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HELP4DNS: Leveraging the programmable data plane for effective and robust defense against DDoS attacks on DNS

Journal of Network and computer Applications 2025年 240卷

作者： Şahin, Mehmet Emin Demirci, Mehmet Department of Information Systems Graduate School of Informatics Gazi University Ankara Turkey Ankara Turkey Department of Computer Engineering Faculty of Engineering Gazi University Ankara Turkey

DNS is a critical component of the Internet infrastructure, and securing it has been an active research domain, with a particular emphasis on countering DDoS attacks. With the rise of programmable data planes, novel defensive strategies taking advantage of their flexibility and line-rate packet processing capabilities have been developed to counter a range of DDoS attacks. This study proposes two novel methodologies against DNS flood and DNS amplification attacks within programmable data planes using P4. The first approach involves constraining the concurrent active queries per client to mitigate DNS query flood attacks, thereby ensuring that clients generating a high volume of requests adhere to predetermined limits. The proposed method uses concurrent query limits per client by employing a modified token bucket algorithm within an updatable Bloom filter data structure to track and limit DNS queries. This approach effectively rate limits malicious client requests, preventing server overload and safeguarding benign users from any resulting disruptions. The second method is a DNS firewall implemented on the P4 switch situated on the victim's side to prevent DNS amplification attacks. The proposed firewall utilizes an updatable Bloom filter on a P4 switch, enabling stateful processing of DNS queries at the application layer. Additionally, it supports stateful tracking of fragmented DNS responses resulting from the Extension Mechanisms for DNS. While IP fragmentation occurs at the IP layer, the proposed approach achieves stateful tracking of fragmented DNS responses at the application layer. In this manner, only the responses corresponding to legitimate requests are forwarded among the received DNS responses by the victim, while responses stemming from DNS amplification attacks are blocked. Evaluation results have demonstrated that the proposed approach effectively blocks high-volume DNS amplification attack packets with minimal memory space requirements. © 2025 Elsevi

关键词： computer system firewalls

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Practical big data techniques for end-to-end machine learning deployment: a comprehensive review

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Discover Data 2025年第1期3卷 1-18页

作者： El-Sayed, Ahmed Abougabal, Mohamed Lazem, Shaimaa Computer and Systems Engineering Department Faculty of Engineering Alexandria University Alexandria Egypt Informatics Research Institute City of Scientific Research and Technological Applications New Borg El-Arab Egypt

In recent years, data generated from diverse sources has grown exponentially, giving rise to new challenges for processing and analysis under the umbrella of “big data". Although various methods and platforms have been proposed to tackle these issues, there is still a need for a conceptual guide that synthesizes state-of-the-art tools and techniques across the full machine learning (ML) workflow. In this survey, we consolidate research on big data infrastructures, distributed processing frameworks, and ML methods, mapping out an end-to-end conceptual framework that can serve as a reference architecture for end-to-end deployment. Specifically, we discuss key aspects of big data analytics (storage solutions, platforms like Hadoop and Spark, NoSQL databases), preprocessing approaches (dimensionality reduction, instance selection, noise handling), ML algorithms (supervised, unsupervised, and emerging deep learning paradigms), and deployment considerations (monitoring, continuous integration, and versioning). By gathering and integrating these elements, our survey provides a comprehensive overview of existing solutions, clarifies the design choices developers must consider, and identifies research gaps that remain to be addressed.

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Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (Science and Information Organization). All rights reserved.

