, R., Manogaran, G., Varatharajan, R., & Priyan, M. K. (2018). Centralized fog computing security platform for IoT and cloud in healthcare system. In Exploring the Convergence of Big Data and the Internet of Things (pp. 141–154). IGI Global.

  17. Manogaran, G., & Lopez, D. (2016). Health data analytics using scalable logistic regression with stochastic gradient descent. International Journal of Advanced Intelligence Paradigms, 9, 1–15.

    Google Scholar 

  18. Byun, J.-Y., Nasridinov, A., & Park, Y.-H. (2014). Internet of things for smart crime detection. Contemporary Engineering Sciences, 7(15), 749–754.

    Article  Google Scholar 

  19. Manogaran, G., Thota, C., Lopez, D., Vijayakumar, V., Abbas, K. M., & Sundarsekar, R. (2017). Big data knowledge system in healthcare. In: Internet of things and big data technologies for next generation healthcare (pp. 133–157). Springer International Publishing.

  20. Manogaran, G., & Lopez, D. (2017). Disease surveillance system for big climate data processing and dengue transmission. International Journal of Ambient Computing and Intelligence (IJACI), 8(2), 88–105.

    Article  Google Scholar 

  21. Li, L., Sun H., & Zhang, Z. (2011) the research and design of honeypot system applied in the lan security. In 2011 IEEE 2nd international conference on software engineering and service science (ICSESS 2011) (pp. 360–363).

  22. Manogaran, G., Lopez, D., Thota, C., Abbas, K. M., Pyne, S., & Sundarasekar, R. (2017). Big data analytics in healthcare Internet of Things. In Innovative healthcare systems for the 21st century (pp. 263–284). Springer International Publishing.

  23. Manogaran, G., & Lopez, D. (2017). A survey of big data architectures and machine learning algorithms in healthcare. International Journal of Biomedical Engineering and Technology, 25(2–4), 182–211.

    Article  Google Scholar 

  24. Suo, X., Han, X. & Gao, Y. (2014) Research on the application of honeypot technology in intrusion detection system. In 2014 IEEE workshop on advanced research and technology in industry applications (WARTIA) 2014 (pp. 1030–1032).

  25. Manogaran, G., Thota, C., & Lopez, D. (2018). Human-computer interaction with big data analytics. In HCI Challenges and privacy preservation in big data security (pp. 1–22). IGI Global.

  26. Kumar, P. M., Gandhi, U., Varatharajan, R., Manogaran, G., Jidhesh, R., & Vadivel, T. (2017). Intelligent face recognition and navigation system using neural learning for smart security in internet of things. Cluster Computing, 1–12.

  27. Lopez, D., & Manogaran, G. (2017). Parametric model to predict H1N1 influenza in Vellore District, Tamil Nadu, India. In S. Pyne et al. (Eds.), Handbook of Statistics (Vol. 37, pp. 301–316). Elsevier.

  28. Manogaran, G., Varatharajan, R., & Priyan, M. K. (2017). Hybrid recommendation system for heart disease diagnosis based on multiple kernel learning with adaptive neuro-fuzzy inference system. Multimedia Tools and Applications, 1–21.

  29. Varatharajan, R., Manogaran, G., Priyan, M. K., Balaş, V. E., & Barna, C. (2017). Visual analysis of geospatial habitat suitability model based on inverse distance weighting with paired comparison analysis. Multimedia Tools and Applications, 1–21.

  30. Varatharajan, R., Vasanth, K., Gunasekaran, M., Priyan, M., & Gao, X. Z. (2017). An adaptive decision based kriging interpolation algorithm for the removal of high density salt and pepper noise in images. Computers & Electrical Engineering (In Press).

  31. Varatharajan, R., Manogaran, G., Priyan, M. K., & Sundarasekar, R. (2017). Wearable sensor devices for early detection of Alzheimer disease using dynamic time warping algorithm. Cluster Computing, 1–10. (In Press)

  32. Varatharajan, R., Manogaran, G., & Priyan, M. K. (2017). A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing. Multimedia Tools and Applications, 1–21.

  33. Pomsathit, A. (2012) Effective of unicast and multicast IP address attack over intrusion detection system with honeypot. In 2012 spring congress on engineering and technology (S-CET) 2012 (pp. 1–4).

  34. Manogaran, G., Varatharajan, R., Lopez, D., Kumar, P. M., Sundarasekar, R., & Thota, C. (2017). A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting. Future Generation Computer Systems.

  35. Thota, C., Sundarasekar, R., Manogaran, G., Varatharajan, R., & Priyan, M. K. (2018). Centralized Fog Computing Security Platform for IoT and Cloud in Healthcare System. In Exploring the Convergence of Big Data and the Internet of Things (pp. 141–154). IGI Global.

  36. Suresh, A., & Varatharajan, R. (2017). Competent resource provisioning and distribution techniques for cloud computing environment. Cluster Computing, 1–8. (In Press)

  37. Priyan, M. K., & Devi, G. U. (2017). Energy efficient node selection algorithm based on node performance index and random waypoint mobility model in internet of vehicles. Cluster Computing, 1–15. (In Press)

  38. Xiaoyong, L., & Dongxi, L. (2005). An automatic scheme to construct Snort rules from honeypots data. Journal of Systems Engineering and Electronics, 16(2), 466–470.

