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Real-time deep learning faces the challenge of balancing accuracy and time, especially in cybersecurity where intrusion detection is crucial. Traditional deep learning techniques have been insufficient in identifying network anomalies and intrusions. To address this, a Fully Streaming Big Data Framework based on optimized Deep Learning for cybersecurity (FSBDL) was proposed. The framework leverages two parallel optimization algorithms, Adam and RMSprop, labeled Hyper-parallel optimization (HPO) techniques to enhance efficiency and stability. The optimized CNN in the proposed framework achieves high accuracy in real-time intrusion detection without compromising reliability. The CNN is customized to address overfitting issues in recurrent connections by reducing the number of training parameters, using customized activation functions and regularization techniques. The CNN is trained in parallel using Adam and RMSprop optimization algorithms, resulting in significant accuracy improvements that surpass traditional methods and current state-of-the-art approaches. The HPO is a crucial component of the proposed framework, as it enables the system to detect intrusions in real-time, ensuring prompt response to potential cyber threats. The six-layer FSBDL framework includes data pre-processing, feature selection, hyper-parallelism, a customized CNN, a GUI layer for interpretation, and a detection-evaluation layer. The optimized CNN was designed to detect intrusions in real-time without compromising accuracy or reliability. The proposed algorithms were evaluated using various performance metrics, showing that the accuracy of the framework surpasses 99.9%, indicating its superiority over other intrusion detection models. This novel intrusion detection model offers promising prospects for cybersecurity, and its effectiveness and accuracy have been demonstrated through experimentation.