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Markov Decision Process based Model for Performance Analysis an Intrusion Detection System in IoT Networks

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper, a new reinforcement learning intrusion detection system is developed for IoT networks incorporated with WSNs. A research is carried out and the proposed model RL-IDS plot is shown, where the detection rate is improved. The outcome shows a decrease in false alarm rates and is compared with the current methodologies. Computational analysis is performed, and then the results are compared with the current methodologies, i.e. distributed denial of service (DDoS) attack. The performance of the network is estimated based on security and other metrics.
Rocznik
Tom
Strony
42--49
Opis fizyczny
Bibliogr. 22 poz., rys., tab.
Twórcy
Bibliografia
  • [1] M. Roopak, G. Y. Tian, and J. Chambers, „Deep learning models for cyber security in IoT networks", in Proc. IEEE 9th Annual Comput. and Commun. Workshop and Conf. (CCWC), Las Vegas, NV, USA, 2019, pp. 452-457 (DOI: 10.1109/CCWC.2019.8666588).
  • [2] X. Yuan, C. Li, and X. Li, „DeepDefense: identifying DDoS attaca via deep learning", in Proc. of the 2017 IEEE Int. Conf. on Smart Comput. (SMARTCOMP), Hong Kong, China, 2017, pp. 1-8 (DOI: 10.1109/SMARTCOMP.2017.7946998).
  • [3] D. Evans, „The Internet of Things: how the next evolution of the Internet is changing everything", Cisco Internet Business Solutions Group (IBSG), 2011 [Online]. Available: http://www.cisco.com/c/dam/en us/about/ac79/docs/innov/IoT IBSG 0411FINAL.pdf
  • [4] C. Kolias, G. Kambourakis, A. Stavrou, and J. Voas, „DDoS In the IoT: Mirai and other botnets", Computer, vol. 50. no. 7, 2017, pp. 80-84 (DOI: 10.1109/MC.2017.201).
  • [5] P. Radanliev et al., „Future developments in cyber risk assessment for the Internet of Things", Computers in Industry, vol. 102, pp. 14-22, 2018 (DOI: 10.1016/j.compind.2018.08.002).
  • [6] E. Bertino and N. Islam, „Botnets and Internet of Things security", Computer, vol. 50, no. 2, pp. 76-79, 2017 (DOI: 10.1109/MC.2017.62).
  • [7] M. A. Al-Garadi, A. Mohamed, A. Al-Ali, X. Du, and M. Guizani, „A Survey of machine and deep learning methods for Internet of Things (IoT) security", IEEE Commun. Surveys & Tutorials, vol. 22, no. 3, pp. 1646-1685, 2020 (DOI: 10.1109/COMST.2020.2988293).
  • [8] A. Okwori, „Intrusion detection in Internet of Things (IoT)", Int. J. of Advanced Res. in Computer Sci., vol. 9, pp. 504-509, 2018 (DOI: 10.26483/ijarcs.v9i1.5429).
  • [9] Y. Meidan, „ProfilIoT: a machine learning approach for IoT device identification based on network traffic analysis", in Proc. of the Symp. on Applied Comput. - SAC '17, Marrakech, Morocco, 2017, pp. 506-509 (DOI: 10.1145/3019612.3019878).
  • [10] E. Anthi, L. Williams, M. Slowinska, G. Theodorakopoulos, and P. Burnap, „A Supervised intrusion detection system for smart Home IoT devices", IEEE Internet of Things J., vol. 6, no. 5, 2019, pp. 9042-9053 (DOI: 10.1109/JIOT.2019.2926365).
  • [11] A. Azmoodeh, A. Dehghantanha, and K.-K. R. Choo, „Robust malware detection for Internet of (battle field) things devices using deep eigenspace learning", IEEE Trans. Sustain. Comput., vol. 4, no. 1, 2019, pp. 88-95 (DOI: 10.1109/TSUSC.2018.2809665).
  • [12] S. Hajiheidari, K. Wakil, M. Badri, and N. J. Navimipour, „Intrusion detection systems in the Internet of Things: A comprehensive investigation", Comput. Netw., vol. 160, pp. 165-191, 2019 (DOI: 10.1016/j.comnet.2019.05.014).
  • [13] R. Nicolescu et al., „Mapping the values of IoT", J. Inf. Technol., vol. 33, pp. 345-360, 2019 (DOI: 10.1057/s41265-018-0054-1).
  • [14] S. Sheng et al., „Deep reinforcement learning-based task scheduling in IoT edge computing", Sensors (Basel), vol. 21, no. 1666, 2021 (DOI: 10.3390/s21051666).
  • [15] Y. Chen et al., „Deep reinforcement learning based dynamic resource management for mobile edge computing in industrial Internet of Things", IEEE Transac. on Industrial Informat., vol. 17, no. 7, pp. 4925-4934, 2021 (DOI: 10.1109/TII.2020.3028963).
  • [16] M. Elrawy, A. Awad, and H. Hamed, „Intrusion detection systems for IoT-based smart environments: a survey", J. Cloud Comput., vol. 7, no. 21, 2018 (DOI: 10.1186/s13677-018-0123-6).
  • [17] M. H. Bhuyan, D. K. Bhattacharyya, and J. K. Kalita, „Network anomaly detection: methods, systems and tools", IEEE Communications Surveys & Tutorials, vol. 16, no. 1, pp. 303-336, 2013 (DOI: 10.1109/SURV.2013.052213.00046).
  • [18] F. Hussain, R. Hussain, S. A. Hassan, and E. Hossain, „Machine learning in IoT security: current solutions and future challenges", arXiv [Online]. Available: https://arxiv.org/pdf/1904.05735.pdf
  • [19] K. A. P. da Costa, J. P. Papa, C. de Oliveira-Lisboa, R. Munoz, and V. H. C. de Albuquerque, „Internet of Things: a survey on machine learning-based intrusion detection approaches", Computer Networks, vol. 151, pp. 147-157, 2019 (DOI: 10.1016/j.comnet.2019.01.023).
  • [20] Z. Chen, C. K. Yeo, B. S. Lee, and C. T. Lau, „Autoencoder-based network anomaly detection", in 2018 Wireless Telecommun. Symp. (WTS), Phoenix, AZ, USA, 2018, pp. 1-5 (DOI: 10.1109/WTS.2018.8363930).
  • [21] S. U. Jan, S. Ahmed, V. Shakhov, and I. Koo, „Toward a lightweight intrusion detection system for the Internet of Things", IEEE Access, vol. 7, pp. 42450-42471, 2019 (DOI: 10.1109/ACCESS.2019.2907965).
  • [22] M. Abomhara and G. M. Koien, „Cyber security and the Internet of Things: vulnerabilities, threats, intruders and attacks", J. of Cyber Secur. and Mobil., vol. 4, no. 1, pp. 65-88, 2015 (DOI: 10.13052/jcsm2245-1439.4).
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e91d9859-b66c-4a56-a169-d20b06e38130
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