Um Sistema Inteligente para Detecção de DDoS em Ambientes Inteligentes Baseado em Computação em Nuvem e em Névoa
Abstract
Urban spaces are becoming Smart Environments (SE) that are composed of a huge number of heterogeneous devices: both personal devices (smartphones, notebooks, etc.) and Internet of Things (IoT) devices (sensors, actuators, and others). One of the existing problems of SEs is the detection of Distributed Denial of Service (DDoS) attacks, due to the vulnerabilities of IoT devices. Thus, it is necessary to implement solutions that can detect DDoS in SEs with scalability, adaptability and heterogeneity (application execution, hardware capacity and different protocols). Within this context, this paper presents an Intelligent System for detection of DDoS in SEs, applying Machine Learning (ML) combined with fog and cloud computing. The experiments performed, using real network traffic, suggest that the proposed system reaches 99% accuracy while reducing the volume of data exchanged and the detection time.
References
Ahmed, E., Yaqoob, I., Gani, A., Imran, M., and Guizani, M. (2016). Internet-of-thingsbased smart environments: state of the art, taxonomy, and open research challenges. IEEE Wireless Communications, 23(5):10–16.
Andrea, I., Chrysostomou, C., and Hadjichristofi, G. (2015). Internet of things: Security vulnerabilities and challenges. In 2015 IEEE Symposium on Computers and Communication (ISCC), pages 180–187.
Brun, O., Yin, Y., Augusto-Gonzalez, J., Ramos, M., and Gelenbe, E. (2018). Iot attack detection with deep learning. In ISCIS Security Workshop.
Chang, C.-C. and Lin, C.-J. (2011). Libsvm: A library for support vector machines. ACM transactions on intelligent systems and technology (TIST), 2(3):1–27.
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.
Doshi, R., Apthorpe, N., and Feamster, N. (2018). Machine learning ddos detection for consumer internet of things devices. In 2018 IEEE Security and Privacy Workshops (SPW), pages 29–35. IEEE.
Friedman, J., Hastie, T., and Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of statistical software, 33(1):1.
Geurts, P., Ernst, D., and Wehenkel, L. (2006). Extremely randomized trees. Machine learning, 63(1):3–42.
Hamamoto, A. H., Carvalho, L. F., Sampaio, L. D. H., Abrão, T., and Proença Jr, M. L. (2018). Network anomaly detection system using genetic algorithm and fuzzy logic. Expert Systems with Applications, 92:390–402.
Kaushik, S. (2016). Introduction to feature selection methods with an example (or how to select the right variables?). Analytics Vidhya.
Koroniotis, N., Moustafa, N., Sitnikova, E., and Turnbull, B. (2018). Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset. CoRR, abs/1811.00701.
Li, H., Ota, K., and Dong, M. (2018). Learning iot in edge: deep learning for the internet of things with edge computing. IEEE Network, 32(1):96–101.
Meidan, Y., Bohadana, M., Mathov, Y., Mirsky, Y., Shabtai, A., Breitenbacher, D., and Elovici, Y. (2018). N-baiot—network-based detection of iot botnet attacks using deep autoencoders. IEEE Pervasive Computing, 17(3):12–22.
Meidan, Y., Bohadana, M., Mathov, Y., Mirsky, Y., Shabtai, A., Breitenbacher, D., and Elovici, Y. (2018). N-baiot—network-based detection of iot botnet attacks using deep autoencoders. IEEE Pervasive Computing, 17(3):12–22.
Peng, H., Long, F., and Ding, C. (2005). Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on pattern analysis and machine intelligence, 27(8):1226–1238.
Pisani, F., de Oliveira, F. M. C., Gama, E. S., Immich, R., Bittencourt, L. F., and Borin, E. (2020). Fog computing on constrained devices: Paving the way for the future iot.
Sharafaldin, I., Lashkari, A. H., Hakak, S., and Ghorbani, A. A. (2019). Developing realistic distributed denial of service (ddos) attack dataset and taxonomy. In 2019 International Carnahan Conference on Security Technology (ICCST), pages 1–8. IEEE.
Sivanathan, A., Gharakheili, H. H., Loi, F., Radford, A., Wijenayake, C., Vishwanath, A., and Sivaraman, V. (2018). Classifying iot devices in smart environments using network traffic characteristics. IEEE Transactions on Mobile Computing, 18(8):1745–1759.
Wood, L. B. and Asada, H. H. (2007). Low variance adaptive filter for cancelling motion artifact in wearable photoplethysmogram sensor signals. In 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages 652–655. IEEE.
