Related papers: SecureDyn-FL: A Robust Privacy-Preserving Federate…

Federated learning (FL) has become an effective paradigm for privacy-preserving, distributed Intrusion Detection Systems (IDS) in cyber-physical and Internet of Things (IoT) networks, where centralized data aggregation is often infeasible…

The rapid expansion of the Internet of Things (IoT) and Industrial IoT (IIoT) has created a massive, heterogeneous attack surface that challenges traditional network security mechanisms. While Federated Learning (FL) offers a…

With the growth of digital financial systems, robust security and privacy have become a concern for financial institutions. Even though traditional machine learning models have shown to be effective in fraud detections, they often…

Cyberattacks are a major issues and it causes organizations great financial, and reputation harm. However, due to various factors, the current network intrusion detection systems (NIDS) seem to be insufficent. Predominant NIDS identifies…

There is a growing trend of cyberattacks against Internet of Things (IoT) devices; moreover, the sophistication and motivation of those attacks is increasing. The vast scale of IoT, diverse hardware and software, and being typically placed…

As the Internet of Things (IoT) continues to expand, ensuring the security of connected devices has become increasingly critical. Traditional Intrusion Detection Systems (IDS) often fall short in managing the dynamic and large-scale nature…

The growing interest in the Internet of Things (IoT) applications is associated with an augmented volume of security threats. In this vein, the Intrusion detection systems (IDS) have emerged as a viable solution for the detection and…

The rapid growth of the Internet of Things (IoT) has expanded opportunities for innovation but also increased exposure to botnet-driven cyberattacks. Conventional detection methods often struggle with scalability, privacy, and adaptability…

Intrusion detection systems are evolving into intelligent systems that perform data analysis searching for anomalies in their environment. The development of deep learning technologies opened the door to build more complex and effective…

Critical role of Internet of Things (IoT) in various domains like smart city, healthcare, supply chain and transportation has made them the target of malicious attacks. Past works in this area focused on centralized Intrusion Detection…

The rapid growth of the Internet of Things (IoT) has revolutionized industries, enabling unprecedented connectivity and functionality. However, this expansion also increases vulnerabilities, exposing IoT networks to increasingly…

The current amount of IoT devices and their limitations has come to serve as a motivation for malicious entities to take advantage of such devices and use them for their own gain. To protect against cyberattacks in IoT devices, Machine…

Federated Learning (FL) has recently become an effective approach for cyberattack detection systems, especially in Internet-of-Things (IoT) networks. By distributing the learning process across IoT gateways, FL can improve learning…

The Internet of Things (IoT) has significantly expanded the digital landscape, interconnecting an unprecedented array of devices, from home appliances to industrial equipment. This growth enhances functionality, e.g., automation, remote…

There have been significant issues given the IoT, with heterogeneity of billions of devices and with a large amount of data. This paper proposed an innovative design of the Internet of Things (IoT) Environment Intrusion Detection System (or…

Federated Learning (FL) has emerged as a transformative paradigm in the field of distributed machine learning, enabling multiple clients such as mobile devices, edge nodes, or organizations to collaboratively train a shared global model…

Federated Learning (FL) represents a paradigm shift in machine learning, allowing collaborative model training while keeping data localized. This approach is particularly pertinent in the Industrial Internet of Things (IIoT) context, where…

Federated learning (FL) has emerged as a privacy solution for collaborative distributed learning where clients train AI models directly on their devices instead of sharing their data with a centralized (potentially adversarial) server.…

Federated Learning (FL) is a decentralized training framework widely used in IoT ecosystems that preserves privacy by keeping raw data local, making it ideal for IoT-enabled cyber-physical systems with sensing and communication like Smart…

Federated learning (FL) is inherently susceptible to privacy breaches and poisoning attacks. To tackle these challenges, researchers have separately devised secure aggregation mechanisms to protect data privacy and robust aggregation…