<![CDATA[In modern network environments, securing systems from newly emerging attacks is essential, and a constructive approach is the use of an IDS (Intrusion Detection System). When faced with attacks that are not in the list of predefined patterns, traditional IDS methods such as signature-based detection or standalone machine learning models may not function properly to detect such attacks because they are not adaptable and not designed to deal with this type of attack. The current IDS systems that employ deep-learning architectures have enhanced detection capabilities; however, most prior art systems are limited by partial feature learning, which only learns features of either spatial or temporal traffic structures. Meanwhile, the lack of contextaware mechanisms, such as attention, limits their ability to attend more to the most informative network components, leading to suboptimal detection performance and generalization. To counter this issue, in this work, we introduce CyberShieldDL, which is the first deep learning-based IDS framework with a novel hybrid architecture: IntruNet-Hybrid, combining Convolutional Neural Networks (CNN) for spatial pattern extraction, Bidirectional Long Short-Term Memory (Bi-LSTM) networks for sequential feature extraction, and an attention mechanism to learn the salient features for intrusion detection dynamically. To create the framework, an optimized preprocessing and feature selection pipeline is presented to effectively and costeffectively prepare the model input. Extensive experiments on the CICIDS2017 dataset demonstrate that CyberShieldDL consistently outperforms the state-of-the-art, achieving an overall accuracy of 98.35% and high precision, recall, and F1-score in various attack scenarios. Cross-dataset validations on NSL-KDD and UNSW-NB15 also verify the system's generalization. The design provides a scalable and flexible solution for realworld network security, offering the flexibility and adaptability necessary to enhance classification accuracy and robustness against evolving attack patterns. Its modular construction enables us to extend it for real-time deployment and future adversarial robustness easily.]]>
