Online ddos testing tool4/8/2024 In: Proceedings - 2019 Amity International Conference on Artificial Intelligence, AICAI 2019 (2019). Wani, A.R., Rana, Q.P., Saxena, U., Pandey, N.: Analysis and detection of DDoS attacks on cloud computing environment using machine learning techniques. 12(5), 2247–2260 (2023)Ĭheng, J., Liu, Y., Tang, X., Sheng, V.S., Li, M., Li, J.: DDoS attack detection via multi-scale convolutional neural network. Įlbarougy, R., Aboghrara, E., Behery, G.M., Younes, Y.M., El-Badry, N.M.: COVID-19 detection on chest x-ray images by combining histogram-oriented gradient and convolutional neural network features. Haider, S., et al.: A deep CNN ensemble framework for efficient DDoS attack detection in software defined networks. In: ICOASE 2018 - International Conference on Advanced Science and Engineering (2018). Zebari, R.R., Zeebaree, S.R.M., Jacksi, K.: Impact ANALYSIS of HTTP and SYN flood DDoS attacks on apache 2 and IIS 10.0 web servers. Įliyan, L.F., Di Pietro, R.: DoS and DDoS attacks in software defined networks: a survey of existing solutions and research challenges. In: 2018 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2018 (2018). Yihunie, F., Abdelfattah, E., Odeh, A.: Analysis of ping of death DoS and DDoS attacks. KeywordsĪImajali, M.H., Ghazwi, M., Alqudah, F.T., ALmahasnah, M.J., Alajarmeh, H.H., Masarweh, A.A.: The legal aspects and the enhanced role of cybersecurity in protecting the electronic voting process in the context of Jordan Parliament election law no. When compared to other models, the proposed model was able to correctly identify DoS/DDoS packets that had never been seen before with a 98.95% level of accuracy. In this study, we looked at how different optimizers, the size of the hidden state, and the number of layers affected the same architecture to find the best way to set it up. Through a process of learning, these data will help to find attacks, predict attacks, or find intrusions. In this paper, we describe a method for security analysis that uses Deep Learning techniques like simple LSTM, LSTM with embedding, and Seq-to-Seq LSTM on several systems log files to find and extract data that may be related to distributed denial of service (DDoS) attacks made by malicious users who want to break into a system. ![]() The main goal of this article is to reduce the number of times that DDoS detection is wrongly labeled. We use Machine Learning (ML) to find network anomalies and build different models that are driven by data to find DDoS attacks. They can be very large and have a complicated structure, which is why they are so useful. Log files are a great way to find out what's wrong with a system and how secure it is.
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