关键词： Quantum cryptography

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Optimality vs. Generality: Performance Assessment of Meta-heuristics in Educational Timetabling

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IEEE Access 2025年 13卷 75679-75696页

作者： Abdipoor, Sina Yaakob, Razali Goh, Say Leng Abdullah, Salwani Hamdan, Hazlina Kasmiran, Khairul Azhar Universiti Putra Malaysia Faculty of Computer Science and Information Technology Department of Computer Science UPM Serdang Selangor 43400 Malaysia Universiti Malaysia Sabah Labuan International Campus Optimisation and Visual Analytics Research Group Faculty of Computing and Informatics Labuan87000 Malaysia Universiti Kebangsaan Malaysia Faculty of Information Science and Technology UKM Bangi Selangor 43600 Malaysia

Educational timetabling, a principal branch of operations research, presents challenging combinatorial optimization problems widely encountered in educational institutions. Meta-heuristics have commonly been applied to these problems and managed to attain promising performance in terms of optimality. However, their general applicability has been overlooked, hindering their effectiveness as versatile solvers. The limited generalizability of current approaches is the primary hurdle between the literature and real-world applications. This paper addresses this gap by introducing a generality taxonomy and conducting comprehensive theoretical and empirical analyses. This study highlights the adverse impact of extreme parameter tuning on generality, emphasizing the need for more generalized approaches. Furthermore, it introduces a performance assessment framework, penalizing problem-tailored solutions. It also examines the optimality vs. generality performance of the state-of-the-art approaches of the latest university course timetabling benchmark to further reinforce our claim and validate the efficacy of our framework. Our findings indicate that the current literature prioritizes optimality over generality. We believe adopting the proposed assessment framework is crucial for bridging the gap between research and practical applications, enabling fairer comparisons, and encouraging more adaptable approaches. © 2013 IEEE.

关键词： Curricula

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Enhancing Solar Photovoltaic Efficiency:A Computational Fluid Dynamics Analysis

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Energy Engineering 2025年第1期122卷 153-166页

作者： Rahool Rai Fareed Hussain Mangi Kashif Ahmed Sudhakar Kumaramsay Faculty of Mechanical and Automotive Engineering Universiti Malaysia Pahang Al-Sultan AbdullahPekan26600PahangMalaysia Faculty of Engineering Science&Technology Hamdard UniversityKarachi74600Pakistan Electrical Engineering Department SUKKUR IBA UniversitySukkur65200Pakistan Berlin School of Business and Innovation BSBI Faculty of Computer Science and InformaticsBerlin12043Germany Automotive Engineering Centre University Malaysia Pahang Al Sultan AbdullahPekan26600PahangMalaysia Centre for Research in Advanced Fluid&Processes(Fluid Centre) Universiti Malaysia Pahang Al-Sultan Abdullah(UMPSA)Paya BesarKuantan26300PahangMalaysia

The growing need for sustainable energy solutions,driven by rising energy shortages,environmental concerns,and the depletion of conventional energy sources,has led to a significant focus on renewable *** energy,among the various renewable sources,is particularly appealing due to its abundant ***,the efficiency of commercial solar photovoltaic(PV)modules is hindered by several factors,notably their conversion efficiency,which averages around 19%.This efficiency can further decline to 10%–16%due to temperature increases during peak sunlight *** study investigates the cooling of PV modules by applying water to their front surface through Computational fluid dynamics(CFD).The study aimed to determine the optimal conditions for cooling the PV module by analyzing the interplay between water film thickness,Reynolds number,and their effects on temperature reduction and heat *** CFD analysis revealed that the most effective cooling condition occurred with a 5 mm thick water film and a Reynolds number of *** specific parameters were found to maximize the heat transfer and temperature reduction *** finding is crucial for the development of practical and efficient cooling systems for PV modules,potentially leading to improved performance and longevity of solar *** cooling fluids or advanced cooling techniques that might offer even better efficiency or practical benefits.

关键词： PV module efficiency water film thickness reynolds number CFD analysis PV/T renewable energy

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Attackers Can Do Better: Over- and Understated Factors of Model Stealing Attacks

arXiv

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arXiv 2025年

作者： Oliynyk, Daryna Mayer, Rudolf Rauber, Andreas CDL AsTra Faculty of Computer Science University of Vienna Vienna Austria SBA Research Vienna Austria Institute of Information Systems Engineering Faculty of Informatics TU Wien Vienna Austria

