Abstract
The term “Cyber-Physical Systems” (CPS) often refers to systems that are both designed and physical, as well as biological. In regard to a CPS, the evolution of physical quantities and distinct software and hardware states are usually distinguishing it over time. Continuous state variables for the physical components interspersed with discrete events may be used to represent them in general. CPS is employed in a variety of industries, including healthcare, because of its efficiency. An MCPS is a medically critical integration of a medical cyber-physical system. Continuous, high-quality treatment is made possible via the employment of these systems. Challenges include interoperability, security/privacy, and high system software assurance in the MCPS architecture. It’s still early days for MCPS, thus, adequate standards and procedures must be established for their security. Also, due to their low processing capability, they are susceptible to a wide variety of cyberattacks. As a result, MCPS devices need defined protocols and paradigms to maintain their security. In this context, this paper aims to propose DDoS attack detection for the MCPS system. We used statistical approaches to identify and mitigate DDoS attack traffic in the MCPS system.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adat, V., Gupta, B.: Security in internet of things: issues, challenges, taxonomy, and architecture. Telecommun. Syst. 67(3), 423–441 (2018)
Agrawal, N., Tapaswi, S.: Defense mechanisms against DDOs attacks in a cloud computing environment: state-of-the-art and research challenges. IEEE Commun. Surv. Tutorials 21(4), 3769–3795 (2019)
Ahmed, K.D., Askar, S., et al.: Deep learning models for cyber security in IoT networks: a review. Int. J. Sci. Bus. 5(3), 61–70 (2021)
Aljuhani, A.: Machine learning approaches for combating distributed denial of service attacks in modern networking environments. IEEE Access 9, 42236–42264 (2021)
Bernabé-Sánchez, I., Díaz-Sánchez, D., Muñoz-Organero, M.: Specification and unattended deployment of home networks at the edge of the network. IEEE Trans. Consum. Electron. 66(4), 279–288 (2020). https://doi.org/10.1109/TCE.2020.3018543
Bojović, P., Bašičević, I., Ocovaj, S., Popović, M.: A practical approach to detection of distributed denial-of-service attacks using a hybrid detection method. Comput. Electr. Eng. 73, 84–96 (2019)
Chaudhary, D., Bhushan, K., Gupta, B.B.: Survey on DDOs attacks and defense mechanisms in cloud and fog computing. Int. J. E-Serv. Mob. Appl. (IJESMA) 10(3), 61–83 (2018)
Cui, J., Long, J., Min, E., Liu, Q., Li, Q.: Comparative study of CNN and RNN for deep learning based intrusion detection system. In: Sun, X., Pan, Z., Bertino, E. (eds.) ICCCS 2018. LNCS, vol. 11067, pp. 159–170. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00018-9_15
Doshi, K., Yilmaz, Y., Uludag, S.: Timely detection and mitigation of stealthy DDOs attacks via IoT networks. IEEE Trans. Dependable Secure Comput. 18, 2164–2176 (2021)
Doshi, R., Apthorpe, N., Feamster, N.: Machine learning DDOs detection for consumer internet of things devices. In: 2018 IEEE Security and Privacy Workshops (SPW), pp. 29–35. IEEE (2018)
Fang, F., Cai, Z., Zhao, Q., Lin, J., Zhu, M.: Adaptive technique for real-time DDOs detection and defense using spark streaming. J. Frontiers Comput. Sci. Technol. 10(5), 601–611 (2016)
Gao, L., Luan, T.H., Yu, S., Zhou, W., Liu, B.: FogRoute: DTN-based data dissemination model in fog computing. IEEE Internet Things J. 4(1), 225–235 (2016)
