# Deep Feature Fusion via Transfer Learning for Multi-Class Network Intrusion Detection
## 2 Related Work  
In the field of network intrusion detection, numerous studies have explored performance enhancement through machine learning- and deep learning-based models. This section examines relevant research and delineates the distinctive contributions of the present study. [PERSON] et al. proposed a hybrid intrusion detection model that integrates artificial neural networks (ANNs) with support vector machines (SVMs), employing traditional machine learning techniques [10]. Their methodology utilized feature extraction and dimensionality reduction to initially classify data into normal and abnormal categories, followed by multi-class classification of abnormal data to further differentiate attack types. This approach aimed to enhance both accuracy and sensitivity in intrusion detection while addressing contemporary network security challenges. [PERSON] et al. (2023) developed a convolutional neural network (CNN) based model to improve existing autoencoder-based intrusion detection systems [11]. By leveraging the robust feature extraction capabilities of CNNs, their model effectively learned critical patterns from network data, thereby accelerating learning speed and increasing detection rates across various attack types compared to preceding methods. The research validated this model using the CIC-IDS dataset, demonstrating superior detection performance relative to established techniques. [PERSON] et al. introduced a hybrid intrusion detection system that synthesizes the advantages of both network intrusion detection systems (NIDSs) and host intrusion detection systems (HIDSs) [12]. This comprehensive approach analyzes not only network traffic data but also internal host logs and file modification data. Furthermore, the system implements a two-stage classification process: initially employing binary classification to distinguish between normal and abnormal data, subsequently applying multi-class classification to categorize abnormal data by specific attack type, thereby maximizing detection efficiency. Transfer learning has emerged as a significant area of interest in intrusion detection research, offering techniques that accelerate learning and enhance performance by applying pretrained models to novel problems. Transfer learning is an effective strategy for addressing performance degradation across domains and the issue of insufficient labeled data. [PERSON] et al. [13] conducted a systematic review of 1676 recent studies, providing a comprehensive overview of the current applications, practical use cases, and future research directions of transfer learning techniques. Transfer learning facilitates knowledge transference from models trained in the source domain to the target domain, enabling high performance even with limited training data. In network intrusion detection, where new attack types continuously emerge, the detection of threats in zero-day attacks and data-scar environments constitutes a critical challenge. Consequently, research exploring intrusion detection through transfer learning has gained considerable momentum [14; 15]. Additionally, feature fusion methodologies have been widely implemented to enhance performance across various deep learning domains by integrating diverse data sources or distinct features. [PERSON] et al. [16] effectively classified plant disease images by proposing a deep information feature fusion network (DFN-PSAN) that integrates and processesmultilevel deep feature representations for improved diagnostic accuracy. Meanwhile, [PERSON] et al. [17] introduced an innovative breast cancer classification framework utilizing deep learning and feature fusion techniques, developing a system that automatically classifies breast cancer through ultrasound imaging. Furthermore, [PERSON] et al. [18] proposed a multi-scale feature fusion framework (MFFSP) that combines the complementary characteristics of CNNs and Transformers, enabling precise and effective detection of landslides. On the other hand, [PERSON] et al.