The fast growth and acceptance of cloud computing technology have completely changed the IT infrastructure of organizations, but along with that transformation, there have been several emerging security concerns. These security concerns have become hard to detect using conventional security approaches, due to the complexity and the evolution of new cyber attacks. In this paper, a complete deep learning cybersecurity framework will be proposed, to detect any threats in real-time within cloud computing environments. The cybersecurity framework consists of several deep learning models. They include the TCN with an autoencoder to detect anomalies at 99% accuracy with a false positive rate of 2.2% based on CSE-CIC-IDS2018 dataset, a transformer with CNN to detect network intrusions with 99.12% accuracy, and a federated learning method for detecting attacks in distributed environment without violating any user's privacy at 98.3% accuracy in 300 communication rounds.