Skip to main content
SpringerLink
Log in

A Novel Approach for RPL Based One and Multi-attacker Flood Attack Analysis

  • Conference paper
  • First Online:
Advances in Intelligent Manufacturing and Service System Informatics (IMSS 2023)

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

  • 386 Accesses

Abstract

The Internet of Things (IoT) encompasses a vast network of interconnected devices, vehicles, appliances, and other items with embedded electronics, software, sensors, and connectivity, allowing them to collect and exchange data. However, the growing number of connected devices raises concerns about IoT cybersecurity. Ensuring the security of sensitive information transmitted by IoT devices is crucial to prevent data breaches and cyberattacks. IoT cybersecurity involves employing various technologies, standards, and best practices, including encryption, firewalls, and multi-factor authentication. Although IoT offers numerous benefits, addressing its security challenges is essential. In this study, a flood attack, a significant threat to IoT devices, was executed to assess the system’s impact. A reference model without the attack was used to analyze network traffic involving single or multiple attackers. To prevent additional load on the operational system, network packets were mirrored via the cloud and transferred to artificial intelligence (AI) and forensic analysis tools in real-time. The study aimed to ensure continuity, a vital aspect of IoT system cybersecurity, by detecting the attacker using AI and analyzing real-time data with forensic analysis tools for continuous network monitoring. Various AI algorithms were evaluated for attacker detection, and the detection process proved successful.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Latif, S., Idrees, Z., Zou, Z., Ahmad, J.: DRaNN: a deep random neural network model for intrusion detection in industrial IoT. In: 2020 International Conference on UK-China Emerging Technologies (UCET), pp. 1–4. IEEE (2020)

    Google Scholar 

  2. Morgan, S.: Global ransomware damage costs predicted to hit $11.5 billion by 2019. Cybercrime Magazine (2018). https://cybersecurityventures.com/ransomware-damage-report-2017-part-2/. Accessed 11 Feb 2023

  3. Wu, M., Song, Z., Moon, Y.B.: Detecting cyber-physical attacks in CyberManufacturing systems with machine learning methods. J. Intell. Manuf. 30, 1111–1123 (2019). https://doi.org/10.1007/s10845-017-1315-5

    Article  Google Scholar 

  4. Narasimhan, S., Biswas, G.: Model-based diagnosis of hybrid systems. IEEE Trans. Syst. Man, Cybern.-Part A: Syst. Hum. 37(3), 348–361 (2007)

    Article  Google Scholar 

  5. Pasqualetti, F., Dörfler, F., Bullo, F.: Cyber-physical attacks in power networks: models, fundamental limitations and monitor design. In: 2011 50th IEEE Conference on Decision and Control and European Control Conference, pp. 2195–2201. IEEE (2011)

    Google Scholar 

  6. Teixeira, A., Pérez, D., Sandberg, H., Johansson, K.H.: Attack models and scenarios for networked control systems. In: Proceedings of 1st International Conference on High Confidence Networked System, pp. 55–64 (2012)

    Google Scholar 

  7. Boateng, E.A.: Anomaly detection for industrial control systems based on neural networks with one-class objective function. In: Proceedings of Student Research Creative Inquiry Day, vol. 5 (2021). https://publish.tntech.edu/index.php/PSRCI/article/view/810/321. Accessed 11 Feb 2023

  8. Zhao, F., Koutsoukos, X., Haussecker, H., Reich, J., Cheung, P.: Monitoring and fault diagnosis of hybrid systems. IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society. 35, 1225-1240 (2006). https://doi.org/10.1109/TSMCB.2005.850178

  9. Abidi, M.H., Alkhalefah, H., Umer, U.: Fuzzy harmony search based optimal control strategy for wireless cyber physical system with industry 4.0. J. Intell. Manuf. 33, 1795–1812 (2022). https://doi.org/10.1007/s10845-021-01757-4

    Article  Google Scholar 

  10. Colabianchi, S., Costantino, F., Di Gravio, G., Nonino, F., Patriarca, R.: Discussing resilience in the context of cyber physical systems. Comput. Ind. Eng. 160, 107534 (2021). https://doi.org/10.1016/j.cie.2021.107534

    Article  Google Scholar 

  11. Lambán, M.P., Morella, P., Royo, J., Sánchez, J.C.: Using industry 4.0 to face the challenges of predictive maintenance: a key performance indicators development in a cyber physical system. Comput. Ind. Eng. 171, 108400 (2022). https://doi.org/10.1016/j.cie.2022.108400

