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International Journal of Interactive Mobile Technologies 2025年第3期19卷 170-190页

作者： Al-Nawashi, Malek M. Al-Hazaimeh, Obaida M. Tahat, Nedal M. Gharaibeh, Nasr Abu-Ain, Waleed A. Abu-Ain, Tarik Al-Huson University College Al-Balqa Applied University Irbid Jordan Faculty of Information Technology Department of Information Security and Cybersecurity Philadelphia University Amman Jordan Faculty of Science The Hashemite University Zarqa Jordan College of Science and Computer Engineering Taibah University Yanbu Saudi Arabia College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

The detection of cyberattacks has been increasingly emphasized in recent years, focusing on both infrastructure and people. Conventional security measures such as intrusion detection, firewalls, and encryption are insufficient in protecting cyber systems against growing and changing threats. In order to address this problem, scholars have explored reinforcement learning (i.e., RL) as a potential solution for intricate cybersecurity decision-making difficulties. Nevertheless, the use of RL faces several obstacles, including dynamic attack scenarios, insufficient training data, and the challenge of replicating real-world complexities. This study presents a novel framework that uses deep reinforcement learning (i.e., DRL) to simulate harmful cyberattacks and improve cybersecurity. This study presents an agent-based framework that is capable of ongoing learning and adaptation in a dynamic network security environment. The agent determines the optimal course of action by considering the current state of the network and the rewards it receives for its decisions. The CIC-IDS-2018 database, constructed using Python 3.7 programming, was used. The conducted studies yielded outstanding results, with a detection accuracy of 98.82% achieved for the CIC-IDS-2018 database in cyber-attack classification. © 2025 by the authors of this article. Published under CC-BY.

关键词： Cybersecurity

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Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems

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IEEE Transactions on Evolutionary Computation 2025年

作者： Shiraishi, Hiroki Hayamizu, Yohei Hashiyama, Tomonori Takadama, Keiki Ishibuchi, Hisao Nakata, Masaya Yokohama National University Faculty of Engineering Yokohama240-8501 Japan State University of New York at Binghamton Department of Computer Science BinghamtonNY13902 United States University of Electro-Communications Department of Informatics Tokyo182-8585 Japan University of Tokyo Information Technology Center Chiba277-0882 Japan Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China

Rule representations significantly influence the search capabilities and decision boundaries within the search space of Learning Classifier Systems (LCSs). However, it is very difficult to choose an appropriate rule representation for each problem. Additionally, some problems benefit from using different representations for different subspaces within the input space. Thus, an adaptive mechanism is needed to choose an appropriate rule representation for each rule in LCSs. This article introduces a flexible rule representation using a four-parameter beta distribution and integrates it into a fuzzy-style LCS. The four-parameter beta distribution can form various function shapes, and this flexibility enables our LCS to automatically select appropriate representations for different subspaces. Our rule representation can represent crisp/fuzzy decision boundaries in various boundary shapes, such as rectangles and bells, by controlling four parameters, compared to the standard representations such as trapezoidal ones. Leveraging this flexibility, our LCS is designed to adapt the appropriate rule representation for each subspace. Moreover, our LCS has a generalization bias to produce as many crisp rules as possible. Experimental results on real-world classification tasks show that our LCS significantly outperformed LCSs with popular rule representations in test classification accuracy on up to 17 of the 25 datasets tested. © 1997-2012 IEEE.

关键词： Adversarial machine learning

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On the Difficulty of NOT being Unique: Fingerprinting Users from Wi-Fi Data in Mobile Devices 25

On the Difficulty of NOT being Unique: Fingerprinting Users ...

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Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing

作者： Mariana Cunha Ricardo Mendes Yves-Alexandre de Montjoye João P. Vilela CRACS/INESC TEC CISUC and Department of Computer Science Faculty of Sciences University of Porto Porto Portugal CISUC and Department of Informatics Engineering University of Coimbra Coimbra Portugal Imperial College London London United Kingdom

ISBN: (纸本)9798400706295

The pervasiveness of mobile devices has fostered a multitude of services and applications, but also raised serious privacy concerns. In order to avoid users' tracking and/or users' fingerprinting, smartphones have been tightening the access to unique identifiers. Nevertheless, smartphone applications can still collect diverse data from available sensors and smartphone resources. Using real-world data from a field study we performed, this paper demonstrates the possibility of fingerprinting users from Wi-Fi data in mobile devices and the consequent privacy impact. From the performed analysis, we concluded that a single snapshot of a set of scanned Wi-Fi BSSIDs (MAC addresses) per user is enough to uniquely identify about 99% of the users. In addition, the most frequent Wi-Fi BSSID is sufficient to re-identify more than 90% of the users, a percentage that goes up to 97% of the users with the top-2 scanned BSSIDs. The Wi-Fi SSID (network name) also leads to a re-identification risk of about 83% and 97% with 1 and 2 of the strongest Wi-Fi Access Points (APs), respectively.

