Abstract
The proliferation of Internet of Things (IoT) devices has revolutionized various domains, but it has also brought forth numerous security challenges. One of the most concerning threats is the emergence of hybrid attacks, which combine multiple attack vectors to exploit vulnerabilities in IoT networks. Existing security mechanisms often struggle to effectively predict and detect these sophisticated hybrid attacks, leading to compromised system integrity and data confidentiality. In this paper, we propose robust learning approach, named HybridRobustNet (HRN), for predicting and detecting hybrid attacks over IoT networks. HRN integrates machine learning algorithms, deep neural networks, and ensemble techniques to achieve enhanced detection accuracy and resilience against evolving hybrid attack patterns. By leveraging a diverse set of features, including network traffic patterns, device behavior, and communication characteristics, HRN effectively captures the complex relationships and dependencies between various attack components. Furthermore, the proposed approach incorporates real-time adaptive learning mechanisms, enabling it to dynamically adapt to new attack strategies and mitigate false positives. To evaluate the effectiveness of HRN, extensive experiments were conducted on a realistic IoT testbed comprising heterogeneous devices and attack scenarios. The results demonstrate that HRN outperforms state-of-the-art approaches in terms of attack detection accuracy, robustness against evasion techniques, and low false positive rates. Additionally, its computational efficiency makes it suitable for deployment in resource-constrained IoT environments. The contributions of this work are twofold. Firstly, it addresses the pressing need for robust detection mechanisms against hybrid attacks, which can have severe consequences for IoT networks. Secondly, it introduces a unique and adaptive learning approach, HRN, which exhibits superior performance and adaptability in the face of emerging attack strategies. The findings presented in this article provide valuable insights into the design of effective security mechanisms for IoT networks and pave the way for future research in the field of hybrid attack detection.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10586-023-04248-8/MediaObjects/10586_2023_4248_Fig1_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10586-023-04248-8/MediaObjects/10586_2023_4248_Fig2_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10586-023-04248-8/MediaObjects/10586_2023_4248_Fig3_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10586-023-04248-8/MediaObjects/10586_2023_4248_Fig4_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10586-023-04248-8/MediaObjects/10586_2023_4248_Fig5_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10586-023-04248-8/MediaObjects/10586_2023_4248_Fig6_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs10586-023-04248-8/MediaObjects/10586_2023_4248_Fig7_HTML.png)
Similar content being viewed by others
References
Kouicem, D.E., Bouabdallah, A., Lakhlef, H.: Internet of things security: A top-down survey. Comput. Networks. 141, 199–221 (2018). https://doi.org/10.1016/j.comnet.2018.03.012
Gupta, B.B., Quamara, M.: An overview of internet of things (IoT): Architectural aspects, challenges, and protocols. Concurr Comput. Pract. Exp. 32, 1–24 (2020). https://doi.org/10.1002/cpe.4946
Abhishek, N.V., Tandon, A., Lim, T.J., Sikdar, B.: A GLRT-Based mechanism for detecting Relay Misbehavior in Clustered IoT Networks. IEEE Trans. Inf. Forensics Secur. 15, 435–446 (2020). https://doi.org/10.1109/TIFS.2019.2922262