Yamauchi, M., Ohsita, Y., Murata, M., Ueda, K., and Kato, Y. (2019). Anomaly detection for smart home based on user behavior. In 2019 IEEE International Conference on Consumer Electronics (ICCE), pages 1–6. IEEE.
Andrea, I., Chrysostomou, C., and Hadjichristofi, G. (2015). Internet of things: Security vulnerabilities and challenges. In 2015 IEEE Symposium on Computers and Communication (ISCC), pages 180–187.
Brun, O., Yin, Y., Augusto-Gonzalez, J., Ramos, M., and Gelenbe, E. (2018). Iot attack detection with deep learning. In ISCIS Security Workshop.
Chang, C.-C. and Lin, C.-J. (2011). Libsvm: A library for support vector machines. ACM transactions on intelligent systems and technology (TIST), 2(3):1–27.
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.
Doshi, R., Apthorpe, N., and Feamster, N. (2018). Machine learning ddos detection for consumer internet of things devices. In 2018 IEEE Security and Privacy Workshops (SPW), pages 29–35. IEEE.
Friedman, J., Hastie, T., and Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of statistical software, 33(1):1.
Geurts, P., Ernst, D., and Wehenkel, L. (2006). Extremely randomized trees. Machine learning, 63(1):3–42.
Hamamoto, A. H., Carvalho, L. F., Sampaio, L. D. H., Abrão, T., and Proença Jr, M. L. (2018). Network anomaly detection system using genetic algorithm and fuzzy logic. Expert Systems with Applications, 92:390–402.
Kaushik, S. (2016). Introduction to feature selection methods with an example (or how to select the right variables?). Analytics Vidhya.
Koroniotis, N., Moustafa, N., Sitnikova, E., and Turnbull, B. (2018). Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset. CoRR, abs/1811.00701.
Li, H., Ota, K., and Dong, M. (2018). Learning iot in edge: deep learning for the internet of things with edge computing. IEEE Network, 32(1):96–101.
Meidan, Y., Bohadana, M., Mathov, Y., Mirsky, Y., Shabtai, A., Breitenbacher, D., and Elovici, Y. (2018). N-baiot—network-based detection of iot botnet attacks using deep autoencoders. IEEE Pervasive Computing, 17(3):12–22.
Meidan, Y., Bohadana, M., Mathov, Y., Mirsky, Y., Shabtai, A., Breitenbacher, D., and Elovici, Y. (2018). N-baiot—network-based detection of iot botnet attacks using deep autoencoders. IEEE Pervasive Computing, 17(3):12–22.
Peng, H., Long, F., and Ding, C. (2005). Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on pattern analysis and machine intelligence, 27(8):1226–1238.
Pisani, F., de Oliveira, F. M. C., Gama, E. S., Immich, R., Bittencourt, L. F., and Borin, E. (2020). Fog computing on constrained devices: Paving the way for the future iot.
Sharafaldin, I., Lashkari, A. H., Hakak, S., and Ghorbani, A. A. (2019). Developing realistic distributed denial of service (ddos) attack dataset and taxonomy. In 2019 International Carnahan Conference on Security Technology (ICCST), pages 1–8. IEEE.
Sivanathan, A., Gharakheili, H. H., Loi, F., Radford, A., Wijenayake, C., Vishwanath, A., and Sivaraman, V. (2018). Classifying iot devices in smart environments using network traffic characteristics. IEEE Transactions on Mobile Computing, 18(8):1745–1759.
Wood, L. B. and Asada, H. H. (2007). Low variance adaptive filter for cancelling motion artifact in wearable photoplethysmogram sensor signals. In 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages 652–655. IEEE.
Yamauchi, M., Ohsita, Y., Murata, M., Ueda, K., and Kato, Y. (2019). Anomaly detection for smart home based on user behavior. In 2019 IEEE International Conference on Consumer Electronics (ICCE), pages 1–6. IEEE.
Published
2021-08-16
How to Cite
COSTA, Wanderson L.; PORTELA, Ariel L. C.; GOMES, Rafael L..
Um Sistema Inteligente para Detecção de DDoS em Ambientes Inteligentes Baseado em Computação em Nuvem e em Névoa. In: URBAN COMPUTING WORKSHOP (COURB), 5. , 2021, Uberlândia.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 237-250.
ISSN 2595-2706.
DOI: https://doi.org/10.5753/courb.2021.17117.
