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 …

[2] This work proposes a method of DDoS attack detection based on deep belief network feature extraction and LSTM model. This technique uses deep learning to extract IP packet attributes, …

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 …

In this paper, we have used machine learning based approach to detect and classify different types of network traffic flows. The proposed approach is validated using a new dataset which …

Mar 5, 2025 · Machine learning classifies network traffic based on various features, distinguishing normal from DDoS traffic. Explainable AI (XAI) methods, including LIME and SHAP, enhance …

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 …

Mar 18, 2020 · Using machine learning-based solutions have enabled researchers to detect DDoS attacks with complex and dynamic patterns. In this work, a subset of the CICIDS2017 dataset, …

One of the major threats in the cyber security and networking world is a Distributed Denial of Service (DDoS) attack. With massive development in Science and Te.

Mar 9, 2024 · Researchers have explored various machine learning algorithms such as K-Nearest Neighbours (KNN), Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes to …

In our project, we have used Machine Learning Algorithms like Logistic Regression Classifier and Support vector machine (SVM) for the detection of DDoS attacks.