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Article
Computer Science, Artificial Intelligence
Md. Milon Islam et al.
Summary: Human Activity Recognition (HAR) is crucial for smart healthcare applications. Existing frameworks dealing with single modality of data lead to decreased reliability and accuracy. This article proposes a multi-level feature fusion technique using multi-head CNN with CBAM for visual data and ConvLSTM for time-sensitive multi-source sensor information. Experimental results show that the developed HAR framework outperforms existing methods.
INFORMATION FUSION
(2023)
Article
Engineering, Biomedical
Ilhan Firat Kilincer et al.
Summary: The widespread use of interconnected healthcare equipment and networks in the Internet of Medical Things (IoMT) increases the risk of security compromise. This study presents a cyber-attack and anomaly detection model based on recursive feature elimination (RFE) and multilayer perceptron (MLP). The model achieved high accuracy rates when tested on various IoMT cybersecurity datasets, demonstrating its effectiveness in countering cyber attacks in healthcare applications.
BIOCYBERNETICS AND BIOMEDICAL ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Zhiguo Qu et al.
Summary: This paper proposes a novel personalized federated learning algorithm based on information fusion to solve the problem of information fusion and sharing in intelligent vehicle networking. The algorithm achieves personalized privacy protection by grading users' privacy based on their preferences and adding noise that satisfies their privacy preferences. It performs collaborative training of deep models among different in-vehicle terminals using a lightweight dynamic convolutional network architecture to generate personalized models. By keeping the last layer local instead of sharing all model parameters, it adds another layer of data confidentiality and makes it difficult for adversaries to infer the target vehicle terminal's image.
INFORMATION FUSION
(2023)
Review
Computer Science, Hardware & Architecture
Raihan Ur Rasool et al.
Summary: The Internet of Medical Things (IoMT) enhances the scalability, efficiency, reliability, and accuracy of healthcare services, but the resource-constrained nature of these devices makes them vulnerable to security and privacy threats.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2022)
Article
Chemistry, Analytical
Mohammed Amin Almaiah et al.
Summary: The Industrial Internet of Things is becoming increasingly important as it integrates various technologies and applications. To address the security and privacy issues in IIoT networks, a blockchain-based deep-learning framework is proposed, which provides two levels of security and privacy protection.
Article
Computer Science, Information Systems
Muna Al-Hawawreh et al.
Summary: The Industrial Internet of Things (IIoT) is a high-value target for cyber attacks, and developing security solutions that fit its requirements is challenging due to the lack of accurate data. To address this, we propose X-IIoTID, an intrusion data set for IIoT that includes multi-view features of connectivity protocols, device activities, attack types, and protocols.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
Wei Liang et al.
Summary: This article proposes a collaborative clustering-characteristic-based data fusion approach for intrusion detection in a Blockchain-based system. By using mathematical and AI models to train and analyze data, it accurately detects abnormal intrusion behavior.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Artificial Intelligence
Ghulam Muhammad et al.
Summary: Smart healthcare integrates technologies like wearables, IoMT, machine learning, and wireless communication to access health records and link resources. The fusion of multimodal medical signals is a key research focus in this field, with recent developments and challenges being explored in the survey of research works from 2014-2020.
INFORMATION FUSION
(2021)
Article
Automation & Control Systems
Edmond Q. Wu et al.
Summary: This paper proposes a new method to solve the challenge of tracking pilot attention by integrating spherical Haar wavelet transform and deep learning methods. The method includes feature learning, spherical Haar wavelet, higher contractive autoencoder, and fuzzy Gaussian support vector machine components. The experimental results show the effectiveness of the model for spherical signal detection.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Chemistry, Analytical
Faisal Hussain et al.
Summary: The rapid growth of IoT technology has revolutionized human life but also raised security concerns, especially in areas such as healthcare. Researchers need IoT-specific tools, methods, and datasets to address the security issues posed by IoT devices.
Article
Automation & Control Systems
Xiaolong Xu et al.
Summary: The article introduces a privacy-aware deployment method (PDM) for hosting ML applications in industrial CPCSs to improve implementation performance and resource utility.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Automation & Control Systems
M. O. Kalinin et al.
Summary: A method for analyzing network traffic based on quantum machine learning is developed, which encodes network traffic into quantum computer terms to detect network attacks, showing superiority over traditional machine learning methods in experiments.
AUTOMATIC CONTROL AND COMPUTER SCIENCES
(2021)
Article
Computer Science, Information Systems
Muna Al-Hawawreh et al.
Summary: This paper introduces a targeted ransomware detection model tailored for IIoT systems, utilizing Asynchronous Peer-to-Peer Federated Learning and Deep Learning techniques to effectively detect known and unknown attacks in these systems with their heterogeneous and distributed nature.
Article
Computer Science, Information Systems
Abhijeet Sahu et al.
Summary: This research introduces a mechanism to fuse real-time data from cyber and physical domains to improve situational awareness in power systems. Enhanced situational awareness can lead to reduced false positives in intrusion detection. The study finds that fusion of features from cyber, security, and physical domains can improve detection accuracy.
Review
Physics, Applied
M. Cerezo et al.
Summary: Variational quantum algorithms, utilizing classical optimizers to train parameterized quantum circuits, have emerged as a leading strategy to address the limitations of quantum computing. Despite challenges, they appear to be the best hope for achieving quantum advantage.
NATURE REVIEWS PHYSICS
(2021)
Article
Computer Science, Information Systems
Aliya Tabassum et al.
Summary: The study introduces a novel privacy-preserving intrusion detection pipeline for distributed incremental learning, which eliminates redundant features through innovative extraction techniques and autoencoders. Experimental results demonstrate promising outcomes.
Article
Computer Science, Theory & Methods
Serena Nicolazzo et al.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2020)
Article
Computer Science, Information Systems
Anar A. Hady et al.
Article
Optics
Maria Schuld et al.
Article
Computer Science, Artificial Intelligence
Saurabh Dey et al.
INFORMATION FUSION
(2019)
Article
Computer Science, Artificial Intelligence
Wenxiu Ding et al.
INFORMATION FUSION
(2019)
Article
Computer Science, Information Systems
M. Shamim Hossain
IEEE SYSTEMS JOURNAL
(2017)
Review
Computer Science, Artificial Intelligence
Bahador Khaleghi et al.
INFORMATION FUSION
(2013)
Article
Computer Science, Hardware & Architecture
T Bass
COMMUNICATIONS OF THE ACM
(2000)