Wu, Y., Wei, D., Feng, J.: Network attacks detection methods based on deep learning techniques: A survey. Secur. Commun. Networks. (2020). (2020). https://doi.org/10.1155/2020/8872923
Liu, S., Lin, G., Han, Q.L., Wen, S., Zhang, J., Xiang, Y.: DeepBalance: Deep-learning and fuzzy oversampling for vulnerability detection. IEEE Trans. Fuzzy Syst. 28, 1329–1343 (2020). https://doi.org/10.1109/TFUZZ.2019.2958558
Moustafa, N., Choo, K.K.R., Radwan, I., Camtepe, S.: Outlier Dirichlet mixture mechanism: Adversarial statistical learning for Anomaly Detection in the fog. IEEE Trans. Inf. Forensics Secur. 14, 1975–1987 (2019). https://doi.org/10.1109/TIFS.2018.2890808
Michele, B., Pena, I., Angueira, P.: Threats and limitations of Terrestrial Broadcast Attacks. IEEE Trans. Broadcast. 64, 105–118 (2018). https://doi.org/10.1109/TBC.2017.2704538
Aledhari, M., Pierro, M., Di, Hefeida, M., Saeed, F.: IEEE Trans. Big Data. 7, 271–284 (2018). https://doi.org/10.1109/tbdata.2018.2805687 A Deep Learning-Based Data Minimization Algorithm for Fast and Secure Transfer of Big Genomic Datasets
Dinkel, H., Qian, Y., Yu, K.: Investigating raw Wave Deep neural networks for end-to-end Speaker Spoofing Detection. IEEE/ACM Trans. Audio Speech Lang. Process. 26, 2002–2014 (2018). https://doi.org/10.1109/TASLP.2018.2851155
Al-Turjman, F., Ever, E., Zahmatkesh, H.: Small cells in the forthcoming 5G/IoT: Traffic modelling and deployment overview. IEEE Commun. Surv. Tutorials. 21, 28–65 (2019). https://doi.org/10.1109/COMST.2018.2864779
Lohachab, A., Karambir, B.: Critical analysis of DDoS—An Emerging Security threat over IoT Networks. J. Commun. Inf. Networks. 3, 57–78 (2018). https://doi.org/10.1007/s41650-018-0022-5
Wang, K., Du, M., Maharjan, S., Sun, Y.: Strategic Honeypot Game Model for distributed denial of service Attacks in the Smart Grid. IEEE Trans. Smart Grid. 8, 2474–2482 (2017). https://doi.org/10.1109/TSG.2017.2670144
Diro, A.A., Chilamkurti, N.: Distributed Attack detection scheme using deep learning approach for internet of things. Futur. Gener. Comput. Syst. 82, 761–768 (2018). https://doi.org/10.1016/j.future.2017.08.043
Roy, S., Li, J., Choi, B.J., Bai, Y.: A lightweight supervised intrusion detection mechanism for IoT networks. Futur. Gener. Comput. Syst. 127, 276–285 (2022). https://doi.org/10.1016/j.future.2021.09.027
Saba, T., Rehman, A., Sadad, T., Kolivand, H., Bahaj, S.A.: Anomaly-based intrusion detection system for IoT networks through deep learning model. Comput. Electr. Eng. 99, 107810 (2022). https://doi.org/10.1016/j.compeleceng.2022.107810
Bostani, H., Sheikhan, M.: Hybrid of anomaly-based and specification-based IDS for internet of things using unsupervised OPF based on MapReduce approach. Comput. Commun. 98, 52–71 (2017). https://doi.org/10.1016/j.comcom.2016.12.001
Saif, S., Das, P., Biswas, S., Khari, M., Shanmuganathan, V.: HIIDS: Hybrid intelligent intrusion detection system empowered with machine learning and metaheuristic algorithms for application in IoT based healthcare. Microprocess Microsyst. 104622 (2022). https://doi.org/10.1016/j.micpro.2022.104622
Kumar, R., Malik, A., Ranga, V.: An intellectual intrusion detection system using hybrid Hunger games Search and Remora optimization algorithm for IoT wireless networks. Knowledge-Based Syst. 256, 109762 (2022). https://doi.org/10.1016/j.knosys.2022.109762
Yang, L., Ding, C., Wu, M., Wang, K.: Robust detection of false data injection Attacks for data aggregation in an internet of things-based environmental surveillance. Comput. Networks. 129, 410–428 (2017). https://doi.org/10.1016/j.comnet.2017.05.027
