# Deep Feature Fusion via Transfer Learning for Multi-Class Network Intrusion Detection
## 1 Introduction  
With the rapid advancement of information and communication technology (ICT), networks have evolved into essential infrastructure in contemporary society, serving diverse technologies and services beyond fundamental data exchange. Critical sectors of modern society--including economics, education, healthcare, telecommunications, and finance--are increasingly interconnected and operated through networks, amplifying their significance. In the global marketplace, organizations exchange data in real time, while e-commerce platforms facilitate worldwide customer connections, enabling transactions irrespective of time or location. Furthermore, networks constitute the foundational infrastructure for cloud-based services and big data analytics, facilitating more efficient organizational decision making [1]. This proliferation of network technologies has generated vast quantities of data exchange while simultaneously creating novel security vulnerabilities, resulting in escalating cybertimes and threats that exploit these weaknesses. Both the frequency and magnitude of cyberattacks increase annually, with attack methodologies becoming increasingly sophisticated and complex. Vulnerable sectors, including finance, healthcare, and defense, face threats that extend beyond simple data breaches, potentially incurring severedamage through attacks such as denial of service (DoS) and ransomware [2]. Consequently, cybersecurity has emerged as a critical imperative across societal and industrial domains. The advancement of technologies for detecting intrusions and anomalies in encrypted network traffic is essential to counter irregular attack patterns and evolving methodologies that transform annually across heterogeneous environments. Moreover, conventional security technologies exhibit limitations in defending against emergent threats that elude anticipation, necessitating the implementation of more sophisticated and automated detection mechanisms [3]. Previous research has predominantly focused on the binary classification of network traffic into normal and malicious categories using machine learning-based pattern analysis [4; 5; 6]. While this approach demonstrated efficacy in relatively simplistic historical network environments, it exhibits limitations in adequately capturing the complexities of rapidly evolving contemporary networks and traffic patterns. Specifically, traditional methodologies rely on fixed pattern-based learning, reducing adaptability to novel attack types or dynamic traffic variations. In addition, the substantial disparity between normal traffic and attack data proportions creates class imbalance, causing models to develop bias toward the predominant normal class, thereby diminishing sensitivity to actual attacks. Consequently, these approaches struggle to effectively respond to highly sophisticated and irregular network attacks [7]. Furthermore, datasets commonly employed in network intrusion detection research are predominantly designed around well-documented attack types, resulting in diminished generalization performance against emerging cyber threats [8]. Consequently, while existing models achieve high detection performance for known attacks, their effectiveness deteriorates rapidly when encountering unknown threats. This challenge intensifies as network security environments grow increasingly complex and cyber attackers employ diverse techniques to circumvent traditional detection systems. Accordingly, modern intrusion detection systems (IDSs) must demonstrate both scalability and adaptability to effectively counter emerging cyber threats, providing a defense not only against well-documented attack types but also against the increasing diversity and complexity of novel cyberattacks. Achieving this objective requires approaches that resolve dataset imbalance issues while maintaining robust detection performance across diverse environments through techniques such as transfer learning [9].