Skip to main content
SpringerLink
Log in

Efficient time-delay attack detection based on node pruning and model fusion in IoT networks

  • Published:
Peer-to-Peer Networking and Applications Aims and scope Submit manuscript

Abstract

IoT devices are vulnerable to various attacks because they are resource-limited. This paper introduces a novel type of attack called time-delay attack. The malicious nodes delay packet forwarding by extending the processing time of packets, thus affecting the performance and availability of the network. This attack is very stealthy and difficult to detect because it does not violate any communication protocol. To the best of our knowledge, how to detect the time-delay attack in IoT networks is still an open problem. We first propose a machine learning-based baseline algorithm to detect the time-delay attack. It models the system features of each node and the forwarding time of packets to detect whether a node is malicious or not. However, the baseline algorithm needs to detect all nodes in the network, which causes unnecessary resource consumption. Moreover, using a single model in the baseline algorithm does not have high robustness. To reduce the overhead and improve the detection performance, we design an efficient Detection algorithm based on Node pruning and Model fusion (DNM). DNM uses node pruning to filter out suspected nodes from all nodes. The suspected nodes are then detected according to a fusion model. We conduct experimental evaluations based on the Cooja network simulator. The experimental results show that baseline and DNM possess close to 90% accuracy, and DNM significantly outperforms other algorithms with an average F1-score of 0.85.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Aheleroff S, Xu X, Lu Y, Aristizabal M, Velásquez JP, Joa B, Valencia Y (2020) Iot-enabled smart appliances under industry 4.0: A case study. Adv Eng Inform 43. https://doi.org/10.1016/j.aei.2020.101043

  2. Viswanath SK, Yuen C, Tushar W, Li W-T, Wen C-K, Hu K, Chen C, Liu X (2016) System design of the internet of things for residential smart grid. IEEE Wirel Commun 23(5):90–98. https://doi.org/10.1109/MWC.2016.7721747

    Article  Google Scholar 

  3. Fang S, Da Xu L, Zhu Y, Ahati J, Pei H, Yan J, Liu Z (2014) An integrated system for regional environmental monitoring and management based on internet of things. IEEE Trans Industr Inf 10(2):1596–1605. https://doi.org/10.1109/TII.2014.2302638

    Article  Google Scholar 

  4. Wang D, Chen D, Song B, Guizani N, Yu X, Du X (2018) From iot to 5g i-iot: The next generation iot-based intelligent algorithms and 5g technologies. IEEE Commun Mag 56(10):114–120. https://doi.org/10.1109/MCOM.2018.1701310

    Article  Google Scholar 

  5. Pokhrel SR, Vu HL, Cricenti AL (2019) Adaptive admission control for iot applications in home wifi networks. IEEE Trans Mob Comput 19(12):2731–2742. https://doi.org/10.1109/TMC.2019.2935719

    Article  Google Scholar 

  6. Li Y, Chi Z, Liu X, Zhu T (2018). Passive-zigbee: Enabling zigbee communication in iot networks with 1000x+ less power consumption. In: Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems, pp. 159–171. https://doi.org/10.1145/3274783.3274846

  7. Kim H-S, Ko J, Culler DE, Paek J (2017) Challenging the ipv6 routing protocol for low-power and lossy networks (rpl): A survey. IEEE Commun Surv Tutorials 19(4):2502–2525. https://doi.org/10.1109/COMST.2017.2751617

    Article  Google Scholar 

  8. Deogirikar J, Vidhate A (2017) Security attacks in iot: A survey. In: 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), pp. 32–37. https://doi.org/10.1109/I-SMAC.2017.8058363

  9. Stellios I, Kotzanikolaou P, Psarakis M, Alcaraz C, Lopez J (2018) A survey of iot-enabled cyberattacks: Assessing attack paths to critical infrastructures and services. IEEE Commun Surv Tutorials 20(4):3453–3495. https://doi.org/10.1109/COMST.2018.2855563

    Article  Google Scholar 

  10. Prathapchandran K, Janani T (2021) A trust aware security mechanism to detect sinkhole attack in rpl-based iot environment using random forest-rftrust. Comput Netw 198:108413. https://doi.org/10.1016/j.comnet.2021.108413

