Federated learning (FL) is an efficient decentralized machine learning methodology for
processing nonindependent and identically distributed (non-IID) data due to geographical …

With the rapid development of artificial intelligence and a new generation of network
technologies, the Internet of Things (IoT) is expanding worldwide. Malicious agents …

The novelty of Federated Learning (FL) has emerged as a promising alternative to
centralized machine learning systems in the context of anomaly-based intrusion detection …

Cybersecurity breaches within the Internet of Vehicles (IoV) have been increasingly reported
annually with the proliferation of intelligent connected vehicles. Two primary obstacles are …

Malware propagation in IoT (Internet of Things) systems can lead to data leakages, financial
losses, and other serious consequences. To solve this issue, we propose a new active IoT …

Network intrusion detection systems (NIDS) are vital for identifying security attacks and
predicting early invasion attempts, which is essential for protecting the Internet. Recently …

Abstract The Internet of Things (IoT) landscape is booming, and a wide range of IoT
endpoints has been integrated into various aspects of life, opening opportunities for …

Malware has been a real threat to Internet-of-Things (IoT). Although commercial antivirus
solutions can detect malware files and provide label information indicating malware types or …

The federated Android malware classifier has attracted much attention owing to its
advantages of privacy protection and multi-party joint modeling. However, the research …

Securing connected devices in smart environments is crucial in the age of Internet of Things
(IoT). This paper proposes “MalBuster”, a scalable and real-time malware detection system …