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Analysis of Autonomous Penetration Testing Through Reinforcement Learning and Recommender Systems.
Sensors (Basel)
January 2025
Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Computer Science and Engineering, Office 431, Universidad Complutense de Madrid (UCM), Calle Profesor José García Santesmases, 9, Ciudad Universitaria, 28040 Madrid, Spain.
Conducting penetration testing (pentesting) in cybersecurity is a crucial turning point for identifying vulnerabilities within the framework of Information Technology (IT), where real malicious offensive behavior is simulated to identify potential weaknesses and strengthen preventive controls. Given the complexity of the tests, time constraints, and the specialized level of expertise required for pentesting, analysis and exploitation tools are commonly used. Although useful, these tools often introduce uncertainty in findings, resulting in high rates of false positives.
View Article and Find Full Text PDFStudy of Positioning Accuracy Parameters in Selected Configurations of a Modular Industrial Robot-Part 1.
Sensors (Basel)
December 2024
Institute of Mechanical Technology, Poznan University of Technology, 60-965 Poznan, Poland.
This article presents the fundamental principles of robot accuracy. It characterizes a modular robot, describes the measurement setup, and outlines the methodology for evaluating positioning accuracy across different configurations of the modular robot (four, five, and six modules) under varying loads of 6, 10, and 16 kg. An analysis was conducted on the impact of load changes on four- and five-module configurations, as well as the effect of configuration changes on the robot's performance with 6 and 10 kg loads.
View Article and Find Full Text PDFEvaluation of Stress Tolerance and Fermentation Performance in Commercial Yeast Strains for Industrial Applications.
Foods
January 2025
Science Center for Future Foods, Jiangnan University, Wuxi 214122, China.
This study evaluates the stress tolerance and metabolic adaptability of twelve yeast strains, including eleven commercial strains from Wyeast Laboratories and one prototrophic laboratory strain, under industrially relevant conditions. Yeast strains were assessed for their fermentation performance and stress responses under glucose limitation, osmotic stress, acid stress, elevated ethanol concentrations, and temperature fluctuations. Results revealed significant variability in glucose consumption, ethanol production, and stress tolerance across strains.
View Article and Find Full Text PDFPrediction of Composite Clinical Outcomes for Childhood Neuroblastoma Using Multi-Omics Data and Machine Learning.
Int J Mol Sci
December 2024
School of Computer Science and Technology, Xidian University, Xi'an 710126, China.
Neuroblastoma is a common malignant tumor in childhood that seriously endangers the health and lives of children, making it essential to find effective prognostic markers to accurately predict their clinical outcomes. The development of high-throughput technology in the biomedical field has made it possible to obtain multi-omics data, whose integration can compensate for missing or unreliable information in a single data source. In this study, we integrated clinical data and two omics data, i.
View Article and Find Full Text PDFApplication of Machine Learning to the Prediction of Surface Roughness in the Milling Process on the Basis of Sensor Signals.
Materials (Basel)
January 2025
Faculty of Mechanical Engineering and Aeronautics, Rzeszow University of Technology, 35-959 Rzeszow, Poland.
This article presents an investigation of the use of machine learning methodologies for the prediction of surface roughness in milling operations, using sensor data as the primary source of information. The sensors, which included current transformers, a microphone, and displacement sensors, captured comprehensive machining signals at a frequency of 10 kHz. The signals were subjected to preprocessing using the Savitzky-Golay filter, with the objective of isolating relevant moments of active material machining and reducing noise.
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