| Abstract: |
With the increasing adoption of cloud computing and the emergence of Industry 4.0, the need for robust intrusion detection mechanisms to safeguard cloud-based systems against Distributed Denial of Services Attacks (DDoS) attacks has become more critical than ever. This study presents a comprehensive comparative analysis of traditional Machine Learning (ML) techniques and Deep Learning (DL) for DDoS attack detection in cloud environments. Utilizing the CIC-IDS 2017 dataset, transformed from tabular data into image-based formats for DL model compatibility, we evaluate 27,001 instances of normal traffic and 21,844 DDoS attack instances. We preprocess network traffic data and explore various DL architectures, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, Bidirectional LSTM (BLSTM) networks, and Gated Recurrent Unit (GRU) networks. Additionally, we evaluate the performance of DL models against traditional ML algorithms, such as Random Forest (RF), Support Vector Machines (SVM), and Logistic Regression (LR), using standard evaluation metrics. Our results highlight the superior performance of DL models, particularly the BLSTM model, with an accuracy of 96.35%, precision of 97.42%, recall of 94.39%, F1 score of 95.88%, and ROC AUC score of 96.17%. Through in-depth analysis and discussion, we provide insights into the strengths and weaknesses of different intrusion detection mechanisms, such as the higher interpretability of ML models and the superior discriminatory power of DL models. These findings offer valuable guidance for practitioners and researchers in enhancing the security of cloud-based systems against DDoS attacks. |