Hybrid Multi-Objective Deep Learning Model for Anomaly Detection in Cloud Computing Environment
Keywords:
anomaly detection, cloud computing, DDoS attack, feature extraction, feature optimizationAbstract
Cloud computing environments play a pivotal role in the IT landscape, seamlessly integrated into the fabric of organizations and individuals' daily activities. Despite the myriad advantages offered by these environments, the specter of distributed denial of service (DDoS) attacks looms, casting potential disruptions such as service unavailability and extended response times. To tackle this challenge, we present a novel hybrid multi-objective deep learning model tailored for anomaly detection in cloud computing. Our approach commences with the deployment of the UNet pretrained architecture coupled with the modified emperor penguin optimization (MEPO) algorithm for robust feature extraction and optimization from the provided traffic traces. MEPO strategically selects optimal features, mitigating data dimensionality issues. Furthermore, we introduce the convolutional tensor-train neural network (CTT-NN) designed explicitly for anomaly detection in cloud computing. This innovative neural network architecture significantly enhances security and stability in cloud environments. To validate the efficacy of our proposed model, we conducted experiments using the widely recognized UNB ISCX dataset. The results underscore the superiority of our MEPO+CTT-NN, shows a 13.45% increase in accuracy and 14.56% improvement in an anomaly detection rate compared to existing methods. This performance validation underscores the potential of our hybrid multi-objective deep learning model as a robust solution for anomaly detection in cloud computing environments.
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