Efficient Deep Neural Network for Intrusion Detection Using CIC-IDS-2017 Dataset

Intrusion Detection Systems (IDS) are essential in identifying and reporting potential network attacks. IDS are classified into Host-based IDS (HIDS), which monitor internal computer threats, and Network-based IDS (NIDS), which monitor network-level attacks. IDS can also function as anomaly-based, using Machine Learning (ML) and Deep Learning (DL) to recognize unfamiliar attack patterns, or as rule-based, which relies on historical data-driven rules. Notably, anomaly-based IDS can detect zero-day attacks. Therefore, this study proposes a Deep Neural Network (DNN) model for anomaly-based IDS, focusing on accurate and efficient attack detection and categorization using the CIC-IDS-2017 dataset. By optimizing dataset preprocessing, the DNN architecture aims to minimize computational demands while maintaining high accuracy. Comparative evaluation with other models demonstrates the proposed model's effectiveness in attack detection despite using a simpler, more lightweight architecture than those in other studies, where more complex, less efficient approaches are often employed.