关键词： privacy

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Mining Intelligence Hierarchical Feature for Malware Detection 15th

Mining Intelligence Hierarchical Feature for Malware Detecti...

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15th International Conference on Intelligent Computing and Networking, IC-ICN 2024

作者： Davuluri, Sandeep Kumar Soni, Mukesh ALMahadin, Ghayth Rivera, Richard Upadhyay, Jinal Patel, Pavan Department of Information Technology University of the Cumberlands Williamsburg United States Dr. D. Y. Patil Vidyapeeth Dr. D. Y. Patil School of Science & Technology Tathawade Pune India Faculty of Information Technology Al Ahliyya Amman University Amman Jordan Department of Informatics and Computer Science Escuela Politécnica Nacional Quito Ecuador HasmukhGoswami College of Engineering Gujarat Vahelal India Ahmedabad Institute of Technology Gujarat Ahmedabad India

ISBN: (纸本)9789819786305

In recent years, a new strain of malware, known as mining malware, has emerged, resulting in significant losses for its victims due to its encryption techniques. This research proposes an innovative approach to detect mining malware by integrating hierarchical threat intelligence features, focusing on the static attributes of mining malware. From the perspective of mining malware threat intelligence, we train classifiers using distinct layers of features, including byte-level features, Portable Executable (PE) structure features, and mining operation execution features. By leveraging diverse malicious software attributes for detection preferences, we develop a comprehensive mining malware detector based on a hierarchical feature integration methodology. To evaluate our approach, we conducted experiments in both controlled lab settings and real-world datasets. The results indicate that our hierarchical feature-integrated mining malware detection method achieves an impressive accuracy rate of 97.01% on the simulated real-world dataset, demonstrating a substantial 6.13% accuracy improvement compared to the baseline mining malware detection method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Characterization and comparative profiling of piRNAs in serum biopsies of pediatric Wilms tumor patients

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Cancer Cell International 2025年第1期25卷 1-15页

作者： Mohamed, Fatma S. Jalal, Deena Fadel, Youssef M. El-Mashtoly, Samir F. Khaled, Wael Z. Sayed, Ahmed A. Ghazy, Mohamed A. Biotechnology Program Institute of Basic and Applied Science Egypt-Japan University of Science and Technology Alexandria New Borg El-Arab City Egypt Biochemistry ProgramFaculty of Science Minia University El-Minia Egypt Genomics and Metagenomics Program Department of Basic Research Children’s Cancer Hospital Egypt Cairo 57357 Egypt Bioinformatics Group Center for Informatics Science School of Information Technology and Computer Science Nile University Giza Egypt Leibniz Institute of Photonic Technology Albert Einstein-Straße 9 Jena 07745 Germany Department of Pediatric Oncology National Cancer Institute Cairo Egypt Department of Pediatric Oncology Children’s Cancer Hospital Egypt Cairo 57357 Egypt Consultant Pediatric Oncology Mouwasat Hospital Dammam Saudi Arabia Department of Biochemistry Faculty of Science Ain Shams University Cairo Egypt

Piwi-interacting RNAs (piRNAs) are small non-coding RNAs involved in transposon silencing and linked to cancer progression. However, their role in Wilms tumors (WT) remains unexplored. We conducted a thorough analysis and characterization of piRNAs in serum liquid biopsies of WT patients. Our study examined their expression patterns and functional annotations related to WT pathogenesis, as well as their clinical potential for diagnosis and monitoring. We identified 307 piRNAs expressed in WT serum samples, with 4% classified as repeat-related and 96% as non-repeat-related. The most abundant repeat-related piRNAs originated from LINEs retrotransposon, while tRNA-derived piRNAs were the most prevalent among non-repeat-related piRNAs. Furthermore, a distinct profile of 34 piRNAs showed significant differential expression in WT patients compared to healthy controls—22 downregulated and 12 upregulated. The target genes of differentially expressed piRNAs exhibited significant enrichment in biological pathways related to cytokine activity, inflammatory responses, TGF-beta signaling, p38 MAPK, and ErbB signaling. These genes are also involved in DNA damage response, DNA methylation, cell cycle regulation, as well as kidney development and function. Low expression levels of several piRNAs, especially piR-hsa-1,913,711, piR-hsa-28,190, piR-hsa-28,849, piR-hsa-28,848, and piR-hsa-28,318, showed significant diagnostic potential as non-invasive biomarkers for WT (AUC > 0.8, p < 0.05). Their expression levels also significantly correlated with adverse pathological features, including metastasis, anaplasia, and bilateral WT development. In conclusion, non-transposon-related piRNAs may serve as reliable biomarkers for WT and possess potential non-germline functions, particularly in regulating DNA methylation, cell growth, immune responses, and immune responses. Further studies are warranted to elucidate their functional significance. © The Author(s) 2025.

关键词： Blood-based biomarkers Diagnostic potential Non-germline functions Non-transposon-related PiRNAs PiRNAome profiling Wilms tumor

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On the Complexity of Computing a Fastest Temporal Path in Interval Temporal Graphs

arXiv

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arXiv 2025年

作者： Aubian, Guillaume Brunelli, Filippo Dragan, Feodor Ducoffe, Guillaume Habib, Michel Ibiapina, Allen Viennot, Laurent IRIF CNRS Université Paris Cité France Kent State University United States University of Bucharest Faculty of Mathematics and Computer Science National Institute for Research and Development in Informatics Romania Inria DI ENS Paris France

Temporal graphs arise when modeling interactions that evolve over time. They usually come in several flavors, depending on the number of parameters used to describe the temporal aspects of the interactions: time of appearance, duration, delay of transmission. In the point model, edges appear at specific points in time, while in the more general interval model, edges can be present over multiple time intervals. In both models, the delay for traversing an edge can change with each edge appearance. When time is discrete, the two models are equivalent in the sense that the presence of an edge during an interval is equivalent to a sequence of point-in-time occurrences of the edge. However, this transformation can drastically change the size of the input and has complexity issues. Indeed, we show a gap between the two models with respect to the complexity of the classical problem of computing a fastest temporal path from a source vertex to a target vertex, i.e. a path where edges can be traversed one after another in time and such that the total duration from source to target is minimized. It can be solved in near-linear time in the point model, while we show that the interval model requires quadratic time under classical assumptions of fine-grained complexity. With respect to linear time, our lower bound implies a factor of the number of vertices, while the best known algorithm has a factor of the number of underlying edges. We show a similar gap for computing a shortest temporal path, i.e. with minimum number edges. Interestingly, we show that near-linear time for fastest temporal path is possible in the interval model when restricted to all delays being zero, i.e. traversing an edge is instantaneous. Copyright © 2025, The Authors. All rights reserved.

关键词： Network theory (graphs)

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Enhancing signal-to-noise ratio in LED-based photoacoustic imaging using Conditional Denoising Diffusion Probabilistic Model

Enhancing signal-to-noise ratio in LED-based photoacoustic i...

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Photons Plus Ultrasound: Imaging and Sensing 2025

作者： Barratov, Farrukh Rajendran, Praveenbalaji Singh, Mithun Kuniyil Ajith Francis, Kalloor Joseph Awasthi, Navchetan Department of Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Harvard University United States Cyberdyne Inc. Netherlands Erasmus MC Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam Netherlands

ISBN: (纸本)9781510683860

Photoacoustic imaging (PAI) is an emerging medical imaging technique with applications in blood oxygen imaging and tumor detection. LED-based PAI offers a cost-effective and accessible alternative but faces challenges in high-frame-rate applications due to significant noise, necessitating extensive signal averaging. In this work, we investigate the use of deep learning techniques, specifically a conditional Denoising Diffusion Probabilistic Model (cDDPM), for denoising photoacoustic images obtained from an LED-based system. Our study evaluates the effectiveness of cDDPM in reconstructing image features and explores optimization through scheduler modifications. We implement a cosine scheduler to reduce redundant denoising steps, significantly improving inference efficiency while maintaining high image quality. These results demonstrate the potential of diffusion models for enhancing low-frame-averaged photoacoustic images. © 2025 SPIE.

关键词： Image enhancement

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A novel real-time noise-resilient zeroing neural network and its applications to matrix problem solving

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Mathematics and computers in Simulation 2025年

作者： Yang, Yiguo Wu, Pin Katsikis, Vasilios N. Li, Shuai Feng, Weibing The School of Computer Engineering and Science Shanghai University Shanghai200444 China The Department of Economics Division of Mathematics-Informatics and Statistics-Econometrics National and Kapodistrian University of Athens Athens10559 Greece The Faculty of Information Technology and Electrical Engineering University of Oulu Oulu90570 Finland

Given the critical role of zeroing neural networks (ZNN) in various fields and the practical demand for models in effectively resisting real-time noise, this study introduces a novel anti-noise integral zeroing neural network (AN-IZNN) model alongside its enhanced counterpart (EAN-IZNN), for the applications of matrix problem solving. Theoretical analysis demonstrates their ability to achieve convergence even under different noise conditions. Both theoretical analyses and simulation validations highlight the superior performance of the proposed models over existing neural network models. Notably, the root mean square error of the proposed AN-IZNN and EAN-IZNN models is reduced by 92.6249% and 91.4178%, respectively, compared to scenarios without the proposed method, demonstrating the effectiveness of the solution. © 2025

关键词： Neural network models

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Supply Chain Sustainability in Disruption: SC Intelligence, Customer Knowledge Transfer, and the Mediating Role of IS/IT

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IEEE Engineering Management Review 2025年

作者： Ratna, Silvia Saide, Saide Rater, Salya Svyazhanovna, Baizakova Saule Herzavina, Herzavina Faculty of Information Technology Department of Informatics Engineering and Department of Information Systems Banjarmasin Indonesia State Islamic University of Sultan Syarif Kasim Riau Universitas Islam Negeri Sultan Syarif Kasim Riau-UIN SUSKA Riau Department of Information Systems Faculty of Science and Technology Indonesia PRO Knowledge Indonesia Pekanbaru Indonesia Universitas Pahlawan Tuanku Tambusai Indonesia Lembaga Enreach UIN Suska Riau Indonesia Politeknik Aceh Selatan Department of Informatics Engineering Aceh Indonesia Information Communication Technologies Robotics in Arkalyk Pedagogical Institute Master of Natural Sciences Educational Program for the Informatics Computer Science Kazakhstan Universitas Pahlawan Tuanku Tambusai Department of Informatics Engineering Indonesia National Taiwan University of Science and Technology Taiwan

Knowledge resource and information system/technology (IS/IT) capability have been considered to improve firm performance, however there is still a gap regarding the sustainability of supply chain to face and recover from disruption (e.g., COVID-19). Questionnaire-based surveys was adopted and received feedback from sixty (160) Indonesian companies or about 28.07% of response rate. © 1973-2011 IEEE.

关键词： customer knowledge transfer disruption information system/technology (IS/IT) responding knowledge management resource-based view (RBV) Supply chain (SC) sustainability supply chain knowledge transfer

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Data Dams: A Novel Framework for Regulating and Managing Data Flow in Large-Scale Systems

arXiv

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arXiv 2025年

作者： Bouke, Mohamed Aly Abdullah, Azizol Cengiz, Korhan Ivković, Nikola Mihaljević, Ivan Mohamud, Mudathir Ahmed Kowrina, Ahmed Department of Communication Technology and Network Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang43400 Malaysia Department of Electrical-Electronics Engineering Biruni University Istanbul Turkey Faculty of Organization and Informatics University of Zagreb Pavlinska 2 Varaždin42000 Croatia Faculty of Computing SIMAD University Mogadishu252 Somalia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Serdang43400 Malaysia Département d’Informatique et Mathématiques Faculté des Sciences et Techniques Université de Nouakchott Al Aasriya Nouakchott880 Mauritania

In the era of big data, managing dynamic data flows efficiently is crucial as traditional storage models struggle with real-time regulation and risk overflow. This paper introduces Data Dams, a novel framework designed to optimize data inflow, storage, and outflow by dynamically adjusting flow rates to prevent congestion while maximizing resource utilization. Inspired by physical dam mechanisms, the framework employs intelligent sluice controls and predictive analytics to regulate data flow based on system conditions such as bandwidth availability, processing capacity, and security constraints. Simulation results demonstrate that the Data Dam significantly reduces average storage levels (371.68 vs. 426.27 units) and increases total outflow (7999.99 vs. 7748.76 units) compared to static baseline models. By ensuring stable and adaptive outflow rates under fluctuating data loads, this approach enhances system efficiency, mitigates overflow risks, and outperforms existing static flow control strategies. The proposed framework presents a scalable solution for dynamic data management in large-scale distributed systems, paving the way for more resilient and efficient real-time processing architectures. © 2025, CC BY.

关键词： Queueing theory

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