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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Singh, Nongmeikapam Thoiba Rani, Jyoti Sharma, Prince Mishra, Akriti Yadav, Ankit Raj, Shivani Chandigarh University Department of Computer Science and Engineering Punjab India University Institute of Computing Chandigarh University Punjab India

ISBN: (纸本)9798350383522

This paper delves into the pervasive threat of phishing in the e-commerce sector, where adversaries impersonate financial institutions to perpetrate identity theft. The modus operandi involves sending deceptive emails urging users to click on fraudulent links, leading them to fake websites mimicking authentic ones. Focused on URL-based detection, the study employs sophisticated algorithms and machine learning techniques, utilizing a dataset for training and testing to identify phishing URLs. The paper outlines various types of phishing attacks, including deceptive, spell, clone, whale, link manipulation, and voice phishing. Prevention strategies, such as spam avoidance, secure communication, and user education, are discussed. The methodology involves frontend and backend development using Python, JavaScript, HTML, CSS, PHP, and MySQL, incorporating machine learning for analysis. The website provides a user-friendly interface with a reporting option, contributing to a robust defense against phishing threats. Results indicate the effectiveness of URL-based detection, emphasizing the importance of staying updated on evolving phishing trends and employing a multifaceted approach for enhanced organizational cybersecurity. © 2024 IEEE.

关键词： data security phishing phishing detection URL

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Insights from Univalent Foundations: A Case Study Using Double Categories 33

Insights from Univalent Foundations: A Case Study Using Doub...

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33rd EACSL Annual Conference on computer science Logic, CSL 2025

作者： Rasekh, Nima van der Weide, Niels Ahrens, Benedikt North, Paige Randall Institute of Mathematics and Computer Science University of Greifswald Germany Institute for Computing and Information Sciences Radboud University Nijmegen Netherlands Delft University of Technology Netherlands University of Birmingham United Kingdom Department of Information and Computing Sciences Mathematical Institute Utrecht University Netherlands

ISBN: (纸本)9783959773621

Category theory unifies mathematical concepts, aiding comparisons across structures by incorporating not just objects, but also morphisms capturing interactions between objects. Of particular importance in some applications are double categories, which are categories with two classes of morphisms, axiomatizing two different kinds of interactions between objects. These have found applications in many areas of mathematics and theoretical computer science, for instance, the study of lenses, open systems, and rewriting. However, double categories come with a wide variety of equivalences, which makes it challenging to transport structure along equivalences. To deal with this challenge, we propose the univalence maxim: each notion of equivalence of categorical structures has a corresponding notion of univalent categorical structure which induces that notion of equivalence. We also prove corresponding univalence principles, which allow us to transport structure and properties along equivalences. In this way, the usually informal practice of reasoning modulo equivalence becomes grounded in an entirely formal logical principle. We apply this perspective to various double categorical structures, such as (pseudo) double categories and double bicategories. Concretely, we characterize and formalize their definitions in Coq UniMath up to chosen equivalences, which we achieve by establishing their univalence principles. © Nima Rasekh, Niels van der Weide, Benedikt Ahrens, and Paige Randall North.

关键词： Choquet integral

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Minimum Consistent Subset in Trees and Interval Graphs 44

Minimum Consistent Subset in Trees and Interval Graphs

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44th IARCS Annual Conference on Foundations of Software Technology and Theoretical computer science, FSTTCS 2024

作者： Banik, Aritra Das, Sayani Maheshwari, Anil Manna, Bubai Nandy, Subhas C. Krishna Priya, K.M. Roy, Bodhayan Roy, Sasanka Sahu, Abhishek National Institute of Science Education and Research An OCC of Homi Bhabha National Institute Bhubaneswar India Theoretical Computer Science The Institute of Mathematical Sciences Chennai India School of Computer Science Carleton University OttawaON Canada Department of Mathematics Indian Institute of Technology Kharagpur India Advanced Computing and Microelectronics Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9783959773553

In the Minimum Consistent Subset (MCS) problem, we are presented with a connected simple undirected graph G, consisting of a vertex set V (G) of size n and an edge set E(G). Each vertex in V (G) is assigned a color from the set {1, 2, ⋯, c}. The objective is to determine a subset V′ ⊆ V (G) with minimum possible cardinality, such that for every vertex v ∈ V (G), at least one of its nearest neighbors in V′ (measured in terms of the hop distance) shares the same color as v. The decision problem, indicating whether there exists a subset V′ of cardinality at most l for some positive integer l, is known to be NP-complete even for planar graphs. In this paper, we establish that the MCS problem is NP-complete on trees. We also provide a fixed-parameter tractable (FPT) algorithm for MCS on trees parameterized by the number of colors (c) running in O(26cn6) time, significantly improving the currently best-known algorithm whose running time is O(24cn2c+3). In an effort to comprehensively understand the computational complexity of the MCS problem across different graph classes, we extend our investigation to interval graphs. We show that it remains NP-complete for interval graphs, thus enriching graph classes where MCS remains intractable. © 2024 Aritra Banik, Sayani Das, Anil Maheshwari, Bubai Manna, Subhas C. Nandy, Krishna Priya K. M., Bodhayan Roy, Sasanka Roy, and Abhishek Sahu.

关键词： Undirected graphs

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Learning in sinusoidal spaces with physics-informed neural networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 985-1000页

作者： Wong, Jian Cheng Ooi, Chin Chun Gupta, Abhishek Ong, Yew Soon Institute of High Performance Computing Agency for Science Technology and Research Singapore138632 Singapore School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Singapore Institute of Manufacturing Technology Agency for Science Technology and Research Singapore138632 Singapore Agency for Science Technology and Research Singapore138632 Singapore Data Science and Artificial Intelligence Research Centre School of Computer Science and Engineering Nanyang Technological University Singapore138632 Singapore

A physics-informed neural network (PINN) uses physics-Augmented loss functions, e.g., incorporating the residual term from governing partial differential equations (PDEs), to ensure its output is consistent with fundamental physics laws. However, it turns out to be difficult to train an accurate PINN model for many problems in practice. In this article, we present a novel perspective of the merits of learning in sinusoidal spaces with PINNs. By analyzing behavior at model initialization, we first show that a PINN of increasing expressiveness induces an initial bias around flat output functions. Notably, this initial solution can be very close to satisfying many physics PDEs, i.e., falling into a localminimum of the PINN loss that onlyminimizes PDE residuals, while still being far from the true solution that jointly minimizes PDE residuals and the initial and/or boundary conditions. It is difficult for gradient descent optimization to escape from such a local minimum trap, often causing the training to stall. We then prove that the sinusoidalmapping of inputs-in an architecture we label as sf-PINN-is effective to increase input gradient variability, thus avoiding being trapped in such deceptive local minimum. The level of variability can be effectively modulated to match high-frequency patterns in the problem at hand. A key facet of this article is the comprehensive empirical study that demonstrates the efficacy of learning in sinusoidal spaces with PINNs for a wide range of forward and inversemodeling problems spanning multiple physics domains. Impact Statement-Falling under the emerging field of physicsinformed machine learning, PINN models have tremendous potential as a unifying AI framework for assimilating physics theory and measurement data. However, they remain infeasible for broad science and engineering applications due to computational cost and training challenges, especially for more complex problems. Instead of focusing on empirical demonstration of appli

关键词： Gradient methods

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Grover’s search finds new applications in continuous optimization and spectral analysis

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science China(Physics,Mechanics & Astronomy) 2025年第6期68卷 212-213页

作者： Xiaoming Sun State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049China

A novel quantum search algorithm tailored for continuous optimization and spectral problems was proposed recently by a research team from the University of Electronic science and Technology of China to broaden quantum computation frontiers and enrich its application *** computing has traditionally excelled at tackling discrete search challenges,but many important applications from large-scale optimization to advanced physics simulations necessitate searching through continuous *** continuous search problems involve uncountably infinite solution spaces and bring about computational complexities far beyond those faced in conventional discrete *** draft,titled“Fixed-Point Quantum Continuous Search Algorithm with Optimal Query Complexity”,takes on the core challenge of performing search tasks in domains that may be uncountably infinite,offering theoretical and practical insights into achieving quantum speedups in such settings[1].

