Federated learning has gained popularity in recent years to enhance IoT security because the model allows decentralized devices to collaboratively learn a shared model without exchanging raw data. Despite its privacy advantages, federated learning is...
Deep neural networks (DNNs) have successfully delivered cutting-edge performance in several fields. With the broader deployment of DNN models on critical applications, the security of DNNs has become an active and yet nascent area. Attacks against DN...
The rapid expansion and pervasive reach of the internet in recent years have raised concerns about evolving and adaptable online threats, particularly with the extensive integration of Machine Learning (ML) systems into our daily routines. These syst...
Federated learning (FL) enables collaborative model building among a large number of participants without sharing sensitive data to the central server. Because of its distributed nature, FL has limited control over local data and the corresponding tr...
In recent years, human–computer interactions have begun to apply deep neural networks (DNNs), known as deep learning, to make them work more friendly. Nowadays, adversarial example attacks, poisoning attacks, and backdoor attacks are the typica...
Deep learning (DL) technology has shown outstanding performance in various fields such as object recognition and classification, speech recognition, and natural language processing. However, it is well known that DL models are vulnerable to data pois...
The outstanding performance of deep neural networks (DNNs) in multiple computer vision in recent years has promoted its widespread use in aerial image semantic segmentation. Nonetheless, prior research has demonstrated the high susceptibility of DNNs...
Anomaly-based attack detection methods depend on some form of machine learning to detect data falsification attacks in smart living cyber–physical systems. However, there is a lack of studies that consider the presence of attacks during the tra...
Obtaining the balance between information loss and training accuracy is crucial in federated learning. Nevertheless, inadequate data quality will affect training accuracy. Here, to improve the training accuracy without affecting information loss, we...
As computer networks become increasingly important in various domains, the need for secure and reliable networks becomes more pressing, particularly in the context of blockchain-enabled supply chain networks. One way to ensure network security is by...
Federated Learning (FL) systems are increasingly vulnerable to data poisoning attacks, in which malicious clients attempt to manipulate their training data in order to compromise the corresponding machine learning model. Existing detection techniques...
Federated learning is a new paradigm where multiple data owners, referred to as clients, work together with a global server to train a shared machine learning model without disclosing their personal training data. Despite its many advantages, the sys...
In this paper, we introduce a security approach for on-device learning Edge AIs designed to detect abnormal conditions in factory machines. Since Edge AIs are easily accessible by an attacker physically, there are security risks due to physical attac...
Deep Generative Models (DGMs), as a state-of-the-art technology in the field of artificial intelligence, find extensive applications across various domains. However, their security concerns have increasingly gained prominence, particularly with regar...
Artificial intelligence (AI) and machine learning (ML) have become integral to various applications, leveraging vast amounts of heterogeneous, globally distributed Internet of Things (IoT) data to identify patterns and build accurate ML models for pr...
This paper presents a framework that integrates blockchain-enabled Federated Learning (FL) with consensus mechanisms to mitigate poisoning attacks in healthcare environments. The framework incorporates blockchain consensus mechanisms, with Proof-of-W...
The integration of unmanned aerial vehicles (UAVs) into next-generation wireless networks greatly enhances the flexibility and efficiency of communication and distributed computation for ground mobile devices. Federated learning (FL) provides a priva...
Address Resolution Protocol (ARP) is a widely used protocol that provides a mapping of Internet Protocol (IP) addresses to Media Access Control (MAC) addresses in local area networks. This protocol suffers from many spoofing attacks because of its st...
Intensive Care Units (ICUs) face unprecedented challenges in resource allocation, particularly during health crises in which algorithmic systems may be exposed to adversarial manipulation. A transformer-based expert system, ICU-Transformer, is presen...
Graph-based Recommender Systems (GRSs) model complex user–item relationships. They offer improved accuracy and personalization in recommendations compared to traditional models. However, GRSs also face severe challenges from novel poisoning att...
Federated learning is a novel distributed learning framework, which enables thousands of participants to collaboratively construct a deep learning model. In order to protect confidentiality of the training data, the shared information between server...
Studies related to pattern recognition and visualization using computer technology have been introduced. In particular, deep neural networks (DNNs) provide good performance for image, speech, and pattern recognition. However, a poisoning attack is a...
Artificial intelligence (AI) will play an important role in realizing maritime autonomous surface ships (MASSs). However, as a double-edged sword, this new technology brings forth new threats. The purpose of this study is to raise awareness among sta...
Various studies have been conducted on Multi-Agent Reinforcement Learning (MARL) to control multiple agents to drive effectively and safely in a simulation, demonstrating the applicability of MARL in autonomous driving. However, several studies have...
Accurate building energy prediction is useful in various applications starting from building energy automation and management to optimal storage control. However, vulnerabilities should be considered when designing building energy prediction models,...