Machine learning (ML) models were shown to be vulnerable to model stealing attacks, which lead to intellectual property infringement. Among other attack methods, substitute model training is an all-encompassing attack applicable to any machine learning model whose behaviour can be approximated from input-output queries. Whereas previous works mainly focused on improving the performance of substitute models by, e.g. developing a new substitute training method, there have been only limited ablation studies that try to understand the impact the strength of an attacker has on the substitute model’s performance. As a result, different authors came to diverse, sometimes contradicting, conclusions. In this work, we exhaustively examine the ambivalent influence of different factors resulting from varying the attacker’s capabilities and knowledge on a substitute training attack. Our findings suggest that some of the factors that have been considered important in the past are, in fact, not that influential;instead, we discover new correlations between attack conditions and success rate. In particular, we demonstrate that better-performing target models enable higher-fidelity attacks and explain the intuition behind this phenomenon. Further, we propose to shift the focus from the complexity of target models toward the complexity of their learning tasks. Therefore, for the substitute model, rather than aiming for a higher architecture complexity, we suggest focusing on getting data of higher complexity and an appropriate architecture. Finally, we demonstrate that even in the most limited data-free scenario, there is no need to overcompensate weak knowledge with unrealistic capabilities in the form of millions of queries. Our results often exceed or match the performance of previous attacks that assume a stronger attacker, suggesting that these stronger attacks are likely endangering a model owner’s intellectual property to a significantly higher degree than shown until now. Cop

关键词： Adversarial machine learning

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Job Resumes Recommendation using Integration of Fuzzy Discernibility Matrix Feature Selection and Convolutional Neural Network Multi-label Text Classification

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第13期49卷 49-58页

作者： Hayami, Regiolina Yusoff, Nooraini Daud, Kauthar Mohd Fatma, Yulia Department of Informatics Engineering Faculty Computer Science Universitas Muhammadiyah Riau Indonesia Faculty of Data Science and Computing Universiti Malaysia Kelantan 16100 Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia 43600 Malaysia

The manual analysis of job resumes poses specific challenges, including the time-intensive process and the high likelihood of human error, emphasizing the need for automation in content-based recommendations. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNN) for Multi-label Text Classification (MLTC), offer a promising solution for addressing these challenges through artificial intelligence. While CNN is renowned for its robust feature extraction capabilities, it faces specific challenges such as managing high-dimensional data and poor data interpretability. To address these limitations, this study employs the Fuzzy Discernibility Matrix (FDM) feature selection technique to determine the relevance of skills for each job vacancy. FDM assigns weights to the features of each job category and ranks their relevance based on the highest scores, which are then utilized in the MLTC CNN model. The integration of FDM and MLTC CNN serves as the foundation for generating content-based recommendations derived from key features in job resumes. This study produces a content-based job recommendation system that displays the top three job categories, accompanied by explanations of the skills supporting each selected category. These recommendations also consider cosine similarity values between analyzed items and the integrated results of FDM and MLTC CNN. The application of FDM for feature weighting has been proven to enhance multi-label text classification outcomes by providing better insights during the feature selection process. With a recall of 97.26%, precision of 94.81%, and accuracy of 98.58%, the MLTC model integrating FDM feature selection and CNN demonstrates robust performance characteristics in content-based job recommendation tasks. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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A STUDY ON SOME CLASSES OF PANTOGRAPH LANGEVIN FRACTIONAL DIFFERENTIAL PROBLEMS WITH NONLOCAL CONDITIONS

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Journal of Mathematical Sciences (United States) 2025年 1-18页

作者： Bouzid, Houari Benali, Abdelkader Salim, Abdelkrim Erhan, İnci M. Department of Mathematics Faculty of Exact Science and Informatics Laboratory of Mathematics and Applications (LMA) Hassiba Benbouali University of Chlef Ouled Fares Chlef 02000 Algeria Faculty of Technology Hassiba Benbouali University of Chlef Chlef 02000 Algeria Laboratory of Mathematics Djillali Liabes University of Sidi Bel-Abbes Sidi Bel-Abbes 22000 Algeria Department of Computer Engineering Aydın Adnan Menderes University Aydın 09010 Turkey

