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Computer Security

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Classification and Explanation for Intrusion Detection System Based on Ensemble Trees and SHAP Method.

Sensors (Basel, Switzerland)
In recent years, many methods for intrusion detection systems (IDS) have been designed and developed in the research community, which have achieved a perfect detection rate using IDS datasets. Deep neural networks (DNNs) are representative examples a...

Medicolite-Machine Learning-Based Patient Care Model.

Computational intelligence and neuroscience
This paper discusses the machine learning effect on healthcare and the development of an application named "Medicolite" in which various modules have been developed for convenience with health-related problems like issues with diet. It also provides ...

Attacks to Automatous Vehicles: A Deep Learning Algorithm for Cybersecurity.

Sensors (Basel, Switzerland)
Rapid technological development has changed drastically the automotive industry. Network communication has improved, helping the vehicles transition from completely machine- to software-controlled technologies. The autonomous vehicle network is contr...

Development of an IoT Architecture Based on a Deep Neural Network against Cyber Attacks for Automated Guided Vehicles.

Sensors (Basel, Switzerland)
This paper introduces an integrated IoT architecture to handle the problem of cyber attacks based on a developed deep neural network (DNN) with a rectified linear unit in order to provide reliable and secure online monitoring for automated guided veh...

Design of Secure Microcontroller-Based Systems: Application to Mobile Robots for Perimeter Monitoring.

Sensors (Basel, Switzerland)
This paper describes an original methodology for the design of microcontroller-based physical security systems and its application for the system of mobile robots. The novelty of the proposed methodology lies in combining various design algorithms on...

Temporal Weighted Averaging for Asynchronous Federated Intrusion Detection Systems.

Computational intelligence and neuroscience
Federated learning (FL) is an emerging subdomain of machine learning (ML) in a distributed and heterogeneous setup. It provides efficient training architecture, sufficient data, and privacy-preserving communication for boosting the performance and fe...

Byzantine-robust federated learning via credibility assessment on non-IID data.

Mathematical biosciences and engineering : MBE
Federated learning is a novel framework that enables resource-constrained edge devices to jointly learn a model, which solves the problem of data protection and data islands. However, standard federated learning is vulnerable to Byzantine attacks, wh...

A machine and human reader study on AI diagnosis model safety under attacks of adversarial images.

Nature communications
While active efforts are advancing medical artificial intelligence (AI) model development and clinical translation, safety issues of the AI models emerge, but little research has been done. We perform a study to investigate the behaviors of an AI dia...

Is Homomorphic Encryption-Based Deep Learning Secure Enough?

Sensors (Basel, Switzerland)
As the amount of data collected and analyzed by machine learning technology increases, data that can identify individuals is also being collected in large quantities. In particular, as deep learning technology-which requires a large amount of analysi...

Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions.

Sensors (Basel, Switzerland)
The Industrial Internet of Things (IIoT) refers to the use of smart sensors, actuators, fast communication protocols, and efficient cybersecurity mechanisms to improve industrial processes and applications. In large industrial networks, smart devices...