  11. Divya K, Jaipriya S, Anitha G, Malathy S, Maheswar R (2018) An energy efficient technique for time sensitive application using mc-wsn. In: 2018 2nd International Conference on Inventive Systems and Control (ICISC), pp. 1451–1455. https://doi.org/10.1109/ICISC.2018.8399048

  12. Poe WY, Schmitt JB (2008) Placing multiple sinks in time-sensitive wireless sensor networks using a genetic algorithm. In: 14th GI/ITG Conference-Measurement, Modelling and Evalutation of Computer and Communication Systems, pp. 1–15

  13. Korala H, Georgakopoulos D, Jayaraman PP, Yavari A (2022) A survey of techniques for fulfilling the time-bound requirements of time-sensitive iot applications. ACM Comput Surv. https://doi.org/10.1145/3510411

  14. Song H, Zhu S, Cao G (2007) Attack-resilient time synchronization for wireless sensor networks. Ad Hoc Netw 5(1):112–125. https://doi.org/10.1016/j.adhoc.2006.05.016

    Article  Google Scholar 

  15. Lee JH, Shin J, Realff MJ (2018) Machine learning: Overview of the recent progresses and implications for the process systems engineering field. Comput Chem Eng 114:111–121. https://doi.org/10.1016/j.compchemeng.2017.10.008

    Article  Google Scholar 

  16. Chen Z, Liu J, Shen Y, Simsek M, Kantarci B, Mouftah HT, Djukic P (2022) Machine learning-enabled iot security: Open issues and challenges under advanced persistent threats. ACM Comput Surv 55(5):1–37. https://doi.org/10.1145/3530812

    Article  Google Scholar 

  17. Huang X, Wu Y (2022) Identify selective forwarding attacks using danger model: Promote the detection accuracy in wireless sensor networks. IEEE Sens J 22(10):9997–10008. https://doi.org/10.1109/JSEN.2022.3166601

    Article  Google Scholar 

  18. Ding J, Wang H, Wu Y (2022) The detection scheme against selective forwarding of smart malicious nodes with reinforcement learning in wireless sensor networks. IEEE Sens J 22(13):13696–13706. https://doi.org/10.1109/JSEN.2022.3176462

    Article  Google Scholar 

  19. Chen X, Feng W, Luo Y, Shen M, Ge N, Wang X (2022) Defending against link flooding attacks in internet of things: A bayesian game approach. IEEE Internet Things J 9(1):117–128. https://doi.org/10.1109/JIOT.2021.3093538

    Article  Google Scholar 

  20. Srinivas TAS, Manivannan S (2020) Prevention of hello flood attack in iot using combination of deep learning with improved rider optimization algorithm. Comput Commun 163:162–175. https://doi.org/10.1016/j.comcom.2020.03.031

    Article  Google Scholar 

  21. Teng Z, Du C, Li M, Zhang H, Zhu W (2022) A wormhole attack detection algorithm integrated with the node trust optimization model in wsns. IEEE Sens J 22(7):7361–7370. https://doi.org/10.1109/JSEN.2022.3152841

    Article  Google Scholar 

  22. Pu C, Choo K-KR (2022) Lightweight sybil attack detection in iot based on bloom filter and physical unclonable function. Comput Secur 113:102541. https://doi.org/10.1016/j.cose.2021.102541

  23. Alghamdi R, Bellaiche M (2023) A cascaded federated deep learning based framework for detecting wormhole attacks in iot networks. Comput Secur 125:103014. https://doi.org/10.1016/j.cose.2022.103014

  24. Kim J-D, Ko M, Chung J-M (2022) Physical identification based trust path routing against sybil attacks on rpl in iot networks. IEEE Wireless Commun Lett 11(5):1102–1106. https://doi.org/10.1109/LWC.2022.3157831

    Article  Google Scholar 

  25. Moradi M, Jahangir AH (2021) A new delay attack detection algorithm for ptp network in power substation. Int J Electr Power Energy Syst 133:107226. https://doi.org/10.1016/j.ijepes.2021.107226

  26. Moussa B, Kassouf M, Hadjidj R, Debbabi M, Assi C (2020) An extension to the precision time protocol (ptp) to enable the detection of cyber attacks. IEEE Trans Industr Inf 16(1):18–27. https://doi.org/10.1109/TII.2019.2943913

