Numerous DDoS detection techniques exist, but they often fall short in effectively mitigating these attacks. Thus, in this project, we implemented eight distinct Machine Learning (ML) techniques to detect DDoS attacks from the source side within a cloud infrastructure.

This technique uses deep learning to extract IP packet attributes, builds an LSTM traffic prediction model, and then recognises DDoS attacks using the built-in LSTM model. Technology for detecting DDoS attacks is appropriate for this system.

May 30, 2023 · Various machine learning techniques have shown promise in detecting DDoS attacks with low false-positive rates and high detection rates. This survey paper offers a comprehensive taxonomy of machine learning-based methods for detecting DDoS attacks, reviewing supervised, unsupervised, hybrid approaches, and analyzing the related challenges.

In this study, we investigate the application of machine learning approaches to classify and forecast DDoS attacks. We present a comparative study of XGBoost, RandomForest, and Naive Bayes algorithms, highlighting their strengths and weaknesses in detecting DDoS attacks.

Identify the most effective approach with high accuracy and low time complexity. Create a reliable detection system capable of safeguarding websites by identifying and blocking malicious requests in real-time. This paper analyzes recent ML algorithms using the …

This project focuses on developing a system for detecting and mitigating Distributed Denial of Service (DDoS) attacks in Software-Defined Networking (SDN) environments using machine learning algorithms. DDoS attacks are one of the most prevalent security threats to …

Apr 23, 2025 · Machine learning-based DDoS attack detection using feature-based models offers a solution by handling complex traffic patterns. Ye et al. [ 7 ] developed support vector machines for attack classification based on flow table features, while Xu et al. [ 8 ] improved attack detection on NSL-KDD by combining k-means++ clustering with fast KNN ...

This work explores the potential of real-time DDoS attack detection and mitigation using machine learning and deep learning, with a particular emphasis on scalability, accuracy, and adaptability. ... and Long Short-Term Memory (LSTM) networks. The study attempts to determine the best models for real-time DDoS detection by comparing and ...

2 days ago · The experimental results show that the model achieves an accuracy of 99.925% and an F1 score of 99.962% on the CICDDoS2019 dataset, outperforming existing machine learning and deep learning methods, demonstrating outstanding performance in …

Nov 1, 2024 · SDNs play a crucial role in controlling DDoS attacks and protecting end nodes like IoT nodes, as well as other computing devices, in large-scale cloud networks. This paper provides an efficient approach to DDoS attack detection and prevention using machine learning algorithms.