    Article  Google Scholar 

  12. Boateng, E.A., Bruce, J.W., Talbert, D.A.: Anomaly detection for a water treatment system based on one-class neural network. IEEE Access 10, 115179–115191 (2022). https://doi.org/10.1109/ACCESS.2022.3218624

    Article  Google Scholar 

  13. Muna, A.H., Moustafa, N., Sitnikova, E.: Identification of malicious activities in industrial internet of things based on deep learning models. J. Inf. Secur. Appl. 41, 1–11 (2018)

    Google Scholar 

  14. Kim, H., Lee, K.: IIoT malware detection using edge computing and deep learning for cybersecurity in smart factories. Appl. Sci. 12(15), 7679 (2022). https://doi.org/10.3390/app12157679

    Article  Google Scholar 

  15. Yang, K., Li, Q., Lin, X., Chen, X., Sun, L.: iFinger: intrusion detection in industrial control systems via register-based fingerprinting. IEEE J. Sel. Areas Commun. 38(5), 955–967 (2020)

    Article  Google Scholar 

  16. Di, W., Jiang, Z., Xie, X., Wei, X., Weiren, Y., Li, R.: LSTM learning with Bayesian and Gaussian processing for anomaly detection in industrial IoT. IEEE Trans. Industr. Inf. 16(8), 5244–5253 (2019)

    Google Scholar 

  17. Leyi, S., Hongqiang, Z., Yihao, L., Jia, L.: Intrusion detection of industrial control system based on correlation information entropy and CNN-BiLSTM. J. Comput. Res. Dev. 56(11), 2330–2338 (2019). https://doi.org/10.7544/issn1000-1239.2019.20190376

    Article  Google Scholar 

  18. Chu, A., Lai, Y., Liu, J.: Industrial control intrusion detection approach based on multiclassification GoogLeNet-LSTM model. Secur. Commun. Netw. 2019, 1–11 (2019)

    Article  Google Scholar 

  19. Rachmadi, S., Mandala, S., Oktaria, D.: Detection of DoS attack using AdaBoost algorithm on IoT system. In: Proceedings of the 2021 International Conference on Data Science and Its Applications (ICoDSA’21). IEEE, pp. 28–33. Los Alamitos, CA (2021)

    Google Scholar 

  20. Wahla, A.H., Chen, L., Wang, Y., Chen, R., Fan, W.: Automatic wireless signal classification in multimedia Internet of Things: an adaptive boosting enabled approach. IEEE Access 7(2019), 160334–160344 (2019)

    Article  Google Scholar 

  21. Mohammed, A.S., Anthi, E., Rana, O., Saxena, N., Burnap, P.: Detection and mitigation of field flooding attacks on oil and gas critical infrastructure communication. Comput. Secur. 124, 103007 (2023)

    Article  Google Scholar 

  22. Nedeljkovic, D., Jakovljevic, Z.: CNN based method for the development of cyber-attacks detection algorithms in industrial control systems. Comput. Secur. 114, 102585 (2022)

    Article  Google Scholar 

  23. Shafiq, M., Tian, Z., Sun, Y., Du, X., Guizani, M.: Selection of effective machine learning algorithm and Bot-IoT attacks traffic identification for Internet of Things in smart city. Future Gener. Comput. Syst. 107, 433–442 (2020). https://doi.org/10.1016/j.future.2020.02.017

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Software Engineering Department, Istanbul Gelisim University, Istanbul, Turkey

    Serkan Gonen

Authors
  1. Serkan Gonen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Serkan Gonen .

Editor information

Editors and Affiliations

  1. Istanbul Medipol University, Istanbul, Türkiye

    Zekâi Şen

  2. Department of Industrial Engineering, Sakarya University, Serdivan, Sakarya, Türkiye

    Özer Uygun

  3. Faculty of Applied Sciences, Sakarya University of Applied Sciences, Kaynarca, Sakarya, Türkiye

    Caner Erden

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gonen, S. (2024). A Novel Approach for RPL Based One and Multi-attacker Flood Attack Analysis. In: Şen, Z., Uygun, Ö., Erden, C. (eds) Advances in Intelligent Manufacturing and Service System Informatics. IMSS 2023. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-99-6062-0_42

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-6062-0_42

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-6061-3

  • Online ISBN: 978-981-99-6062-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation