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Machine Learning Approaches in Cybersecurity to Enhance Security in Future Network Technologies

SN computer science 2025年第4期6卷 1-18页

作者： Ahmad, Sultan Haque, Md. Alimul Abdeljaber, Hikmat A. M. Eljialy, A.E.M. Nazeer, Jabeen Mishra, B.K. Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Mohali Punjab Gharuan 140413 India Department of Computer Science Veer Kunwar Singh University Bihar Ara India Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Information Systems College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia Department of Physics Veer Kunwar Singh University Ara 802301 India

As technology continues to evolve rapidly, cybersecurity has become a critical global concern. The increasing sophistication of cyber threats poses significant risks to individuals, businesses, and governments. To combat these threats, cybersecurity tools play a crucial role in monitoring, detecting, and mitigating security risks in digital environments. These tools ensure data protection, prevent unauthorized access, and safeguard sensitive information. However, traditional cybersecurity approaches are struggling to keep pace with emerging cyber-attacks. In response, machine learning (ML) has emerged as a powerful solution for enhancing cybersecurity strategies. ML algorithms enable organizations to analyze large datasets, identify anomalies, and predict potential threats with greater accuracy. Cybersecurity tools, integrated with ML, act as the final line of defense against attacks such as data breaches, identity theft, and system intrusions. This research explores the application of ML in selecting and optimizing cybersecurity models for enterprise ICT systems. It also emphasizes the growing demand for skilled professionals who can develop and implement ML-based security solutions. By examining current trends and future possibilities, this study provides valuable insights into the role of ML in strengthening cybersecurity measures and enhancing overall digital protection for organizations worldwide. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber security Cybercrime Data analysis ML algorithms Network security Threats

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Lower limit of percolation threshold on a square lattice with complex neighborhoods

arXiv

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arXiv 2025年

作者： Cieplucha, Antoni Utnicki, Marcin Woloszyn, Maciej Malarz, Krzysztof AGH University Faculty of Physics and Applied Computer Science al. Mickiewicza 30 Kraków30-059 Poland

In this paper, the 60-year-old concept of long-range interaction in percolation problems introduced by Dalton, Domb, and Sykes, is reconsidered. With Monte Carlo simulation—based on Newman–Ziff algorithm and finite-size scaling hypothesis—we estimate 64 percolation thresholds for random site percolation problem on a square lattice with neighborhoods that contain sites from the 7th coordination zone. The percolation thresholds obtained range from 0.27013 (for the neighborhood that contains only sites from the 7th coordination zone) to 0.11535 (for the neighborhood that contains all sites from the 1st to the 7th coordination zone). Similarly to neighborhoods with smaller ranges, the power-law dependence of the percolation threshold on the effective coordination number with exponent close to −1/2 is observed. Finally, we empirically determine the limit of the percolation threshold on square lattices with complex neighborhoods. This limit scales with the inverse square of the mean radius of the neighborhood. The boundary of this limit is touched for threshold values associated with extended (compact) neighborhoods. Copyright © 2025, The Authors. All rights reserved.

关键词： Monte Carlo methods

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Magnetocaloric properties of RE5Pd2In4 (RE = Tb-Tm) compounds with multiple magnetic sublattices

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Physica B: Condensed Matter 2025年 710卷

作者： Hayyu, Altifani Rizky Baran, Stanislaw Szytula, Andrzej Jagiellonian University Doctoral School of Exact and Natural Sciences Faculty of Physics Astronomy and Applied Computer Science prof. Stanislawa Lojasiewicza 11 KrakówPL-30-348 Poland Jagiellonian University Faculty of Physics Astronomy and Applied Computer Science M. Smoluchowski Institute of Physics prof. Stanislawa Lojasiewicza 11 KrakówPL-30-348 Poland

Magnetocaloric performance of the RE5Pd2In4 (RE = Tb-Tm) rare earth compounds has been investigated using measurements of magnetization in the function of temperature and applied magnetic field. The maximum magnetic entropy change (−ΔSMmax) at magnetic flux density change (Δμ0H) of 0–9 T has been determined to be 3.3 Jkg−1K−1 at 62 K for Tb5Pd2In4, 7.0 Jkg−1K−1 at 22 K for Dy5Pd2In4, 12.6 Jkg−1K−1 at 22 K for Ho5Pd2In4, 12.1 Jkg−1K−1 at 17 K for Er5Pd2In4 and 11.9 Jkg−1K−1 at 9.0 K for Tm5Pd2In4. Among RE5Pd2In4, the highest values of parameters characterizing the magnetocaloric effect are found for RE = Ho and Er. Furthermore, for fixed RE element, the RE5Pd2In4 compound displays the highest RCP and RC values when compared to those of its isostructural RE5T2In4 (T = Ni, Pt) analogues. At low temperatures, a distinct inverse magnetocaloric effect is detected for RE5Pd2In4 (RE = Tb and Dy). © 2025 Elsevier B.V.

关键词： Magnetocaloric effects

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Increased rubidium levels in brain regions involved in food intake in obese rats

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Brain Structure and Function 2025年第5期230卷 1-11页

作者： Szczerbowska-Boruchowska, Magdalena Chenczke, Aleksandra Ruszczycki, Blazej Wrobel, Pawel Tokarczyk, Wiktoria Stec, Patryk Sowa, Katarzyna M. Ziomber-Lisiak, Agata Faculty of Physics and Applied Computer Science AGH University of Krakow Krakow Poland SOLARIS National Synchrotron Radiation Centre Jagiellonian University Krakow Poland Faculty of Medicine Jagiellonian University Medical College Krakow Poland

The hypothalamus, particularly its ventromedial and lateral regions, plays a pivotal role in homeostatic appetite regulation and is therefore a significant brain structure in the development of obesity. Additionally, the development of obesity can be caused by improper hedonic regulation, which involves neural circuits and systems associated with pleasure and reward. Several studies indicate a possible link between rubidium (Rb) and obesity, despite this element is not being typically considered influential in vital life processes. The present study, therefore, aims to investigate whether excessive body fat in obese animals alters rubidium levels in brain regions directly or indirectly involved in appetite regulation. The research was conducted on high-calorie diet (HCD)-induced obese rats (OB, n = 8) and their lean counterparts (L, n = 8). The determination of Rb levels in brain areas was performed using synchrotron radiation-based X-ray fluorescence microanalysis (SRXRF). The obtained results show a significantly higher level of Rb in all brain areas examined, although the increase in this element in obese individuals was not the same in all structures. The largest relative difference (over 70%) was observed for the orbitofrontal cortex, and the smallest (about 35%) for the amygdala. Principal component analysis with linear projections demonstrated a clear differentiation between the brain structures of obese and non-obese individuals based on the full elemental composition of tissues, while Rb was the only element that distinguished the obese group in each of the examined brain structures. The results obtained clearly confirm the increase in Rb levels in the brain structures responsible for regulating appetite in obesity.

关键词： Appetite regulation Brain activation Brain structures Obesity Rubidium

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Quantum pushforward designs

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Physical Review A 2025年第3期111卷 032433-032433页

作者： Jakub Czartowski Karol Życzkowski Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University 30-348 Kraków Poland Center for Theoretical Physics (CFT) Polish Academy of Sciences Al. Lotników 32/46 02-668 Warszawa Poland

Designs, structures connected to averaging with respect to a given measure using finite sets of points, have proven themselves to be invaluable tools across the field of quantum information, finding their uses in state and process tomography, key distribution, and others. In this work, we introduce a concept of pushforward designs that allows us to obtain new structures from already existing ones by mapping them between the spaces, with specific examples including simplex designs and mixed state designs from complex projective designs. Based on the general concept, we put forward a structure called channel [t,k]-design, allowing for averaging over space of quantum channels for systems in contact with an environment of dimension k. Based on this notion, we introduce the concept of effective environment dimensionality k*, which we estimate for the IBM Kyoto quantum computer to be below 2.2 for times up to 350µs.

关键词： Quantum channel

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Optimizing BI-RADS 4 Lesion Assessment Using Lightweight Convolutional Neural Network with CBAM in Contrast Enhanced Mammography 1st

Optimizing BI-RADS 4 Lesion Assessment Using Lightweight Con...

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1st Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in Breast Care, Deep-Breath 2024

作者： Oladimeji, Oladosu Ayaz, Hamail McLoughlin, Ian Unnikrishnan, Saritha Faculty of Engineering and Design Atlantic Technological University Sligo Ireland Health and Biomedical Strategic Research Centre Atlantic Technological University Sligo Ireland Faculty of Engineering and Design Atlantic Technological university SligoF91 YW50 Ireland Department of Computer Science and Applied Physics Atlantic Technological university Galway Ireland

ISBN: (纸本)9783031777882

Breast cancer is the leading cause of cancer-related mortality and morbidity among women worldwide. Early detection plays a crucial role in improving survival rates and BI-RADS classification is one of the effective ways of predicting breast cancer. However, BI-RADS Category 4 encompasses a broad spectrum of malignancy probabilities, ranging from over 2% to 95%. Due to the wide malignancy likelihood range and the ambiguous qualitative attributes of BI-RADS 4, patients are subjected to overdiagnosis and unnecessary procedures, such as biopsy, which entail a certain degree of physical trauma as well as financial strain. This study proposed a lightweight CNN where MobileNet serves as the backbone architecture, augmented with the Convolutional Block Attention Module (CBAM), resulting in the MobileNet-CBAM model. The model demonstrated good performance in discriminating BI-RADS 4 category malignant and benign cases in Contrast Enhanced Spectral Mammogram (CESM) with a prediction of 82%, 82% and 0.91 for accuracy, f1-score and roc-auc respectively. Additionally, for clinical friendliness, the model explanation was given using SHAP. Hence, the model presents potential utility in predicting breast cancer for lesions categorized as BI-RADS category 4 in breast imaging. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolution

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Scaling properties of elastic pp cross-section 31

Scaling properties of elastic pp cross-section

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31st International Workshop on Deep Inelastic Scattering, DIS 2024

