Autors: Pamukov, M. E., Poulkov, V. K., Shterev, V. A.
Title: Negative Selection and Neural Network Based Algorithm for Intrusion Detection in IoT
Keywords: Negative Selection, Neural Network, IoT, IDS

Abstract: Internet of Things expands the boundaries of the Internet to encompass many devices with constraint computational and power capabilities. This limits the implementation of security techniques such as Intrusion Detection Systems. In this paper, we propose a novel classification algorithm specifically designed for Internet of Things Intrusion Detection Systems. Our solution consists of two distinct layers. First, we employ a Negative Selection algorithm for creating a training set based only on the knowledge of the normal network behavior. Based on this data we train a simple Neural Network that is used to do the actual classification. This multilayer approach allows to distance the training complexity from the computationally and power constrained IoT devices. Furthermore, the addition of Negative Selection layer allows us to train a Neural Network only based on the self/normal behavior of the network, without the need for nonself/attack data. We call this algorithm Negative Selection.

References

    Issue

    41st International Conference on Telecommunications and Signal Processing (TSP), 2018, Greece, ISBN 978-1-5386-4695-3

    Copyright IEEE

    Full text of the publication

    Цитирания (Citation/s):
    1. Hajiheidari, S., Wakil, K., Badri, M., Navimipour, N.J., "Intrusion detection systems in the Internet of things: A comprehensive investigation", Computer Networks, vol. 160, pp. 165-191, 2019, DOI: 10.1016/j.comnet.2019.05.014. - 2019 - в издания, индексирани в Scopus или Web of Science
    2. Said, W., Mostafa, A.M., "Towards a Hybrid Immune Algorithm Based on Danger Theory for Database Security", IEEE Access, vol. 8, pp. 145332-145362, 2020, DOI: 10.1109/ACCESS.2020.3015399. - 2020 - в издания, индексирани в Scopus или Web of Science
    3. Yang, C., Jia, L., Chen, B.-Q., Wen, H.-Y., "Negative Selection Algorithm Based on Antigen Density Clustering", IEEE Access, vol. 8, pp. 44967-44975, 2020, DOI: 10.1109/ACCESS.2020.2976875. - 2020 - в издания, индексирани в Scopus или Web of Science
    4. Aldhaheri, S., Alghazzawi, D., Cheng, L., Barnawi, A., Alzahrani, B.A., "Artificial Immune Systems approaches to secure the internet of things: A systematic review of the literature and recommendations for future research", Journal of Network and Computer Applications, vol. 157, 2020, DOI: 10.1016/j.jnca.2020.102537. - 2020 - в издания, индексирани в Scopus или Web of Science
    5. Sharma, R.K., Pippal, R.S., "Malicious Attack and Intrusion Prevention in IoT Network using Blockchain based Security Analysis", Proceedings - 2020 12th International Conference on Computational Intelligence and Communication Networks, CICN 2020, pp. 380-385, 2020, DOI: 10.1109/CICN49253.2020.9242610. - 2020 - в издания, индексирани в Scopus или Web of Science
    6. de Souza, C.A., Westphall, C.B., Machado, R.B., Sobral, J.B.M., Vieira, G.D.S., "Hybrid approach to intrusion detection in fog-based IoT environments", Computer Networks, vol. 180, 2020, DOI: 10.1016/j.comnet.2020.107417. - 2020 - в издания, индексирани в Scopus или Web of Science
    7. Rahman, S.A., Tout, H., Talhi, C., Mourad, A., "Internet of Things intrusion Detection: Centralized, On-Device, or Federated Learning?", IEEE Network, vol. 34, no. 6, pp. 310-317, 2020, DOI: 10.1109/MNET.011.2000286. - 2020 - в издания, индексирани в Scopus или Web of Science
    8. Mohamed, T.S., Aydin, S., IoT-Based Intrusion Detection Systems: A Review, Smart Science, available online: https://www.tandfonline.com/doi/abs/10.1080/23080477.2021.1972914?journalCode=tsma20 - 2021 - в издания, индексирани в Scopus или Web of Science
    9. Singh, K., Kaur, L., Maini, R., A survey of intrusion detection techniques based on negative selection algorithm, International Journal of Systems Assurance Engineering and Management - 2021 - в издания, индексирани в Scopus или Web of Science
    10. Alheeti, K.M.A., Alsukayti, I., Alreshoodi, M., Intelligent Botnet Detection Approach in Modern Applications, International Journal of Interactive Mobile Technologies 15(16), pp. 113-126 - 2021 - в издания, индексирани в Scopus или Web of Science
    11. Alrubayyi, H., Goteng, G., Jaber, M., Kelly, J., A novel negative and positive selection algorithm to detect unknown malware in the IoT, IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021 9484483 - 2021 - в издания, индексирани в Scopus или Web of Science
    12. Khurma, R.A., Almomani, I., Aljarah, I., Iot botnet detection using salp swarm and ant lion hybrid optimization model, Symmetry vbol. 13 no.8, pp.1377 - 2021 - в издания, индексирани в Scopus или Web of Science
    13. Albulayhi, K., Smadi, A.A., Sheldon, F.T., Abercrombie, R.K., Iot intrusion detection taxonomy, reference architecture, and analyses, Sensors vol. 21 no.19, pp.6432 - 2021 - в издания, индексирани в Scopus или Web of Science
    14. Alrubayyi, H., Goteng, G., Jaber, M., Kelly, J., Challenges of malware detection in the IoT and a review of artificial immune system approaches, Journal of Sensor and Actuator Networks vol. 10 no 4, pp. 61 - 2021 - в издания, индексирани в Scopus или Web of Science
    15. Tsimenidis, S., Lagkas, T., Rantos, K., Deep Learning in IoT Intrusion Detection, Journal of Network and Systems Management vo. 30, no.1, pp. 8 - 2022 - в издания, индексирани в Scopus или Web of Science
    16. Haq, M.A., Khan, M.A.R., AL-Harbi, T., Development of pccnn-based network intrusion detection system for edge computing, Computers, Materials and Continua vol. 71, no. 1, pp. 1769-1788 - 2022 - в издания, индексирани в Scopus или Web of Science
    17. Alhasan, S., Abdul-Salaam, G., Bayor, L., Oliver, K., "Intrusion Detection System Based on Artificial Immune System: A Review", Proceedings - 2021 International Conference on Cyber Security and Internet of Things, ICSIoT 2021, pp. 7-14, 2021, DOI: 10.1109/ICSIoT55070.2021.00011. - 2021 - в издания, индексирани в Scopus или Web of Science
    18. Abdelhamid, S., Aref, M., Hegazy, I., Roushdy, M., "A Survey on Learning-Based Intrusion Detection Systems for IoT Networks", Proceedings - 2021 IEEE 10th International Conference on Intelligent Computing and Information Systems, ICICIS 2021, pp. 278-288, 2021, DOI: 10.1109/ICICIS52592.2021.9694226. - 2021 - в издания, индексирани в Scopus или Web of Science
    19. Kumar, N., Sharma, S., "A Semantic Review on Challenges, Trends towards Defensive IDS in Internet of Things", 3rd IEEE 2022 International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022, pp. 30-37, 2022, DOI: 10.1109/ICCCIS56430.2022.10037617. - 2022 - в издания, индексирани в Scopus или Web of Science
    20. Cherfi, S., Boulaiche, A., Lemouari, A., "Multi-layer Perceptron for Intrusion Detection Using Simulated Annealing", Lecture Notes in Networks and Systems, vol. 593 LNNS, pp. 31-45, 2023, DOI: 10.1007/978-3-031-18516-8_3. - 2023 - в издания, индексирани в Scopus или Web of Science
    21. Hussain, A., Sharif, H., Rehman, F., Kirn, H., Sadiq, A., Khan, M.S., Riaz, A., Ali, C.N., Chandio, A.H., "A Systematic Review of Intrusion Detection Systems in Internet of Things Using ML and DL", 2023 4th International Conference on Computing, Mathematics and Engineering Technologies: Sustainable Technologies for Socio-Economic Development, iCoMET 2023, 2023, DOI: 10.1109/iCoMET57998.2023.10099142. - 2023 - в издания, индексирани в Scopus или Web of Science
    22. Li, B., Chang, Y., Huang, H., Li, W., Li, T., Chen, W., "Artificial immunity based distributed and fast anomaly detection for Industrial Internet of Things", Future Generation Computer Systems, vol. 148, pp. 367-379, 2023, DOI: 10.1016/j.future.2023.06.011. - 2023 - в издания, индексирани в Scopus или Web of Science
    23. Seifi, S., Beaubrun, R., Bellaiche, M., Halabi, T., "A Study on the Efficiency of Intrusion Detection Systems in IoT Networks", Proceedings of the 2023 IEEE International Conference on Computer, Information, and Telecommunication Systems, CITS 2023, 2023, DOI: 10.1109/CITS58301.2023.10188799. - 2023 - в издания, индексирани в Scopus или Web of Science
    24. Rashid, M.M., Khan, S.U., Eusufzai, F., Redwan, M.A., Sabuj, S.R., Elsharief, M., "A Federated Learning-Based Approach for Improving Intrusion Detection in Industrial Internet of Things Networks", Network, vol. 3, no. 1, pp. 158-179, 2023, DOI: 10.3390/network3010008. - 2023 - в издания, индексирани в Scopus или Web of Science
    25. Alrubayyi H.; Goteng G.; Jaber M., "AIS for Malware Detection in a Realistic IoT System: Challenges and Opportunities", Network, vol. 3, no. 4, pp. 522-537, 2023, DOI: 10.3390/network3040023. - 2023 - в издания, индексирани в Scopus или Web of Science
    26. Alrubayyi H.; Alshareef M.S.; Nadeem Z.; Abdelmoniem A.M.; Jaber M., "Security Threats and Promising Solutions Arising from the Intersection of AI and IoT: A Study of IoMT and IoET Applications", Future Internet, vol. 16, no. 3, 2024, DOI: 10.3390/fi16030085. - 2024 - в издания, индексирани в Scopus или Web of Science

    Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus и Web of Science