Optimizing Early Network Intrusion Detection: A Comparison of LSTM and LinearSVC with SMOTE on Imbalanced Data - Nugroho, Khabib Adi; Hariguna, Taqwa; Barkah, Azhari Shouni (Informatika, Universitas Jenderal Soedirman, 2025)
This study aims to improve network intrusion detection systems (IDS) by addressing class imbalance in the CICIDS 2017 dataset. It compares the effectiveness of Long Short-Term Memory (LSTM) networks and Linear Support Vector Classifier (LinearSVC) in detecting intrusions, with a focus on the impact of Synthetic Minority Over-sampling Technique (SMOTE) for balancing the dataset. The dataset was preprocessed by removing irrelevant features, handling missing values, and applying Min-Max normalization. SMOTE was applied to balance the training dataset. Results showed that LSTM outperformed LinearSVC, especially in recall and F1-score, after applying SMOTE. This research highlights the benefits of combining LSTM with SMOTE to address class imbalance in IDS and emphasizes the importance of temporal sequence models like LSTM for detecting network intrusions. Future work could involve using the full dataset, exploring advanced feature engineering, and implementing more complex architectures to further enhance performance. This research underscores the critical need for improving network security by addressing the challenges of class imbalance in intrusion detection systems, which is vital for ensuring the real-time identification and mitigation of sophisticated cyber threats in the ever-evolving landscape of network security.