This paper concentrates on the examination of a class of nonlocal initial value problems for Langevin fractional differential equations featuring the ψ-Caputo fractional derivative within Banach spaces. By utilizing Banach’s fixed-point theorem, we establish the uniqueness of the solutions to these problems. The existence of solutions is then confirmed using Schaefer’s and Darbo’s fixed-point theorems. Finally, we provide examples to illustrate our findings. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Darbo theorem Fractional differential equations (FDE) Measure of noncompactness ψ-fractional integral

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SEMU: Singular Value Decomposition for Efficient Machine Unlearning

arXiv

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arXiv 2025年

作者： Sendera, Marcin Struski, Lukasz Ksiazek, Kamil Musiol, Kryspin Tabor, Jacek Rymarczyk, Dawid Faculty of Mathematics and Computer Science Jagiellonian University Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Poland Ardigen SA Poland

While the capabilities of generative foundational models have advanced rapidly in recent years, methods to prevent harmful and unsafe behaviors remain underdeveloped. Among the pressing challenges in AI safety, machine unlearning (MU) has become increasingly critical to meet upcoming safety regulations. Most existing MU approaches focus on altering the most significant parameters of the model. However, these methods often require fine-tuning substantial portions of the model, resulting in high computational costs and training instabilities, which are typically mitigated by access to the original training dataset. In this work, we address these limitations by leveraging Singular Value Decomposition (SVD) to create a compact, low-dimensional projection that enables the selective forgetting of specific data points. We propose Singular Value Decomposition for Efficient Machine Unlearning (SEMU), a novel approach designed to optimize MU in two key aspects. First, SEMU minimizes the number of model parameters that need to be modified, effectively removing unwanted knowledge while making only minimal changes to the model’s weights. Second, SEMU eliminates the dependency on the original training dataset, preserving the model’s previously acquired knowledge without additional data requirements. © 2025, CC BY.

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ClipArtGAN: An Application of Pix2Pix Generative Adversarial Network for Clip Art Generation

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Multimedia Tools and Applications 2025年 1-25页

作者： Elnabawy, Reham H. Abdennadher, Slim Hellwich, Olaf Eldawlatly, Seif Technische Universität Berlin Berlin Germany Computer Science and Engineering Department Faculty of Media Engineering and Technology German University in Cairo Cairo Egypt Computer Science Department Faculty of Informatics and Computer Science German International University New Administrative Capital Egypt Computer and Systems Engineering Department Faculty of Engineering Ain Shams University 1 El-sarayat st. Abbassia Cairo Egypt Computer Science and Engineering Department The American University in Cairo Cairo Egypt School of Information Technology New Giza University Giza Egypt

An image can convey a thousand words. This statement emphasizes the importance of illustrating ideas visually rather than writing them down. Although detailed image representation is typically instructive, there are several contexts where simplification of image representations may be more informative. One way to provide an abstract representation of an object is via clip art, which is a simple graphic illustration of an object that can be included in documents or presentations. Clip art representation can be utilized in graphic design for concepts expression and daily life objects illustration. Furthermore, clip art had shown remarkable advantages when compared to real images in terms of editing and manipulation since clip art geometry can be easily edited in addition to the feasibility of storing it in a resolution-independent manner. However, it can be difficult to automatically create clip arts that resemble any object. Here, we propose a novel deep learning model, named ClipArtGAN, that utilizes Generative Adversarial Networks (GANs) to convert a photo of an object to its corresponding clip art. To our knowledge, this is the first GAN model based on Pix2Pix architecture that is capable of automatically generating clip art for any arbitrary object. Three training datasets were collected to train and compare two GAN models based on Pix2Pix and CycleGAN architectures. Our results demonstrate that the mean accuracy obtained using ClipArtGAN utilizing Pix2Pix outperforms that of CycleGAN by 58% and that of Google Images-based search by 54%. These results indicate the efficacy of ClipArtGAN in generating representative clip art images. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Clip Art Generation Dataset Collection Generative Adversarial Network Pix2Pix

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