How to process and classify zero-day attacks due to their huge damage to social Internet of
Things (SIoT) systems has become a hot research issue. To solve this issue, we propose a …

The evolution of cybersecurity is undoubtedly associated and intertwined with the
development and improvement of artificial intelligence (AI). As a key tool for realizing more …

This paper focuses on the problem of personalized federated learning (FL) with the schema
of contrastive learning (CL), which is to implement collaborative pattern classification by …

With the rapid advancement in machine learning (ML), ML-based Intrusion Detection
Systems (IDSs) are widely deployed to protect networks from various attacks. One of the …

Для обеспечения точного и своевременного реагирования на различные типы атак
системы обнаружения вторжений собирают и анализируют большое количество …

Learning-outcome prediction (LOP) is a longstanding and critical problem in educational
routes. Many studies have contributed to developing effective models while often suffering …

In the era of Internet of Things (IoT), network-wide anomaly detection is a crucial part of
monitoring IoT networks due to the inherent security vulnerabilities of most IoT devices …

The network intrusion detection system (NIDS) plays a crucial role as a security measure in
addressing the increasing number of network threats. The majority of current research relies …

Malicious traffic detection has been a focal point in the field of network security, and deep
learning-based approaches are emerging as a new paradigm. However, most of them are …

The rapid expansion of the Internet of Things (IoT) has raised increasing concern about
targeted cyber attacks. Previous research primarily focused on static Intrusion Detection …