Related papers: FedMSE: Semi-supervised federated learning approac…
The rise of IoT has made possible the development of %increasingly personalized services, like industrial services that often deal with massive amounts of data. However, as IoT grows, its threats are even greater. The false data injection…
Self-supervised learning (SSL) is capable of learning remarkable representations from centrally available data. Recent works further implement federated learning with SSL to learn from rapidly growing decentralized unlabeled images (e.g.,…
The significant rise of security concerns in conventional centralized learning has promoted federated learning (FL) adoption in building intelligent applications without privacy breaches. In cybersecurity, the sensitive data along with the…
The rapid growth of Internet of Things (IoT) devices has generated vast amounts of data, leading to the emergence of federated learning as a novel distributed machine learning paradigm. Federated learning enables model training at the edge,…
The integration of Internet of Things (IoT) applications in our daily lives has led to a surge in data traffic, posing significant security challenges. IoT applications using cloud and edge computing are at higher risk of cyberattacks…
The number of Internet of Things (IoT) deployments is expected to reach 75.4 billion by 2025. Roughly 70% of all IoT devices employ weak or no encryption; thus, putting them and their connected infrastructure at risk of attack by devices…
Device fingerprinting combined with Machine and Deep Learning (ML/DL) report promising performance when detecting cyberattacks targeting data managed by resource-constrained spectrum sensors. However, the amount of data needed to train…
The rapid expansion of Internet of Things (IoT) systems across various domains such as industry, smart cities, healthcare, manufacturing, and government services has led to a significant increase in security risks, threatening data…
The ongoing deployment of the Internet of Things (IoT)-based smart applications is spurring the adoption of machine learning as a key technology enabler. To overcome the privacy and overhead challenges of centralized machine learning, there…
The rapid progress in technology innovation usage and distribution has increased in the last decade. The rapid growth of the Internet of Things (IoT) systems worldwide has increased network security challenges created by malicious third…
Internet of Things (IoT) is transforming human lives by paving the way for the management of physical devices on the edge. These interconnected IoT objects share data for remote accessibility and can be vulnerable to open attacks and…
An application of software known as an Intrusion Detection System (IDS) employs machine algorithms to identify network intrusions. Selective logging, safeguarding privacy, reputation-based defense against numerous attacks, and dynamic…
Internet of Things (IoT) has become a popular paradigm to fulfil needs of the industry such as asset tracking, resource monitoring and automation. As security mechanisms are often neglected during the deployment of IoT devices, they are…
Device failure detection is one of most essential problems in industrial internet of things (IIoT). However, in conventional IIoT device failure detection, client devices need to upload raw data to the central server for model training,…
The Internet of Things (IoT) has emerged as a foundational paradigm supporting a range of applications, including healthcare, education, agriculture, smart homes, and, more recently, enterprise systems. However, significant advancements in…
This paper presents an energy-efficient transmission framework for federated learning (FL) in industrial Internet of Things (IIoT) environments with strict latency and energy constraints. Machinery subnetworks (SNs) collaboratively train a…
As IoT ecosystems continue to expand across critical sectors, they have become prominent targets for increasingly sophisticated and large-scale malware attacks. The evolving threat landscape, combined with the sensitive nature of…
Internet of Things (IoT) have widely penetrated in different aspects of modern life and many intelligent IoT services and applications are emerging. Recently, federated learning is proposed to train a globally shared model by exploiting a…
Many IoT applications at the network edge demand intelligent decisions in a real-time manner. The edge device alone, however, often cannot achieve real-time edge intelligence due to its constrained computing resources and limited local…
False Data Injection Attacks (FDIAs) pose severe security risks to smart grids by manipulating measurement data collected from spatially distributed devices such as SCADA systems and PMUs. These measurements typically exhibit…