Federated Learning (FL) is a promising distributed training model that aims to minimize the
data sharing to enhance privacy and performance. FL requires sufficient and diverse training …

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 …

Network intrusion detection systems are evolving into intelligent systems that perform data
analysis while searching for anomalies in their environment. Indeed, the development of …

Abstract The Internet of Things (IoT) ecosystem needs Intrusion Detection Systems (IDS) to
mitigate cyberattacks and exploit security vulnerabilities. Over the past years, utilizing …

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

Anomaly detection methods based on machine learning assist in identifying attacker
behavior concealed in critical infrastructure's high-speed network traffic. However, these …

Network intrusion detection systems (NIDS) are critical to defending network systems from
cyber attacks. Recently, machine learning has been applied to enhance NIDS capability. To …

The influence of non-Independent Identically Distribution (non-IID) data on Federated
Learning (FL) has been a serious concern. Clustered Federated Learning (CFL) is an …

The rapid development of the information technology era has resulted in increased
awareness of data privacy by the general public, and the corresponding laws and …

Federated learning (FL) as an emerging distributed machine learning (ML) paradigm
enables participants to train their on-device data locally and share model parameters with …