Gupta, B.B., Li, K.C., Leung, V.C., Psannis, K.E., Yamaguchi, S., et al.: Blockchain-assisted secure fine-grained searchable encryption for a cloud-based healthcare cyber-physical system. IEEE/CAA J. Automatica Sinica 8(12), 1877–1890 (2021)
Haraty, R., Kaddoura, S., Zekri, A.: Recovery of business intelligence systems: towards guaranteed continuity of patient centric healthcare systems through a matrix-based recovery approach. Telematics Inform. 35(4), 801–814 (2018). https://doi.org/10.1016/j.tele.2017.12.010
Herrera, H.A., Rivas, W.R., Kumar, S.: Evaluation of internet connectivity under distributed denial of service attacks from botnets of varying magnitudes. In: 2018 1st International Conference on Data Intelligence and Security (ICDIS), pp. 123–126. IEEE (2018)
Jia, Y., Zhong, F., Alrawais, A., Gong, B., Cheng, X.: Flowguard: an intelligent edge defense mechanism against IoT DDOs attacks. IEEE Internet Things J. 7(10), 9552–9562 (2020)
Kaddoura, S., Haraty, R., Al Kontar, K., Alfandi, O.: A parallelized database damage assessment approach after cyberattack for healthcare systems. Future Internet 13(4), 90 (2021). https://doi.org/10.3390/fi13040090
Kalkan, K., Altay, L., Gür, G., Alagöz, F.: JESS: joint entropy-based DDOs defense scheme in SDN. IEEE J. Sel. Areas Commun. 36(10), 2358–2372 (2018)
Khan, W.Z., Aalsalem, M.Y., Khan, M.K.: Communal acts of IoT consumers: a potential threat to security and privacy. IEEE Trans. Consum. Electron. 65(1), 64–72 (2019). https://doi.org/10.1109/TCE.2018.2880338
Koay, A., Chen, A., Welch, I., Seah, W.K.: A new multi classifier system using entropy-based features in DDOs attack detection. In: 2018 International Conference on Information Networking (ICOIN), pp. 162–167. IEEE (2018)
Kumar, P., Tripathi, M., Nehra, A., Conti, M., Lal, C.: SAFETY: early detection and mitigation of TCP SYN flood utilizing entropy in SDN. IEEE Trans. Netw. Serv. Manage. 15(4), 1545–1559 (2018)
Kurt, M.N., Yilmaz, Y., Wang, X.: Real-time nonparametric anomaly detection in high-dimensional settings. IEEE Trans. Pattern Anal. Mach. Intell. 43, 2463–2479 (2020)
Li, J., Liu, M., Xue, Z., Fan, X., He, X.: RTVD: A real-time volumetric detection scheme for DDOs in the internet of things. IEEE Access 8, 36191–36201 (2020)
Li, J., Xue, Z.: Distributed threat intelligence sharing system: a new sight of p2p botnet detection. In: 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS), pp. 1–6. IEEE (2019)
Liu, M., Xue, Z., He, X., Chen, J.: Cyberthreat-intelligence information sharing: enhancing collaborative security. IEEE Consum. Electron. Mag. 8(3), 17–22 (2019)
Mahjabin, T., Xiao, Y., Li, T., Chen, C.P.: Load distributed and benign-bot mitigation methods for IoT DNS flood attacks. IEEE Internet Things J. 7(2), 986–1000 (2019)
Mahmud, R., Kotagiri, R., Buyya, R.: Fog computing: a taxonomy, survey and future directions. In: Di Martino, B., Li, K.-C., Yang, L.T., Esposito, A. (eds.) Internet of Everything. IT, pp. 103–130. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-5861-5_5
Meidan, Y., et al.: N-BAIOT-network-based detection of IoT botnet attacks using deep autoencoders. IEEE Pervasive Comput. 17(3), 12–22 (2018)
Min, E., Long, J., Liu, Q., Cui, J., Chen, W.: TR-IDS: anomaly-based intrusion detection through text-convolutional neural network and random forest. Secur. Commun. Netw. 2018 (2018)
Mustapha, H., Alghamdi, A.M.: DDOs attacks on the internet of things and their prevention methods. In: Proceedings of the 2nd International Conference on Future Networks and Distributed Systems, pp. 1–5 (2018)
