Multiclass decomposition and Artificial Neural Networks for intrusion detection and identification in Internet of Things environments
Resumo
The Internet of Things (IoT) systems have limited resources, making it difficult to implement some security mechanisms. It is important to detect attacks against these environments and identify their type. However, existing multi-class detection approaches present difficulties related to false positives and detection of less common attacks. Thus, this work proposes an approach with a two-stage analysis architecture based on One-Vs-All (OVA) and Artificial Neural Networks (ANN) to detect and identify intrusions in fog and IoT computing environments. The results of experiments with the Bot-IoT dataset demonstrate that the approach achieved promising results and reduced the number of false positives compared to state-of-the-art approaches and machine learning techniques.
Palavras-chave:
Intrusion Detection, Intrusion Identification, Internet of Things, Fog Computing, One vs. All, Artificial Neural Networks
Referências
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Bhuvaneswari, A. N. and Selvakumar, S. (2020). Anomaly detection framework for internet of things trafc using vector convolutional deep learning approach in fog environment. Future Generation Computer Systems, 113:255–265.
Bhuvaneswari Amma, N. G. and Subramanian, S. (2018). Vcdeep: Vector convolutional deep feature learning approach for identication of known and unknown denial of service attacks. In TENCON 2018 2018 IEEE Region 10 Conference, pages 0640–0645.
Bowyer, K. W., Chawla, N. V., Hall, L. O., and Kegelmeyer, W. P. (2011). SMOTE: synthetic minority over-sampling technique. CoRR, abs/1106.1813.
Chua, L. O. and Yang, L. (1988). Cellular neural networks: Theory. IEEE Transactions on circuits and systems, 35(10):1257–1272.
Conti, M., Dehghantanha, A., Franke, K., and Watson, S. (2018). Internet of things security and forensics: Challenges and opportunities.
Diro, A. A. and Chilamkurti, N. (2018). Distributed attack detection scheme using deep learning approach for internet of things. Future Generation Computer Systems, 82:761 – 768.
Du, R., Li, Y., Liang, X., and Tian, J. (2020). Support vector machine intrusion detection scheme based on cloud-fog collaboration. In International Conference on Security and Privacy in New Computing Environments, pages 321–334. Springer.
Frustaci, M., Pace, P., Aloi, G., and Fortino, G. (2017). Evaluating critical security issues of the iot world: Present and future challenges. IEEE Internet of things journal, 5(4):2483–2495.
Garcia-Teodoro, P., Diaz-Verdejo, J., Maciá-Fernández, G., and Vázquez, E. (2009). Anomaly-based network intrusion detection: Techniques, systems and challenges. computers & security, 28(1-2):18–28.
Haykin, S. et al. (2009). Neural networks and learning machines. Upper Saddle River: Pearson Education, 3.
Hughes, K., McLaughlin, K., and Sezer, S. (2020). Dynamic countermeasure knowledge In 2020 31st Irish Signals and Systems Conference for intrusion response systems. (ISSC), pages 1–6. IEEE.
Iorga, M., Feldman, L., Barton, R., and Martin, M. (2018). Fog computing conceptual model. special publication (nist sp)-500–325.
Karsoliya, S. (2012). Approximating number of hidden layer neurons in multiple hidden layer bpnn architecture. International Journal of Engineering Trends and Technology, 3(6):714–717.
Koroniotis, N., Moustafa, N., Sitnikova, E., and Turnbull, B. (2019). Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset. Future Generation Computer Systems, 100:779–796.
Liu, H. and Lang, B. (2019). Machine learning and deep learning methods for intrusion detection systems: A survey. Applied Sciences, 9(20).
Lorena, A. C., De Carvalho, A. C., and Gama, J. M. (2008). A review on the combination of binary classiers in multiclass problems. Articial Intelligence Review, 30(1-4):19.
Oong, T. H. and Isa, N. A. M. (2012). One-against-all ensemble for multiclass pattern classication. Applied Soft Computing, 12(4):1303–1308.
Oppitz, M. and Tomsu, P. (2018). Internet of things. In Inventing the Cloud Century, pages 435–469. Springer.
Popoola, S. I., Adebisi, B., Hammoudeh, M., Gui, G., and Gacanin, H. (2021). Hybrid deep learning for botnet attack detection in the internet-of-things networks. IEEE Internet of Things Journal, 8(6):4944–4956.
Quinlan, J. R. (1986). Induction of decision trees. Machine learning, 1(1):81–106.
Shaq, M., Tian, Z., Bashir, A. K., Du, X., and Guizani, M. (2021). Corrauc: A malicious bot-iot trafc detection method in iot network using machine-learning techniques. IEEE Internet of Things Journal, 8(5):3242–3254.
Soe, Y. N., Feng, Y., Santosa, P. I., Hartanto, R., and Sakurai, K. (2020). Towards a lightweight detection system for cyber attacks in the iot environment using corresponding features. Electronics, 9(1).
Vikram, N., Harish, K., Nihaal, M., Umesh, R., and Kumar, S. A. A. (2017). A low cost home automation system using wi- based wireless sensor network incorporating internet of things (iot). In 2017 IEEE 7th International Advance Computing Conference (IACC), pages 174–178. IEEE.
Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A., and Arshad, H. (2018). State-of-the-art in articial neural network applications: A survey. Heliyon, 4(11):e00938.
