Skip to main content
SpringerLink
Log in

Benchmarking the Bagging and Boosting (B & B) Algorithms for Modeling Optimized Autonomous Intrusion Detection Systems (AIDS)

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

The mapped mathematical models (MMMs) in machine learning are instrumental in obtaining segregated yet effective solutions for security-specific scenarios in intrusion detection systems (IDSs). Utilizing machine learning approaches amplifies the achievable security desired for communication among nodes constituting the IoT networks. The myriad of attacks threatening security and hindering advanced communication in IoT networks need capable ML approaches for detection. The bagging and boosting (B & B) algorithms minimize VBN (variance, noise, bias) derived errors for the amelioration of stability and accuracy of ML approaches and greatly aid in the detection of malicious nodes while segregating attributes which chiefly contributes to the prediction of attacks occurring in the network which later provide for establishing threshold values and determination of important metrics affected during attacks. The internet of vehicles requires security monitoring and analysis for the attainment of secure vehicular communication by avoiding on-road accidental risks and ensuring on-road timeliness. The increasing population with the boom in traffic highlights the imminence of studies on intelligent transportation networks (ITNs). The study targets to improve the efficiency of machine learning frameworks for future use in ITNs by utilizing powerful techniques in the pre-processing stage for optimized performance in different attack scenarios. Subsequently, it compares the obtained metrics for finding the most suitable B & B algorithm for future studies. In the study, we first present the taxonomy of attacks analyzed and then concisely describe the working principles of B & B algorithms for improved, secure communication in IoTs. Additionally, it involves the analysis of existent internet of vehicles (IoV) architectures, preceding the B & B algorithms applied on various datasets, that were analyzed through dataset pre-processing and attribute description. Finally, results were analyzed using the different B & B algorithms for intrusion detection systems (IDSs) to find the most suitable algorithm.

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

Similar content being viewed by others

References

  1. Alrashdi I, Alqazzaz A, Aloufi E, Alharthi R, Zohdy M, Ming H. AD-IoT: Anomaly detection of IoT cyberattacks in smart city using machine learning. In Proceedings of the 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, 7–9 January 2019; pp. 305–310.

  2. An Y, Liu D. Multivariate Gaussian-based false data detection against cyber-attacks. IEEE Access. 2019;7:119804–12.

    Article  Google Scholar 

  3. Azmoodeh A, Dehghantanha A, Conti M, Choo KKR. Detecting crypto-ransomware in IoT networks based on energy consumption footprint. J Ambient Intell Humaniz Comput. 2018;9:1141–52.

    Article  Google Scholar 

  4. Bhatia R, Benno S, Esteban J, Lakshman TV, Grogan J. Unsupervised machine learning for network-centric anomaly detection in IoT. In Proceedings of the 3rd Acm Conext Workshop on Big Data, Machine Learning and Artificial Intelligence for Data Communication Networks, Orlando, FL, USA, 9 December 2019; pp. 42–48.

  5. Doshi R, Apthorpe N, Feamster N. Machine learning DDoS detection for consumer internet of things devices. In Proceedings of the 2018 IEEE Security and Privacy Workshops (SPW), San Francisco, CA, USA, 24 May 2018; pp. 29–35.

  6. Dovom EM, Azmoodeh A, Dehghantanha A, Newton DE, Parizi RM, Karimipour H. Fuzzy pattern tree for edge malware detection and categorization in IoT. J Syst Archit. 2019;97:1–7.

    Article  Google Scholar 

  7. HaddadPajouh H, Dehghantanha A, Khayami R, Choo KKR. A deep recurrent neural network based approach for internet of things malware threat hunting. Futur Gener Comput Syst. 2018;85:88–96.

    Article  Google Scholar 

  8. Hasan M, Islam MM, Zarif MII, Hashem MMA. Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches. Internet Things. 2019;7: 100059.

    Article  Google Scholar 

  9. Hodo E, Bellekens X, Hamilton A, Dubouilh PL, Iorkyase E, Tachtatzis C, Atkinson R. Threat analysis of IoT networks using artificial neural network intrusion detection system. In Proceedings of the 2016 International Symposium on Networks, Computers and Communications (ISNCC), Yasmine Hammamet, Tunisia, 11–13 May 2016; pp. 1–6.

  10. Jahromi AN, Karimipour H, Dehghantanha A, Choo KKR. Toward detection and attribution of cyber-attacks in IoTenabled cyber-physical systems. IEEE Internet Things J. 2021;8:13712–22.