Naha, R.K., et al.: Fog computing: survey of trends, architectures, requirements, and research directions. IEEE Access 6, 47980–48009 (2018). https://doi.org/10.1109/ACCESS.2018.2866491
Nõmm, S., Bahşi, H.: Unsupervised anomaly based botnet detection in IoT networks. In: 2018 17th IEEE international conference on machine learning and applications (ICMLA), pp. 1048–1053. IEEE (2018)
Qing-Tao, W., Zhi-qing, S.: Detecting DDOs attacks against web server using time series analysis. Wuhan Univ. J. Nat. Sci. 11(1), 175–180 (2006)
Ray, P.P., Thapa, N., Dash, D.: Implementation and performance analysis of interoperable and heterogeneous IoT-edge gateway for pervasive wellness care. IEEE Trans. Consum. Electron. 65(4), 464–473 (2019). https://doi.org/10.1109/TCE.2019.2939494
Roy, K.C., Chen, Q.: DeepRan: attention-based BiLSTM and CRF for ransomware early detection and classification. Inf. Syst. Frontiers 23, 1–17 (2020)
Shah, S.B.I., Anbar, M., Al-Ani, A., Al-Ani, A.K.: Hybridizing entropy based mechanism with adaptive threshold algorithm to detect RA flooding attack in IPv6 networks. In: Computational Science and Technology. LNEE, vol. 481, pp. 315–323. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-2622-6_31
She, C., Wen, W., Lin, Z., Zheng, K.: Application-layer DDOs detection based on a one-class support vector machine. Int. J. Netw. Secur. Appl. (IJNSA) 9(1), 13–24 (2017)
Tewari, A., Gupta, B.B.: Secure timestamp-based mutual authentication protocol for IoT devices using RFID tags. Int. J. Semant. Web Inf. Syst. (IJSWIS) 16(3), 20–34 (2020)
Varga, A., Hornig, R.: An Overview of the OMNeT++ Simulation Environment (2008). https://doi.org/10.1145/1416222.1416290
Vishwakarma, R., Jain, A.K.: A honeypot with machine learning based detection framework for defending IoT based botnet DDOs attacks. In: 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), pp. 1019–1024. IEEE (2019)
Wahab, O., Bentahar, J., Otrok, H., Mourad, A.: Optimal load distribution for the detection of VM-based DDOs attacks in the cloud. IEEE Trans. Serv. Comput. 13(1), 114–129 (2020). https://doi.org/10.1109/TSC.2017.2694426
Yilmaz, Y.: Online nonparametric anomaly detection based on geometric entropy minimization. In: 2017 IEEE International Symposium on Information Theory (ISIT), pp. 3010–3014. IEEE (2017)
Yunis, M., Markarian, C., El-Kassar, A.N.: A conceptual model for sustainable adoption of ehealth: role of digital transformation culture and healthcare provider’s readiness, vol. 2, pp. 179–184 (2020)
Zhang, X., Upton, O., Beebe, N.L., Choo, K.K.R.: Iot botnet forensics: a comprehensive digital forensic case study on mirai botnet servers. Forensic Sci. Int. Digit. Investig. 32, 300926 (2020)
Zhao, Y., Zhang, W., Feng, Y., Yu, B.: A classification detection algorithm based on joint entropy vector against application-layer DDOs attack. Secur. Commun. Netw. 2018 (2018)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gupta, B.B., Chui, K.T., Arya, V., Gaurav, A. (2023). A Novel Approach of Securing Medical Cyber Physical Systems (MCPS) from DDoS Attacks. In: Hsu, CH., Xu, M., Cao, H., Baghban, H., Shawkat Ali, A.B.M. (eds) Big Data Intelligence and Computing. DataCom 2022. Lecture Notes in Computer Science, vol 13864. Springer, Singapore. https://doi.org/10.1007/978-981-99-2233-8_11
Download citation
DOI: https://doi.org/10.1007/978-981-99-2233-8_11
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-2232-1
Online ISBN: 978-981-99-2233-8
eBook Packages: Computer ScienceComputer Science (R0)