关键词： advanced physics simulations discrete search challengesbut quantum computation optimization spectral problems spectral analysis fixed point algorithm quantum search algorithm continuous optimization

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A Detailed Study on Anaphora Resolution System for Asian Languages

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SN computer science 2024年第7期5卷 811页

作者： Prajapati, Priyanka Goyal, Vishal Kaur, Kawaljit Department of Computer Science Punjabi University Punjab Patiala 147002 India University Institute of Computing Chandigarh University Punjab Mohali 140413 India

In the field of Computational Linguistics, “anaphora" is a term used to denote the deliberate utilization of a word earlier mentioned in the discourse to mitigate redundancy. Anaphora resolution, a crucial element in this domain, involves identifying the phrase corresponding to a pronoun already introduced in the text or discourse. The importance of anaphora resolution is pivotal in numerous Natural Language Processing(NLP) applications, attracting many researchers. These applications encompass question-answer systems, Text Summarization, Machine Translation, Language Generation, Dialog Systems, and Information Extraction. The intricacy of resolving anaphora stems from the vast linguistic variation among different languages. This document offers the reader an in-depth comprehension of anaphora resolution, particularly within Natural Language Processing. Furthermore, it endeavors to illuminate a range of accessible resources for resolving anaphora, with a particular emphasis on Asian languages. This study examines the hitherto uncharted languages within the Asian continent and proposes novel avenues for emerging scholars. Our research team intends to focus on one of these unexplored linguistic domains for further investigation. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Anaphora resolution (AR) Asian languages Natural language processing (NLP) Relationship dependency

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Unveiling Security Vulnerabilities in NFTs: A Comprehensive Risk Assessment 4

Unveiling Security Vulnerabilities in NFTs: A Comprehensive ...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Verma, Ravi Chandrawanshi, Kamlesh Soni, Gaurav Jain, Garima Nigam, Shekhar Jain, Neelesh Vit Bhopal University School of Computing Science and Engineering Bhopal India Samrat Ashok Technological Institute Department of Computer Science Vidisha India Sam Global University School of Computer Science and Engineering Bhopal India Prestiage Institute of Science and Technology Department of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798331518981

The biggest invention of Non-Fungible Tokens System (NFTs), that defines its role digitally using the self-generated concept of proof of ownership, which comes as a result of continues progress and advancement in Blockchain Technology This study aims to provide a comprehensive understanding of NFTs, their foundational technology, and the platforms enabling their trade. It highlights the key challenges of implementing NFT technology, focusing on issues related to ownership, governance, security, privacy, and environmental concerns. The research explores the inherent risks in the evolving NFT ecosystem, offering insights into the nature and scale of these threats. Additionally, it proposes a set of actionable solutions for participants and stakeholders to mitigate these risks. By categorizing core hazards within a simplified taxonomy, this paper identifies key vulnerabilities and outlines strategies for addressing them to enhance the safety and sustainability of the NFT space. © 2024 IEEE.

关键词： Differential privacy

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CVS-FLN: a novel IoT-IDS model based on metaheuristic feature selection and neural network classification model

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Multimedia Tools and Applications 2024年第39期83卷 86557-86591页

作者： Geetha, R. Jegatheesan, A. Dhanaraj, Rajesh Kumar Vijayalakshmi, K. Nayyar, Anand Arulkumar, V. Velmurugan, J. Thavasimuthu, Rajendran Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Campus Chengalpattu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Pune India Department of Computer Science and Engineering Ramco Institute of Technology Tamil Nadu Rajapalayam India Graduate School Faculty of Information Technology Duy Tan University Da Nang550000 Viet Nam School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

The Internet of Things (IoT) is one of the technologies that will be used all over the world in the future, and its security and privacy features are the primary concerns. However, the most critical limitation to overcome before the IoT's widespread use is addressing its security concerns. One of the most critical tasks to address the security problems posed by the IoT is the detection of network intrusions. Intrusion Detection Systems (IDS) for the IoT face substantial challenges because of the functional and physical diversity of the devices. In the rapidly growing IoT industry, the use of IDS is essential for ensuring security since many devices communicate efficiently. IDS are crucial for continual monitoring and responding to any security risks, ensuring the integrity and reliability of interconnected IoT networks. The primary objective of this research is to develop a feature-selection-based intrusion classification model. Therefore, we develop an IoT-IDS feature selection-based classification model to detect intrusions. We propose a metaheuristic algorithm, the Chaotic Vortex Search (CVS) algorithm, for feature selection. The Fast-Learning Network (FLN), an artificial neural network (ANN) model, is additionally proposed for classification. We use the IDS datasets, such as CIC IDS-2017 and BoT-IoT, to evaluate our research model. To test and validate the proposed model, extensive experiments were performed, and the outcomes stated that the CVS-FLN model achieved 99.77% accuracy, 99.92% specificity, 99.60% precision, 99.81% detection rate, and a 99.72% F1-score in the CIC IDS dataset, and 99.68% accuracy, 99.30% precision, 99.83% specificity, 99.11% detection rate, and a 99.21% F1-score in evaluating the BoT-IoT dataset. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Intrusion detection

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Potato Plant Disease Detection and Classification for Improved Agriculture 2

Potato Plant Disease Detection and Classification for Improv...

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2nd International Conference on Automation, computing and Renewable Systems, ICACRS 2023

作者： Chaudary, Sarthak Bakshi, Khushi Iniyan, S. Department of Computer Science and Engineering SRM Institute of Science and Technolgy Kaatankulathur Tamilnadu Chennai India Department of Computing Technologies School of Computing SRM Institute of Science and Technolgy Kaatankulathur Tamilnadu Chennai India

ISBN: (纸本)9798350340235

Agriculture is evolving, and deep learning techniques, especially convolutional neural networks, play a pivotal role. They streamline disease detection, providing farmers with timely information to deploy effective disease control strategies and amplify crop yields. Imagine a world where diseases in plants are detected with unparalleled speed and precision. In the age of technological advancement, agriculture stands at the brink of transformation. The proposed work delves into the world of innovation, by harnessing the power of cutting-edge technologies to safeguard crops. Embarking on a journey where image processing, artificial intelligence, and machine learning join forces to become the guardians of the farmlands. This is the vision that drives the project. The magic of image processing techniques, enabling to swiftly identify and categorize leaf diseases. As a result, farmers can now take immediate, well-informed actions, preventing their crops from getting plagued by diseases and ensuring a healthy yield of crops. The project introduces artificial intelligence and machine learning as formidable allies in the quest to diagnose crop diseases at an early stage. These intelligent algorithms detect disease symptoms even before the human eye can perceive them. These models, powered by convolutional neural networks, meticulously analyze leaf images, extract essential features, and classify diseases with remarkable accuracy. By fusing image processing and deep learning, the approach provides a holistic solution to the complex task of plant disease detectionin agriculture. © 2023 IEEE.

关键词： Artificial Intelligence (AI) Convolutional Neural Networks (CNN) Deep Learning Images Machine Learning (ML)

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Intense Triad Defender for End-User Security in Cyber Physical System 2

Intense Triad Defender for End-User Security in Cyber Physic...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Sheryl Sharon, G. Maranco, M. Nidhya, R. Sivakumar, M. Coimbatore Institute of Technology Department of Computer Science and Engineering Coimbatore641014 India SRM Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Networking and Communications Kattankulathur603203 India Madanapalle Institute of Technology and Science Department of Computer Science and Engineering Andhra Pradesh Madanapalle India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365269

The rapid advancement of technology has undoubtedly brought comfort to humanity, but it also necessitates robust authentication measures to ensure security in the ever-expanding e-world. This research aims to address the critical aspects of security, focusing on end-user authentication. The proposed solution incorporates multiple factors, including location-based authentication, text-based authentication with enhanced encryption standards, and facial recognition authentication. Location-based authentication serves to restrict access geographically, mitigating the risk of impersonation by enforcing strict geographic limits on facility access. Strong text passwords, utilizing a unique algorithm with a classic salt method, add an extra layer of complexity for potential attackers, making it challenging to compromise user credentials. The incorporation of facial recognition technology acts as a deterrent against non-human entities attempting unauthorized access. Leveraging deep learning and API servers ensures precision and scalability in the model, enhancing its effectiveness in real-world scenarios. These multi-factor authentication techniques collectively contribute to a secure, reliable, and forward-looking solution to address the evolving landscape of digital security threats. In adhering to the age-old adage "prevention is better than cure,"the research emphasizes the importance of proactive measures to safeguard against potential security breaches. © 2024 IEEE.

关键词： Network security

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