    Google Scholar 

  39. Kumar, P. M., & Gandhi, U. D. (2017). A novel three-tier Internet of Things architecture with machine learning algorithm for early detection of heart diseases. Computers & Electrical Engineering.

  40. Kumar, P. M., & Gandhi, U. D. (2017). Enhanced DTLS with CoAP-based authentication scheme for the internet of things in healthcare application. The Journal of Supercomputing, 1–21.

  41. da Silva Vargas, I. R. J., & Kleinschmidt, J. H. (2015). Capture and analysis of malicious traffic in VoIP environments using a low interaction honeypot. IEEE Latin America Transactions, 13(3), 777–783.

    Article  Google Scholar 

  42. Beham, M.,Vlad, M. & Reiser, H. P. (2013) Intrusion detection and honeypots in nested virtualization environments. In 2013 43rd Annual IEEE/IFIP international conference on dependable systems and networks (DSN) 2013 (pp. 1–6).

  43. La, Q. D., Quek, T., Lee, J., Jin, S., & Zhu, H. (2016). Deceptive attack and defense game in honeypot-enabled networks for the internet of things. IEEE Internet Things Journal, 3, 1025.

    Article  Google Scholar 

  44. Levine, J. G., Grizzard, J. B., & Owen, H. L. (2004). Using honeynets to protect large enterprise networks. IEEE Security and Privacy, 2(6), 73–75.

    Article  Google Scholar 

  45. Selvaraj, R., Kuthadi, V. M., & Marwala, T. (2016). Ant-based distributed denial of service detection technique using roaming virtual honeypots. IET Communications, 10(8), 929–935.

    Article  Google Scholar 

  46. Dongxia, L. & Yongbo, Z. (2012) An intrusion detection system based on honeypot technology. In 2012 International conference on computer science and electronics engineering (ICCSEE) 2012 (Vol. 1, pp. 451–454).

  47. McCarty, B. (2003). Botnets: Big and bigger. IEEE Security and Privacy, 99(4), 87–90.

    Article  Google Scholar 

  48. Raynal, F., Berthier, Y., Biondi, P., & Kaminsky, D. (2004). Honeypot forensics part 1: Analyzing the network. IEEE Security and Privacy, 2(4), 72–78.

    Article  Google Scholar 

  49. Bringer, M. L., Chelmecki, C. A., & Fujinoki, H. (2012). A survey: Recent advances and future trends in honeypot research. International Journal of Computer Network and Information Security, 4(10), 63.

    Article  Google Scholar 

  50. Liu, C., Yang, J., Zhang, Y., Chen, R., & Zeng, J. (2011) Research on immunity-based intrusion detection technology for the internet of things. In 2011 Seventh international conference on natural computation (ICNC) 2011 (Vol. 1, pp. 212–216).

  51. Sforzin, A., Conti, M., Mármol, F. G. & Bohli, J.-M. RPiDS: Raspberry Pi IDS a fruitful intrusion detection system for IoT. (In Press)

  52. Gendreau,A. A. & Moorman, M. (2016) Survey of intrusion detection systems towards an end to end secure internet of things. In 2016 IEEE 4th international conference on future internet of things and cloud (FiCloud) 2016 (pp. 84–90).

  53. Bhatia, J. S., Sehgal, R., Bhushan, B. & Kaur, H. (2008) Multi layer cyber attack detection through honeynet. In 2008. NTMS’08 new technologies, mobility and security 2008 (pp. 1–5).

  54. Kuwatly, I., Sraj, M., Al Masri, Z. & Artail, H. (2004) A dynamic honeypot design for intrusion detection. In ICPS 2004. IEEE/ACS international conference on pervasive services, 2004 (pp. 95–104). (In Press)

  55. Li, X.-Q., Ding, X., Zhang, Y., Sun, Z.-P., & Zhao, H.-W. (2016). IoT family robot based on raspberry Pi, In International conerece on information system and artificial intelligence (ISAI), 2016 (pp. 622–625). (In Press)

  56. Beheshti, M., Han, J., Kowalski, K., Ortiz, J., Tomelden, J., & Alvillar, D. (2008) Packet information collection and transformation for network intrusion detection and prevention. In International symposium on telecomunications, 2008. IST 2008 2008 (pp. 42–48).

  57. Oriwoh, E., Jazani, D., Epiphaniou, G. & Sant, P. (2013) Internet of things forensics: Challenges and approaches. In 2013 9th international conference conference on collaborative computing: Networking, applications and worksharing (Collaboratecom), 2013 (pp. 608–615). (In Press)

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Authors and Affiliations

  1. VIT University, Vellore, India

    Usha Devi Gandhi, Priyan Malarvizhi Kumar & Shreyas Kadu

  2. Sri Ramanujar Engineering College, Chennai, India

    R. Varatharajan

  3. University of California, Davis, CA, USA

    Gunasekaran Manogaran

  4. Anna University, Chennai, India

    Revathi Sundarasekar

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  1. Usha Devi Gandhi
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  3. R. Varatharajan
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  4. Gunasekaran Manogaran
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  5. Revathi Sundarasekar
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  6. Shreyas Kadu
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Correspondence to R. Varatharajan.

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Gandhi, U.D., Kumar, P.M., Varatharajan, R. et al. HIoTPOT: Surveillance on IoT Devices against Recent Threats. Wireless Pers Commun 103, 1179–1194 (2018). https://doi.org/10.1007/s11277-018-5307-3

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