A hybrid deep reinforcement learning framework for proactive cloud network intrusion detection using spatiotemporal feature learning
ShieldDRLNet is a hybrid deep reinforcement learning framework for proactive cloud-network intrusion detection that employs a convolutional neural network and a long short-term memory encoder to obtain a spatiotemporal traffic representation and uses a Double Deep Q-Network agent for adaptive sequential decision-making.