Mohamad Noor, M., binti, Hassan, W.H.: Current research on internet of things (IoT) security: A survey. Comput. Networks. 148, 283–294 (2019). https://doi.org/10.1016/j.comnet.2018.11.025
Turukmane, A.V., Devendiran, R.: M-MultiSVM: An efficient feature selection assisted Network Intrusion Detection System using machine learning. Comput. Secur. 137, 103587 (2023). https://doi.org/10.1016/j.cose.2023.103587
Saba, T., Sadad, T., Rehman, A., Mehmood, Z., Javaid, Q.: Intrusion detection system through Advance Machine Learning for the internet of things networks. IT Prof. 23, 58–64 (2021). https://doi.org/10.1109/MITP.2020.2992710
Zainudin, A., Ahakonye, L.A.C., Akter, R., Kim, D.S., Lee, J.M.: An efficient Hybrid-DNN for DDoS detection and classification in Software-defined IIoT networks. IEEE Internet Things J. 10, 8491–8504 (2023). https://doi.org/10.1109/JIOT.2022.3196942
Ramkumar, D., Annadurai, C., Nirmaladevi, K.: Continuous authentication consoles in mobile ad hoc network (MANET). Cluster Comput. 22, 7777–7786 (2019). https://doi.org/10.1007/s10586-017-1386-2
Hasan, T., Malik, J., Bibi, I., Khan, W.U., Al-Wesabi, F.N., Dev, K., Huang, G.: Securing Industrial Internet of things against Botnet Attacks using Hybrid Deep Learning Approach. IEEE Trans. Netw. Sci. Eng. PP. 1 (2022). https://doi.org/10.1109/TNSE.2022.3168533
Sattari, F., Farooqi, A.H., Qadir, Z., Raza, B., Nazari, H., Almutiry, M.: A Hybrid Deep Learning Approach for Bottleneck Detection in IoT. IEEE Access. 10, 77039–77053 (2022). https://doi.org/10.1109/ACCESS.2022.3188635
Javeed, D., Gao, T., Khan, M.T.: Sdn-enabled hybrid dl-driven framework for the detection of emerging cyber threats in iot. Electron. 10, 1–16 (2021). https://doi.org/10.3390/electronics10080918
Khan, M.A.: HCRNNIDS: Hybrid Convolutional recurrent neural. Multidiscip Digit. Publ Inst. (2021)
Faysal, J., Al, Mostafa, S.T., Tamanna, J.S., Mumenin, K.M., Arifin, M.M., Awal, M.A., Shome, A., Mostafa, S.S.: XGB-RF: A Hybrid Machine Learning Approach for IoT Intrusion Detection. Telecom. 3, 52–69 (2022). https://doi.org/10.3390/telecom3010003
Ramkumar, D., Annadurai, C., Nelson, I.: Iris-based continuous authentication in mobile ad hoc network. Concurr Comput. Pract. Exp. 34, 4–8 (2022). https://doi.org/10.1002/cpe.5542
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., Ayyash, M.: Internet of things: A Survey on Enabling technologies, protocols, and applications. IEEE Commun. Surv. Tutorials. 17, 2347–2376 (2015). https://doi.org/10.1109/COMST.2015.2444095
Sha, K., Wei, W., Yang, A., Wang, T., Shi, Z.: On security challenges and open issues in internet of things. Futur. Gener. Comput. Syst. 83, 326–337 (2018). https://doi.org/10.1016/j.future.2018.01.059
Author information
Authors and Affiliations
Contributions
Dr. D. Adhimuga Sivasakthi : Conceptualization, Methodology, and Project Administration.Dr. A Sathiyaraj: Data Collection, Pre-processing, and Model Implementation.Dr. Ramkumar Devendiran: Evaluation Metrics and Performance Analysis.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
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.
About this article
Cite this article
Sivasakthi, D.A., Sathiyaraj, A. & Devendiran, R. HybridRobustNet: enhancing detection of hybrid attacks in IoT networks through advanced learning approach. Cluster Comput 27, 5005–5019 (2024). https://doi.org/10.1007/s10586-023-04248-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-023-04248-8