REFERENCES

1. de O. Paula A, Meneguette RI, Gonçalves VP, Andrade AO, Peixoto MM, Rocha Filho GP. Melhorando a integridade de sistemas de automação e comunicação em smart grids - uma arquitetura de combate a ciberataques. Available from: https://sol.sbc.org.br/index.php/wcge/article/view/24860[Last accessed on 16 Apr 2024].

2. Cristiani AL, Lieira DD, Meneguette RI, Camargo HA. A fuzzy intrusion detection system for identifying cyber-attacks on IoT networks. In: 2020 IEEE Latin-American Conference on Communications (LATINCOM); 2020. pp. 1–6.

3. Maschi LFC, Pinto ASR, Meneguette RI, Baldassin A. Data summarization in the node by parameters (DSNP): local data fusion in an IoT environment. Sensors 2018;18:799.

4. ISO/IEC 27000: 2018(en). Information technology - security techniques - information security management systems - overview and vocabulary. Available from: https://www.iso.org/obp/ui/#iso:std:iso-iec:27000:ed-5:v1:en[Last accessed on 16 Apr 2024].

5. de Oliveira JA, Gonçalves VP, Meneguette RI, et al. F-NIDS - a network intrusion detection system based on federated learning. Comput Netw 2023;236:110010.

6. Mazurczyk W, Caviglione L. Cyber reconnaissance techniques. Commun ACM 2021;64:86-95.

7. Pastori Valentini E, Ipolito Meneguette R, Alsuhaim A. An attacks detection mechanism for intelligent transport system. In: 2020 IEEE International Conference on Big Data (Big Data); 2020. pp. 2453–61.

8. Murini CT. Análise dos sistemas de detecção de intrusão em redes: snort e suricata comparando com dados da darpa Available from: http://www.redes.ufsm.br/docs/tccs/CleberMurini.pdf[Last accessed on 16 Apr 2024].

9. Zargar GR, Baghaie T. Category-based intrusion detection using PCA. J Inf Secur 2012;3:259-71.

10. do Vale Dalarmelina N, Arora P, Kaur B, Meneguette RI, Teixeira MA. Using ML and DL algorithms for intrusion detection in the industrial internet of things. In: AI, Machine Learning and Deep Learning. CRC Press; 2023. pp. 243–56.

11. Shafin SS, Karmakar G, Mareels I. Obfuscated memory malware detection in resource-constrained IoT devices for smart city applications. Sensors 2023;23:5348.

12. Ambusaidi MA, He X, Nanda P, Tan Z. Building an intrusion detection system using a filter-based feature selection algorithm. IEEE Trans Comput 2016;65:2986-98.

13. Zolanvari M, Teixeira MA, Jain R; IEEE. Effect of imbalanced datasets on security of industrial IoT using machine learning 2018 IEEE International Conference on Intelligence and Security Informatics (ISI), Miami, FL, USA, 2018, pp. 112-117.

14. Apruzzese G, Colajanni M, Ferretti L, Guido A, Marchetti M. On the effectiveness of machine and deep learning for cyber security. In: 2018 10th International Conference on Cyber Conflict (CyCon); 2018. pp. 371–90.

15. Shafin SS, Ahmed MM, Pranto MA, Chowdhury A. Detection of android malware using tree-based ensemble stacking model. In: 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE); 2021. pp. 1–6.

16. Sarker IH, Abushark YB, Alsolami F, Khan AI. Intrudtree: a machine learning based cyber security intrusion detection model. Symmetry 2020;12:754.

17. Teixeira MA, Salman T, Zolanvari M, Jain R, Meskin N, Samaka M. SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach. Available from: https://arxiv.org/abs/1904.00753[[Last accessed on 16 Apr 2024].

18. Pajouh HH, Javidan R, Khayami R, Dehghantanha A, Choo KKR. A Two-Layer Dimension Reduction and Two-Tier Classification Model for Anomaly-Based Intrusion Detection in IoT Backbone Networks. IEEE Transactions on Emerging Topics in Computing 2019;7:314-23.

19. Arya L, Gupta GP. Ensemble Filter-based Feature Selection Model for Cyber Attack Detection in Industrial Internet of Things. In: 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS). vol. 1. IEEE; 2023. pp. 834–40.

20. Mohy-Eddine M, Guezzaz A, Benkirane S, Azrour M, Farhaoui Y. An ensemble learning based intrusion detection model for industrial IoT security. Big Data Min Anal 2023;6:273-87.

21. Verma A, Ranga V. Machine learning based intrusion detection systems for IoT applications. Wireless Pers Commun 2020;111:2287-310.

22. Khoei TT, Aissou G, Hu WC, Kaabouch N. Ensemble learning methods for anomaly intrusion detection system in smart grid. In: 2021 IEEE International Conference on Electro Information Technology (EIT); 2021. pp. 129–35.

23. Liang W, Li KC, Long J, Kui X, Zomaya AY. An industrial network itrusion detection algorithm based on multifeature data clustering optimization model. IEEE Trans Ind Inform 2020;16:2063-71.

24. Khan IA, Pi D, Abbas MZ, Zia U, Hussain Y, Soliman H. Federated-SRUs: a federated-simple-recurrent-units-based IDS for accurate detection of cyber attacks against IoT-augmented industrial control systems. IEEE Internet Things J 2023;10:8467-76.

25. do Vale Dalarmelina N, Teixeira MA, Andrade FRH, Júnior LAP, Filho GPR, Meneguette RI. FSAnalysis: a feature selection and analysis mechanism considering balanced and unbalanced bases. In: 2022 17th Iberian Conference on Information Systems and Technologies (CISTI); 2022. pp. 1–7.

26. Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: machine learning in python. Available from: https://www.jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf[Last accessed on 16 Apr 2024].

27. Cohen J. Statistical power analysis. Available from: https://journals.sagepub.com/doi/abs/10.1111/1467-8721.ep10768783[Last accessed on 16 Apr 2024].