Agarap, A. F. (2019). Deep learning using rectied linear units (relu).
Almiani, M., AbuGhazleh, A., Al-Rahayfeh, A., Atiewi, S., and Razaque, A. (2020). Deep recurrent neural network for iot intrusion detection system. Simulation Modelling Practice and Theory, 101:102031. Modeling and Simulation of Fog Computing.
Bhuvaneswari, A. N. and Selvakumar, S. (2020). Anomaly detection framework for internet of things trafc using vector convolutional deep learning approach in fog environment. Future Generation Computer Systems, 113:255–265.
Bhuvaneswari Amma, N. G. and Subramanian, S. (2018). Vcdeep: Vector convolutional deep feature learning approach for identication of known and unknown denial of service attacks. In TENCON 2018 2018 IEEE Region 10 Conference, pages 0640–0645.
Bowyer, K. W., Chawla, N. V., Hall, L. O., and Kegelmeyer, W. P. (2011). SMOTE: synthetic minority over-sampling technique. CoRR, abs/1106.1813.
Chua, L. O. and Yang, L. (1988). Cellular neural networks: Theory. IEEE Transactions on circuits and systems, 35(10):1257–1272.
Conti, M., Dehghantanha, A., Franke, K., and Watson, S. (2018). Internet of things security and forensics: Challenges and opportunities.
Diro, A. A. and Chilamkurti, N. (2018). Distributed attack detection scheme using deep learning approach for internet of things. Future Generation Computer Systems, 82:761 – 768.
Du, R., Li, Y., Liang, X., and Tian, J. (2020). Support vector machine intrusion detection scheme based on cloud-fog collaboration. In International Conference on Security and Privacy in New Computing Environments, pages 321–334. Springer.
Frustaci, M., Pace, P., Aloi, G., and Fortino, G. (2017). Evaluating critical security issues of the iot world: Present and future challenges. IEEE Internet of things journal, 5(4):2483–2495.
Garcia-Teodoro, P., Diaz-Verdejo, J., Maciá-Fernández, G., and Vázquez, E. (2009). Anomaly-based network intrusion detection: Techniques, systems and challenges. computers & security, 28(1-2):18–28.
Haykin, S. et al. (2009). Neural networks and learning machines. Upper Saddle River: Pearson Education, 3.
Hughes, K., McLaughlin, K., and Sezer, S. (2020). Dynamic countermeasure knowledge In 2020 31st Irish Signals and Systems Conference for intrusion response systems. (ISSC), pages 1–6. IEEE.
Iorga, M., Feldman, L., Barton, R., and Martin, M. (2018). Fog computing conceptual model. special publication (nist sp)-500–325.
Karsoliya, S. (2012). Approximating number of hidden layer neurons in multiple hidden layer bpnn architecture. International Journal of Engineering Trends and Technology, 3(6):714–717.
Koroniotis, N., Moustafa, N., Sitnikova, E., and Turnbull, B. (2019). Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset. Future Generation Computer Systems, 100:779–796.
Liu, H. and Lang, B. (2019). Machine learning and deep learning methods for intrusion detection systems: A survey. Applied Sciences, 9(20).
Lorena, A. C., De Carvalho, A. C., and Gama, J. M. (2008). A review on the combination of binary classiers in multiclass problems. Articial Intelligence Review, 30(1-4):19.
Oong, T. H. and Isa, N. A. M. (2012). One-against-all ensemble for multiclass pattern classication. Applied Soft Computing, 12(4):1303–1308.
Oppitz, M. and Tomsu, P. (2018). Internet of things. In Inventing the Cloud Century, pages 435–469. Springer.
Popoola, S. I., Adebisi, B., Hammoudeh, M., Gui, G., and Gacanin, H. (2021). Hybrid deep learning for botnet attack detection in the internet-of-things networks. IEEE Internet of Things Journal, 8(6):4944–4956.
Quinlan, J. R. (1986). Induction of decision trees. Machine learning, 1(1):81–106.
Shaq, M., Tian, Z., Bashir, A. K., Du, X., and Guizani, M. (2021). Corrauc: A malicious bot-iot trafc detection method in iot network using machine-learning techniques. IEEE Internet of Things Journal, 8(5):3242–3254.
Soe, Y. N., Feng, Y., Santosa, P. I., Hartanto, R., and Sakurai, K. (2020). Towards a lightweight detection system for cyber attacks in the iot environment using corresponding features. Electronics, 9(1).
Vikram, N., Harish, K., Nihaal, M., Umesh, R., and Kumar, S. A. A. (2017). A low cost home automation system using wi- based wireless sensor network incorporating internet of things (iot). In 2017 IEEE 7th International Advance Computing Conference (IACC), pages 174–178. IEEE.
Publicado
04/10/2021
Como Citar
DE SOUZA, Cristiano Antonio; CARDOSO, João Vitor; WESTPHALL, Carlos Becker.
Multiclass decomposition and Artificial Neural Networks for intrusion detection and identification in Internet of Things environments. In: SIMPÓSIO BRASILEIRO DE SEGURANÇA DA INFORMAÇÃO E DE SISTEMAS COMPUTACIONAIS (SBSEG), 21. , 2021, Belém.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 85-98.
DOI: https://doi.org/10.5753/sbseg.2021.17308.