Federated learning is the on-device, collaborative training of a global model that can be utilized to support the privacy preservation of participants’ local data. In federated learning, there are challenges to model training regarding privacy...
Collaborative filtering, as a widely used recommendation method, is widely applied but susceptible to data poisoning attacks, where malicious actors inject synthetic user interaction data to manipulate recommendation results and secure illicit benefi...
Neural machine translation (NMT) systems have achieved outstanding performance and have been widely deployed in the real world. However, the undertranslation problem caused by the distribution of high-translation-entropy words in source sentences sti...
Data poisoning attack is a well-known attack against machine learning models, where malicious attackers contaminate the training data to manipulate critical models and predictive outcomes by masquerading as terminal devices. As this type of attack ca...
In online social networks, users can vote on different trust levels for each other to indicate how much they trust their friends. Researchers have improved their ability to predict social trust relationships through a variety of methods, one of which...
Hypergraph Neural Networks (HGNNs) have become an important tool for processing complex structured data due to their ability to model higher-order associative relationships. However, the inherent adversarial vulnerabilities of HGNNs may raise serious...
The recommendation algorithm based on collaborative filtering is vulnerable to data poisoning attacks, wherein attackers can manipulate system output by injecting a large volume of fake rating data. To address this issue, it is essential to investiga...
Machine learning-based classification algorithms allow communication and computing (2C) task offloading from the end devices to the edge computing network servers. In this paper, we consider task classification based on the hybrid k-means and k
The rapid integration of large-scale AI models into distributed systems, such as the Artificial Intelligence of Things (AIoT), has introduced critical security and privacy challenges. While configurable models enhance resource efficiency, their deplo...
Named data networking (NDN) is gaining momentum in vehicular ad hoc networks (VANETs) thanks to its robust network architecture. However, vehicular NDN (VNDN) faces numerous challenges, including security, privacy, routing, and caching. Specifically,...
Federated learning (FL) is an emerging machine learning technique where machine learning models are trained in a decentralized manner. The main advantage of this approach is the data privacy it provides because the data are not processed in a central...
Machine learning (ML) has become a cornerstone of critical applications, but its vulnerability to data poisoning attacks threatens system reliability and trustworthiness. Prior studies have begun to investigate the impact of data poisoning and propos...
With the increasing popularity of the Internet of Things (IoT) platforms, the cyber security of these platforms is a highly active area of research. One key technology underpinning smart IoT systems is machine learning, which classifies and predicts...
Local differential privacy (LDP) protects user information from potential threats by randomizing data on individual devices before transmission to untrusted collectors. This method enables collectors to derive user statistics by analyzing randomized...
In response to the susceptibility of federated learning, which is based on a distributed training structure, to byzantine poisoning attacks from malicious clients, resulting in issues such as slowed or disrupted model convergence and reduced model ac...
A Distributed Denial-of-Service attack uses multiple sources operating in concert to attack a network or site. A typical DDoS flood attack on a website targets a web server with multiple valid requests, exhausting the server’s resources. The pa...
Federated learning (FL) is a machine learning technique where clients exchange only local model updates with a central server that combines them to create a global model after local training. While FL offers privacy benefits through local training, p...
As promising privacy-preserving machine learning technology, federated learning enables multiple clients to train the joint global model via sharing model parameters. However, inefficiency and vulnerability to poisoning attacks significantly reduce f...
Federated learning is a distributed learning method that seeks to train a shared global model by aggregating contributions from multiple clients. This method ensures that each client’s local data are not shared with others. However, research ha...
Federated learning has been popular for its ability to train centralized models while protecting clients’ data privacy. However, federated learning is highly susceptible to poisoning attacks, which can result in a decrease in model performance...
The classification of the wireless propagation channel between Line-of-Sight (LOS) or Non-Line-of-Sight (NLOS) is useful in the operation of wireless communication systems. The research community has increasingly investigated the application of machi...
In this study, we introduce a novel collaborative federated learning (FL) framework, aiming at enhancing robustness in distributed learning environments, particularly pertinent to IoT and industrial automation scenarios. At the core of our contributi...
The Domain Name System (DNS) is a fundamental component of the Internet, yet its distributed and caching nature makes it susceptible to various attacks, especially cache poisoning. Although the use of random port numbers and transaction IDs has reduc...
The study investigates how adversarial training techniques can be used to introduce backdoors into deep learning models by an insider with privileged access to training data. The research demonstrates an insider-driven poison-label backdoor approach...
Cyber-Physical Systems (CPS) are prone to many security exploitations due to a greater attack surface being introduced by their cyber component by the nature of their remote accessibility or non-isolated capability. Security exploitations, on the oth...
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