    Article  Google Scholar 

  27. Wang J, Peng C (2017) Analysis of time delay attacks against power grid stability. In: Proceedings of the 2nd Workshop on Cyber-Physical Security and Resilience in Smart Grids, pp. 67–72. https://doi.org/10.1145/3055386.3055392

  28. De Pace G, Wang Z, Benin J, He H, Sun Y (2020) Evaluation of communication delay based attack against the smart grid. In: 2020 IEEE Kansas Power and Energy Conference (KPEC), pp. 1–6. https://doi.org/10.1109/KPEC47870.2020.9167543

  29. Lou X, Tran, C, Yau DK, Tan R, Ng H, Fu, TZ, Winslett M (2019) Learning-based time delay attack characterization for cyber-physical systems. In: 2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), pp. 1–6 . https://doi.org/10.1109/SmartGridComm.2019.8909732

  30. Abbasspour A, Sargolzaei A, Victorio M, Khoshavi N (2020) A neural network-based approach for detection of time delay switch attack on networked control systems. Procedia Computer Science 168:279–288. https://doi.org/10.1016/j.procs.2020.02.250

    Article  Google Scholar 

  31. Ganesh P, Lou X, Chen Y, Tan R, Yau DKY, Chen D, Winslett M (2021) Learning-based simultaneous detection and characterization of time delay attack in cyber-physical systems. IEEE Trans Smart Grid 12(4):3581–3593. https://doi.org/10.1109/TSG.2021.3058682

    Article  Google Scholar 

  32. Sargolzaei A, Yen KK, Abdelghani MN (2015) Preventing time-delay switch attack on load frequency control in distributed power systems. IEEE Trans Smart Grid 7(2):1176–1185. https://doi.org/10.1109/TSG.2015.2503429

    Article  Google Scholar 

  33. Victorio M, Sargolzaei A, Khalghani MR (2021) A secure control design for networked control systems with linear dynamics under a time-delay switch attack. Electronics 10(3):322. https://doi.org/10.3390/electronics10030322

    Article  Google Scholar 

  34. Altaf A, Abbas H, Iqbal F, Khan MMZM, Rauf A, Kanwal T (2021) Mitigating service-oriented attacks using context-based trust for smart cities in iot networks. J Syst Archit 115:102028. https://doi.org/10.1016/j.sysarc.2021.102028

  35. Mabodi K, Yusefi M, Zandiyan S, Irankhah L, Fotohi R (2020) Multi-level trust-based intelligence schema for securing of internet of things (iot) against security threats using cryptographic authentication. J Supercomput 76(9):7081–7106. https://doi.org/10.1007/s11227-019-03137-5

    Article  Google Scholar 

  36. Liu L, Ma Z, Meng W (2019) Detection of multiple-mix-attack malicious nodes using perceptron-based trust in iot networks. Futur Gener Comput Syst 101:865–879. https://doi.org/10.1016/j.future.2019.07.021

    Article  Google Scholar 

  37. Liu L, Xu X, Liu Y, Ma Z, Peng J (2021) A detection framework against cpma attack based on trust evaluation and machine learning in iot network. IEEE Internet Things J 8(20):15249–15258. https://doi.org/10.1109/JIOT.2020.3047642

    Article  Google Scholar 

  38. Ma Z, Liu L, Meng W (2020) Towards multiple-mix-attack detection via consensus-based trust management in iot networks. Comput Secur 96:101898. https://doi.org/10.1016/j.cose.2020.101898

  39. Singh M, Sardar AR, Majumder K, Sarkar SK (2017) A lightweight trust mechanism and overhead analysis for clustered wsn. IETE J Res 63(3):297–308. https://doi.org/10.1080/03772063.2017.1284613

    Article  Google Scholar 

  40. Poongodi T, Khan MS, Patan R, Gandomi AH, Balusamy B (2019) Robust defense scheme against selective drop attack in wireless ad hoc networks. IEEE Access 7:18409–18419. https://doi.org/10.1109/ACCESS.2019.2896001

    Article  Google Scholar 

  41. Eskandari M, Janjua ZH, Vecchio M, Antonelli F (2020) Passban ids: An intelligent anomaly-based intrusion detection system for iot edge devices. IEEE Internet Things J 7(8):6882–6897. https://doi.org/10.1109/JIOT.2020.2970501

