<![CDATA[Rapid digital transformation has expanded the cyber attack surface, demanding a responsive and reliable Intrusion Detection System (IDS). The main obstacle in developing Deep Learning-based IDS is the high dimensionality of network traffic features and data class imbalance, which can trigger excessive computational loads. This study aims to develop an efficient and robust IDS model for detecting various types of cyber attacks using the CIC-IDS2017 dataset. The proposed method applies a hybrid approach integrating the XGBoost algorithm for high-importance feature selection to reduce data dimensionality, alongside a Multilayer Perceptron (MLP) architecture for classification. This research explores various neural network depth configurations combined with tanh and SELU activation functions to handle data non-linearity. Model performance is evaluated based on standard classification metrics as well as operational security metrics. Experimental results demonstrate that the proposed model achieved an accuracy of 99.39% with a low False Alarm Ratio (FAR) and Attack Miss Ratio (AMR) of 2.90%. This study contributes by presenting an intrusion detection framework capable of balancing architectural complexity and computational efficiency for implementation in modern network environments.]]>
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