Large deep learning models are impressive, but they struggle when real-time data is not
available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for …

In traditional network management, the configuration of routing policies and associated
settings on individual routers and switches was performed manually, incurring a …

Network Intrusion Detection (NID) plays an important role in identifying network threats and
ensuring the security of computer and communication systems. However, the existing NID …

With the formation and popularity of the Internet of Things (IoT), the difficulty of protecting IoT
infrastructure and smart devices from a few-shot of ever-changing malicious attacks has …

Many end-to-end deep learning algorithms seeking to classify malicious traffic and
encrypted traffic have been proposed in recent years. End-to-end deep learning algorithms …

Ensuring network security, effective malware detection is of paramount importance.
Traditional methods often struggle to accurately learn and process the characteristics of …

Compared with conventional close-domain relation classification, incremental few-shot
relation classification requires incrementally to learn novel relations through very few …

Malicious traffic on the Internet has become an increasingly serious problem, and several
artificial intelligence (AI)-based malicious traffic detection methods have been proposed …

Most of the existing intrusion detection systems use supervised machine learning models,
which can detect attacks well by using a large amount of sample data. However, with the …

With the rapid development of information technologies, the security of cyberspace has
become increasingly serious. Network intrusion detection is a practical scheme to protect …