    Article  Google Scholar 

  42. Nguyen TD, Marchal, S, Miettinen M, Fereidooni H, Asokan N, Sadeghi AR (2019) Dïot: A federated self-learning anomaly detection system for iot. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 756–767. https://doi.org/10.1109/ICDCS.2019.00080

  43. Moussa B, Debbabi M, Assi C (2016) A detection and mitigation model for ptp delay attack in an iec 61850 substation. IEEE Trans Smart Grid 9(5):3954–3965. https://doi.org/10.1109/TSG.2016.2644618

    Article  Google Scholar 

  44. Suhail S, Hussain R, Abdellatif M, Pandey SR, Khan A, Hong CS (2020) Provenance-enabled packet path tracing in the rpl-based internet of things. Comput Netw 173:107189. https://doi.org/10.1016/j.comnet.2020.107189

  45. Rousseeuw PJ, Croux C (1993) Alternatives to the median absolute deviation. J Am Stat Assoc 88(424):1273–1283. https://doi.org/10.1080/01621459.1993.10476408

    Article  MathSciNet  MATH  Google Scholar 

  46. Chen Z, Song S, Wei Z, Fang J, Long J (2021) Approximating median absolute deviation with bounded error. Proceedings of the VLDB Endowment 14(11):2114–2126. https://doi.org/10.14778/3476249.3476266

  47. Ganesh P, Lou X, Chen Y, Tan R, Yau DK, Chen D, Winslett M (2021) Learning-based simultaneous detection and characterization of time delay attack in cyber-physical systems. IEEE Trans Smart Grid 12(4):3581–3593. https://doi.org/10.1109/TSG.2021.3058682

    Article  Google Scholar 

  48. Sak H, Senior AW, Beaufays F (2014) Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In: INTERSPEECH, pp. 338–342

  49. Ganti RK, Jayachandran P, Luo H, Abdelzaher TF (2006) Datalink streaming in wireless sensor networks. In: Proceedings of the 4th International Conference on Embedded Networked Sensor Systems, pp. 209–222. http://doi.org/10.1145/1182807.1182829

  50. Osterlind F, Dunkels A, Eriksson, J, Finne N, Voigt T (2006) Cross-level sensor network simulation with cooja. In: Proceedings. 2006 31st IEEE Conference on Local Computer Networks, pp. 641–648. https://doi.org/10.1109/LCN.2006.322172

Download references

Funding

This work is supported by the National Key R &D Program of China under No. 2021YFB2700500 and 2021YFB2700502, the Open Fund of Key Laboratory of Civil Aviation Smart Airport Theory and System, Civil Aviation University of China under No. SATS202206, the National Natural Science Foundation of China under No. U20B2050, Public Service Platform for Basic Software and Hardware Supply Chain Guarantee under No. TC210804A.

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 29 Jiangjun RD, Nanjing, 211106, Jiangsu, China

    Wenjie Zhao, Wenbin Zhai, Liang Liu & Yulei Liu

  2. The Fifth Electronics Research Institute of the Ministry of Industry and Information Technology, 78 West Zhucun RD, Guangzhou, 511370, Guangdong, China

    Yu Wang

Authors
  1. Wenjie Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenbin Zhai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Liang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yulei Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Wenjie Zhao: Conceptualization, Data curation, Software, Formal analysis, Methodology, Writing - original draft, Writing - review & editing. Yu Wang: Investigation, Methodology, Software, Writing - original draft. Wenbin Zhai: Conceptualization, Resources, Funding acquisition, Project administration, Supervision, Writing - review & editing. Liang Liu: Methodology, Formal analysis, Supervision, Writing - review & editing. Yulei Liu: Writing - review & editing.

Corresponding author

Correspondence to Liang Liu.

Ethics declarations

Ethics approval

Not applicable.

Consent to publish

All of the authors have approved the contents of this paper and have agreed to the submission policies of Peer-to-Peer Networking and Applications.

Conflict of interest

We declare that we have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, W., Wang, Y., Zhai, W. et al. Efficient time-delay attack detection based on node pruning and model fusion in IoT networks. Peer-to-Peer Netw. Appl. 16, 1286–1309 (2023). https://doi.org/10.1007/s12083-023-01477-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-023-01477-x

Keywords

Search

Navigation