    Article  Google Scholar 

  11. Li D, Deng L, Lee M, Wang H. IoT data feature extraction and intrusion detection system for smart cities based on deep migration learning. Int J Inf Manag. 2019;49:533–45.

    Article  Google Scholar 

  12. Li W, Meng W, Au MH. Enhancing collaborative intrusion detection via disagreement-based semi-supervised learning in IoT environments. J Netw Comput Appl. 2020;161: 102631.

    Article  Google Scholar 

  13. Li Y, Xu Y, Liu Z, Hou H, Zheng Y, Xin Y, Zhao Y, Cui L. Robust detection for network intrusion of industrial IoT based on multi-CNN fusion. Meas J Int Meas Confed. 2020;154: 107450.

    Article  Google Scholar 

  14. Meidan Y, Bohadana M, Mathov Y, Mirsky Y, Shabtai A, Breitenbacher D, Elovici Y. N-BaIoT-Network-based detection of IoT botnet attacks using deep autoencoders. IEEE Pervasive Comput. 2018;17:12–22.

    Article  Google Scholar 

  15. Ng BA, Selvakumar S. Anomaly detection framework for Internet of things traffic using vector convolutional deep learning approach in fog environment. Futur Gener Comput Syst. 2020;113:255–65.

    Article  Google Scholar 

  16. Rahman MA, Asyhari AT, Leong LS, Satrya GB, Hai Tao M, Zolkipli MF. Scalable machine learning-based intrusion detection system for IoT-enabled smart cities. Sustain Cities Soc. 2020;61:102324.

    Article  Google Scholar 

  17. Rashid MM, Kamruzzaman J, Hassan MM, Imam T, Gordon S. Cyberattacks detection in iot-based smart city applications using machine learning techniques. Int J Environ Res Public Health. 2020;17:9347.

    Article  Google Scholar 

  18. Roldán J, Boubeta-Puig J, Luis Martínez J, Ortiz G. Integrating complex event processing and machine learning: An intelligent architecture for detecting IoT security attacks. Expert Syst Appl. 2020;149: 113251.

    Article  Google Scholar 

  19. Roopak M, Yun Tian G, Chambers J. Deep learning models for cyber security in IoT networks. In Proceedings of the IEEE 9th annual computing and communication workshop and conference (CCWC), Las Vegas, NV, USA, 7–9 January 2019; pp. 452–457.

  20. Saharkhizan M, Azmoodeh A, Dehghantanha A, Choo KKR, Parizi RM. An ensemble of deep recurrent neural networks for detecting IoT cyber attacks using network traffic. IEEE Internet Things J. 2020;7:8852–9.

    Article  Google Scholar 

  21. 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. Futur Gener Comput Syst. 2020;107:433–42 (Electronics 2022, 11, 198 26 of 27).

    Article  Google Scholar 

  22. Smys S, Basar A. Haoxiang wang hybrid intrusion detection system for internet of things (IoT). J ISMAC. 2020;2:190–9.

    Article  Google Scholar 

  23. Soe YN, Feng Y, Santosa PI, Hartanto R, Sakurai K. Towards a lightweight detection system for cyber attacks in the IoT environment using corresponding features. Electronics. 2020;9:144.

    Article  Google Scholar 

  24. Thamilarasu G, Chawla S. Towards deep-learning-driven intrusion detection for the internet of things. Sensors. 1977;2019:19.

    Google Scholar 

  25. Ullah F, Naeem H, Jabbar S, Khalid S, Latif MA, Al-Turjman F, Mostarda L. Cyber security threats detection in internet of things using deep learning approach. IEEE Access. 2019;7:124379–89.

    Article  Google Scholar 

  26. Wang D, Wang X, Zhang Y, Jin L. Detection of power grid disturbances and cyber-attacks based on machine learning. J Inf Secur Appl. 2019;46:42–52.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Amity University Uttar Pradesh, Noida, India

    Shreya Upadhyaya & Deepti Mehrotra

Authors
  1. Shreya Upadhyaya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Deepti Mehrotra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shreya Upadhyaya.

Ethics declarations

Conflict of Interest

The authors have no conflicts of interest to declare. All co-authors have seen and agreed with the contents of the manuscript and there is no financial interest to report. We certify that the submission is original work and is not under review at any other publication.

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

Upadhyaya, S., Mehrotra, D. Benchmarking the Bagging and Boosting (B & B) Algorithms for Modeling Optimized Autonomous Intrusion Detection Systems (AIDS). SN COMPUT. SCI. 4, 465 (2023). https://doi.org/10.1007/s42979-023-01914-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-023-01914-x

Keywords

Search

Navigation