作者： Praszalowicz, Michal Baldenegro, Cristian Royon, Christophe Staśto, Anna M. Institute of Theoretical Physics Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University S. Lojasiewicza 11 Kraków30-348 Poland Dipartimento di Fisica Sapienza Universitá di Roma Piazzale Aldo Moro 2 Rome00185 Italy Department of Physics and Astronomy The University of Kansas LawrenceKS66045 United States Department of Physics Penn State University University ParkPA16802 United States

We show that the elastic differential pp cross-section has a unique universal property that the ratio of bump-to-dip position is constant from the energies of the ISR to the LHC. We explore this property to compare Geometric Scaling present at the ISR with the recently proposed scaling law at the LHC. We argue that at the LHC, within present experimental uncertainties, there is fact a family of scaling laws. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).

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Yang-Lee zeros for real-space condensation

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Physical Review E 2025年第1期111卷 L012101-L012101页

作者： Zdzislaw Burda Desmond A. Johnston Mario Kieburg Faculty of Physics and Applied Computer Science AGH University of Krakow al. Mickiewicza 30 30-059 Kraków Poland School of Mathematical and Computer Sciences Heriot-Watt University Riccarton Edinburgh EH14 4AS United Kingdom and Maxwell Institute of Mathematical Sciences Edinburgh United Kingdom School of Mathematics and Statistics University of Melbourne Parkville VIC 3010 Australia Faculty of Physics and Applied Computer Science AGH University of Krakow al. Mickiewicza 30 30-059 Kraków Poland and Faculty of Physics Bielefeld University P.O. Box 100131 D-33501 Bielefeld Germany

Using the electrostatic analogy, we derive an exact formula for the limiting Yang-Lee zero distribution in the random allocation model of general weights. This exhibits a real-space condensation phase transition, which is induced by a pressure change. The exact solution allows one to read off the scaling of the density of zeros at the critical point and the angle at which the locus of zeros hits the critical point. Since the order of the phase transition and critical exponents can be tuned with a single parameter for several families of weights, the model provides a useful testing ground for verifying various relations between the distribution of zeros and the critical behavior, as well as for exploring the behavior of physical quantities in the mesoscopic regime, i.e., systems of large but finite size. The main result is that asymptotically the Yang-Lee zeros are images of a conformal mapping, given by the generating function for the weights, of uniformly distributed complex phases.

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Contrastive Learning via Randomly Generated Deep Supervision

Contrastive Learning via Randomly Generated Deep Supervision

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Shibo Ma, Zili Chan, Ka-Hou Liu, Yue Tong, Tong Gao, Qinquan Zhai, Guangtao Liu, Xiaohong Tan, Tao Faculty of Applied Sciences Macao Polytechnic University China Ischemaview Inc. Menlo ParkCA United States College of Physics and Information Engineering Fuzhou University Fuzhou China John Hopcroft Center for Computer Science Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350368741

Unsupervised visual representation learning has gained significant attention in the computer vision community, driven by recent advancements in contrastive learning. Most existing contrastive learning frameworks rely on instance discrimination as a pretext task, treating each instance as a distinct category. However, this often leads to intra-class collision in a large latent space, compromising the quality of learned representations. To address this issue, we propose a novel contrastive learning method that utilizes randomly generated supervision signals. Our framework incorporates two projection heads: one handles conventional classification tasks, while the other employs a random algorithm to generate fixed-length vectors representing different classes. The second head executes a supervised contrastive learning task based on these vectors, effectively clustering instances of the same class and increasing the separation between different classes. Our method, Contrastive Learning via Randomly Generated Supervision(CLRGS), significantly improves the quality of feature representations across various datasets and achieves state-of-the-art performance in contrastive learning tasks. © 2025 IEEE.

关键词： Class collision Contrastive learning Supervised contrastive learning

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Kink movement on a periodic background

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Physical Review E 2025年第2期111卷 024203-024203页

作者： Tomasz Dobrowolski Jacek Gatlik Panayotis G. Kevrekidis Department of Computer Physics and Quantum Computing University of the National Education Commission in Krakow Podchorążych 2 30-084 Cracow Poland AGH University of Krakow Faculty of Physics and Applied Computer Science 30-059 Krakow Poland Department of Mathematics and Statistics University of Massachusetts Amherst Massachusetts 01003-4515 USA

The behavior of the kink in the sine-Gordon (sG) model in the presence of periodic inhomogeneity is studied. An ansatz is proposed that allows for the construction of a reliable effective model with two degrees of freedom. Effective models featuring very good agreement with the original field-theoretic partial differential equation are constructed, including in the nonperturbative region and for relativistic velocities. The numerical solutions of the sG model describing the evolution of the kink in the presence of a barrier as well as in the case of a periodic heterogeneity under the potential additional influence of a switched bias current and/or dissipation were obtained. The results of the field equation and the effective models were favorably compared. The effect of the choice of initial conditions in the field model on the agreement of the results with the effective model is also discussed.

关键词： sine-Gordon equation

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