Machine Learning-Based Approaches for Detecting and Mitigating Distributed Denial of Service (DDoS) Attacks to Improved Cloud Security

Cloud environments encounter massive service disruptions together with security breaches and substantial financiallosses through Distributed Denial of Service (DDoS) attacks. Detecting and mitigating DDoS assaults is the focus of this research,which examines the efficacy of ML models, particularly the CNN-LSTM model and the ID3 decision tree method. The CICDDoS2019dataset was used for both training and evaluation, employing a train-test data split of 80:20. The hybrid CNN-LSTMmodel achieved superior performance than the ID3 decision tree method when subjected to comparison because it integrates CNNspatial extraction with LSTM sequence learning. A CNN-LSTM model using 0.97 recall together with 0.98 precision and 0.98F1-score achieved 98.5% accuracy in detecting DDoS attacks. Analyses indicate that the ID3 model delivered below-average resultsyet remained a usable solution for detection of DDoS attacks in cloud environments. These findings provide light on the utilizationof decision tree algorithms such as ID3 in cloud security applications and highlight the potential of the CNN-LSTM hybrid modelas a strong solution for DDoS attack detection.