Describe An Effective Security Framework for Detection of dos and ddos in the Cloud using Advanced Machine Learning Techniques with ML algorithm with a multi objective evolutionary algorithm (MOEA) to achieve better detection.
In this work, the hybrid model combines a multi-scale convolutional Gated Recurrent Unit network (MCGRU) with a Multi Objective Glowworm Swarm Optimization (MOGSO) algorithm for detecting security attacks.
The proposed security framework using advanced machine learning techniques and a multi-objective evolutionary algorithm for detecting DoS and DDoS attacks in the cloud is an innovative approach. The hybrid model combining a multi-scale convolutional Gated Recurrent Unit network (MCGRU) with a Multi-Objective Glowworm Swarm Optimization (MOGSO) algorithm can improve the accuracy of attack detection and reduce false positives.
The MCGRU network is well suited for detecting attacks in real-time, as it combines the strengths of both convolutional and recurrent neural networks. The MOGSO algorithm is used to optimize the hyperparameters of the MCGRU network, improving its performance and ensuring that it achieves the best possible results.
The use of a multi-objective optimization algorithm is particularly relevant, as it allows for the simultaneous optimization of multiple performance metrics, such as accuracy and false positive rates. This means that the framework can achieve a better balance between these metrics, which is crucial for real-world applications.
Overall, this security framework has the potential to significantly improve the detection of DoS and DDoS attacks in the cloud, reducing the risk of security breaches and improving the overall security of cloud-based systems.
