Towards applying FCM with DBSCAN for Detecting DDoS Attack in Cloud Infrastructure to Improve Data Transmission Rate

  • Devi T Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India. https://orcid.org/0000-0002-1245-7097
  • Deepa N Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India.
  • Karthikeyan R Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India.
  • Bharath Sundararaman J Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India.
Keywords: Pay-to-use Technology, Cloud Infrastructure, FCM with DBSCAN Hybrid Algorithm, Network Security, Unwanted Packets, Vulnerable

Abstract

Cloud is a pay-to-use technology which can be used to offer IT resources instead of buying computer hardware. It is time saving and cheaper technology. This paper analyzes the DDoS attack on cloud infrastructure and can be detected by using FCM with DBSCAN hybrid algorithm that classifies the clusters of data packets and detects the outlier in that particular data packet. The experimental outcome shows that the enhanced hybrid approach has better results in detecting the DDoS attack. The DDoS attack targets the main host of the cloud infrastructure by sending unwanted packets. This attack is a major threat to the network security. The FCM with DBSCAN hybrid approach detects outliers and also assigns one specific data point in clusters to detect DDoS attack in cloud infrastructure. By using this hybrid approach the data can be grouped as clusters and the data beyond the noise level can also be detected. This algorithm helps in identifying the data that are vulnerable to DDoS attack. This detection helps in improving the data transmission rate.

Metrics

Metrics Loading ...

References

Devi, T., Priya, J.S., & Deepa, N., (2022). Framework for detecting the patients affected by COVID-19 at early stages using Internet of Things along with Machine Learning approaches with improved Accuracy, In 2022 International Conference on Computer Communication and Informatics (ICCCI), IEEE, India. https://doi.org/10.1109/ICCCI54379.2022.9740972

Deepa, N., Priya, J.S., & Devi, T., (2022). Towards applying internet of things and machine learning for the risk prediction of COVID-19 in pandemic situation using Naive Bayes classifier for improving accuracy, Materials Today: Proceedings, 62(7), 4795-4799. https://doi.org/10.1016/j.matpr.2022.03.345

Deepa, N., Udayakumar, N., Devi, T., (2022). Management of Traffic in Smart Cities Using Optical Character Recognition for Notifying Users, International Conference on Computer Communication and Informatics, ICCCI, IEEE, India. https://doi.org/10.1109/ICCCI54379.2022.9740793

Devi, T., & Deepa, N., (2021). Ant Colony Optimization (ACO) based Improved Edge Detection Algorithm for Segmentation of Brain Tumor, Annals of the Romanian Society for Cell Biology, 25(3), 2849-2867.

Deepa, N., Devi, T., Gayathri, N., & Kumar, S.R., (2022). Decentralized Healthcare Management System Using Blockchain to Secure Sensitive Medical Data for Users, In Blockchain Security in Cloud Computing, Springer, Cham. 265-282. https://doi.org/10.1007/978-3-030-70501-5_13

Aravinth, K.P., & Devi, T., (2021). Comparison of Fuzzy-based Cluster Head Selection Algorithm with LEACH Algorithm in Wireless Sensor Networks to Maximize Network Lifetime, Revista Geintec-Gestao Inovacao E Tecnologias, 11(4), 1277-1288.

Shah, S.Q.A., Khan, F.Z., & Ahmad, M., (2022). Mitigating TCP SYN flooding based EDOS attack in cloud computing environment using binomial distribution in SDN, Computer Communications, 182, 198-211. https://doi.org/10.1016/j.comcom.2021.11.008

Xu, Y., Deng, G., Zhang, T., Qiu, H., & Bao, Y., (2021). Novel denial-of-service attacks against cloud-based multi-robot systems, Information Sciences, 576, 329-344. https://doi.org/10.1016/j.ins.2021.06.063

Al-mamory, S.O., & Algelal, Z.M., (2017). A modified DBSCAN clustering algorithm for proactive detection of DDoS attacks, In 2017 Annual Conference on New Trends in Information & Communications Technology Applications (NTICT), IEEE, Iraq. https://doi.org/10.1109/NTICT.2017.7976107

Somani, G., Gaur, M.S., Sanghi, D., Conti, M., & Buyya, R., (2017). DDoS attacks in cloud computing: Issues, taxonomy, and future directions, Computer Communications, 107(15), 30-48. https://doi.org/10.1016/j.comcom.2017.03.010

Özçelik, M., Chalabianloo, N., & Gür, G., (2017). Software-defined edge defense against IoT-based DDoS, In 2017 IEEE international conference on computer and information technology (CIT), IEEE, Finland. https://doi.org/10.1109/CIT.2017.61

Batchu, R.K., & Seetha, H., (2022). On Improving the Performance of DDoS attack detection system, Microprocessors and Microsystems, 93, 104571. https://doi.org/10.1016/j.micpro.2022.104571

Al-Mamory, S.O., & Ali, Z.M., (2015). Using dbscan clustering algorithm in detecting ddos attack, Journal of Babylon University/Pure and Applied Sciences, 23(4), 1412-1424.

Published
2022-05-16
How to Cite
T, D., N, D., R, K., & J, B. S. (2022). Towards applying FCM with DBSCAN for Detecting DDoS Attack in Cloud Infrastructure to Improve Data Transmission Rate. International Journal of Computer Communication and Informatics, 4(1), 43-54. https://doi.org/10.34256/ijcci2215



Views: Abstract : 24 | PDF : 21

Plum Analytics