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A Neutrosophic Approach to Robust Web Security: Mitigating XSS Attacks

Neutrosophic Sets and systems 2025年 79卷 1-22页

作者： Salama, A.A. Aboelfotoh, El-Said F. El-Bakry, Hazem M. Khalid, Huda E. Essa, Ahmed K. Sabbagh, Ramiz El-Morshedy, Doaa S. Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt Department of Information Systems Faculty of Computers and Information Mansoura University Mansoura Egypt Telafer University Telafer Iraq Department of the Scientific Affairs Telafer University Mosul Iraq

Cross-Site Scripting (XSS) is one of the most grievous vulnerabilities-a pitfall through which web applications are affected. These types of attacks are complex, and the available threat landscape is always changing, making it hard for conventional security tools to effectively detect or prevent these types of attacks. We present here an approach that detects and prevents XSS attacks on Web pages. Since Neutrosophism works with different kinds of data, it aims at the validity degree of the attack. This way the system understands the different types of attacks and allows the system to act more effectively. Our system combines (1) static analysis-to look into the code behind the website-with (2) dynamic analysis-to watch the website in action. Moreover, a proactive defense watches your user behavior and scrubs every input/output. © 2025, University of New Mexico. All rights reserved.

关键词： Websites

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Securing Cloud-Encrypted Data:Detecting Ransomware-as-a-Service(RaaS)Attacks through Deep Learning Ensemble

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computers, Materials & Continua 2024年第4期79卷 857-873页

作者： Amardeep Singh Hamad Ali Abosaq Saad Arif Zohaib Mushtaq Muhammad Irfan Ghulam Abbas Arshad Ali Alanoud Al Mazroa School of Computer Information Sciences University of CumberlandsWilliamsburgKentucky40769USA Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Electrical Engineering The University of LahoreLahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware *** study provides a new approach for RaaS attack detection which uses an ensemble of deep learning *** this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is *** the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are ***,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested *** proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%*** empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual *** expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.

关键词： Cloud encryption RaaS ensemble threat detection deep learning cybersecurity

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Representing Nonmonotonic Inference Based on c-Representations as an SMT Problem 17th

Representing Nonmonotonic Inference Based on c-Representati...

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17th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2023

作者： von Berg, Martin Sanin, Arthur Beierle, Christoph Knowledge-Based Systems Faculty of Mathematics and Computer Science FernUniversität in Hagen Hagen58084 Germany

ISBN: (纸本)9783031456077

As a semantics for conditional knowledge bases, ranking functions order possible worlds by mapping them to a degree of plausibility. c-Representations are special ranking functions that are obtained by assigning individual integer impacts to the conditionals in a knowledge base R and by defining the rank of each possible world as the sum of these impacts of falsified conditionals. c-Inference is the nonmonotonic inference relation taking all c-representations of a given knowledge base R into account. In this paper, we show how c-inference can be realized as a satisfiability modulo theories problem (SMT), which allows an implementation by an appropriate SMT solver. We develop a transformation of the constraint satisfaction problem characterizing c-inference into a solvable-equivalent SMT problem, prove its correctness, and illustrate it by a running example. Furthermore, we provide a corresponding implementation using the SMT solver Z3, demonstrating the feasibility of the approach as well as the superiority in comparison to former implementations. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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Approximations of System W Between c-Inference, System Z, and Lexicographic Inference 17th

Approximations of System W Between c-Inference, System Z, a...

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17th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2023

作者： Haldimann, Jonas Beierle, Christoph Knowledge-Based Systems Faculty of Mathematics and Computer Science FernUniversität in Hagen Hagen58084 Germany

ISBN: (纸本)9783031456077

Inductive inference operators have been introduced to formalize the process of completing a conditional belief base to a full inference relation. In this paper, we investigate the approximation of inductive inference operator system W with combinations of system Z (or equivalently rational closure) and c-inference, both of which are known to be extended by system W. We introduce general functions for generating inductive inference operators, the combination of two inductive inference operators by union, and the completion of an inductive inference operator by an arbitrary set of axioms. We construct the least inductive inference operator extending system Z and c-inference which, however, does not satisfy system P. We also construct the least inductive inference operator extending system Z and c-inference that also satisfies system P and show that it is strictly extended by system W. Furthermore, we develop approximations that extend system W and introduce an inductive inference operator that strictly extends system W and that is strictly extended by lexicographic inference. This leads to a map of inference relations between rational closure and c-inference on the one side and lexicographic inference on the other side. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Rational functions

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Novel Framework of Segmentation 3D MRI of Brain Tumors

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computers, Materials & Continua 2023年第2期74卷 3489-3502页

作者： Ibrahim Mahmoud El-Henawy Mostafa Elbaz Zainab H.Ali Noha Sakr Department of Computer Science Faculty of Computers and InformaticsZagazig UniversityZagazigEgypt Department of Computer Science Faculty of Computers and InformaticsKafrelsheikh UniversityKafrelsheikhEgypt Department of Embedded Network Sys.and Tec. Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikhEgypt Computers Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansouraEgypt

Medical image segmentation is a crucial process for computer-aided diagnosis and *** image segmentation refers to portioning the images into small,disjointed parts for simplifying the processes of analysis and *** and speckle noise are different types of noise in magnetic resonance imaging(MRI)that affect the accuracy of the segmentation process ***,image enhancement has a significant role in MRI *** paper proposes a novel framework that uses 3D MRI images from Kaggle and applies different diverse models to remove Rician and speckle noise using the best possible noise-free *** proposed techniques consider the values of Peak Signal to Noise Ratio(PSNR)and the level of noise as inputs to the attention-U-Net model for segmentation of the *** framework has been divided into three stages:removing speckle and Rician noise,the segmentation stage,and the feature extraction *** framework presents solutions for each problem at a different stage of the *** the first stage,the framework uses Vibrational Mode Decomposition(VMD)along with Block-matching and 3D filtering(Bm3D)algorithms to remove the ***,the most significant Rician noise-free images are passed to the three different methods:Deep Residual Network(DeRNet),Dilated Convolution Auto-encoder Denoising Network(Di-Conv-AE-Net),andDenoising Generative Adversarial Network(DGAN-Net)for removing the speckle *** Bm3D have achieved PSNR values for levels of noise(0,0.25,0.5,0.75)for reducing the Rician noise by(35.243,32.135,28.214,24.124)and(36.11,31.212,26.215,24.123)*** framework also achieved PSNR values for removing the speckle noise process for each level as follows:(34.146,30.313,28.125,24.001),(33.112,29.103,27.110,24.194),and(32.113,28.017,26.193,23.121)forDeRNet,Di-Conv-AE-Net,and DGAN-Net,*** experiments that have been conducted have proved the efficiency of the proposed framework a

关键词： MRI Rician noise speckle noise segmentation deep learning

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Novel Optimized Feature Selection Using Metaheuristics Applied to Physical Benchmark Datasets

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computers, Materials & Continua 2023年第2期74卷 4027-4041页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Fadwa Alrowais Sunil Kumar Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt School of Computer Science University of Petroleum and Energy StudiesDehradun248001India Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

In data mining and machine learning,feature selection is a critical part of the process of selecting the optimal subset of features based on the target *** are 2n potential feature subsets for every n features in a dataset,making it difficult to pick the best set of features using standard ***,in this research,a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm(ASSOA)has been *** using metaheuristics to pick features,it is common for the selection of features to vary across runs,which can lead to *** of this,we used the adaptive squirrel search to balance exploration and exploitation duties more evenly in the optimization *** the selection of the best subset of features,we recommend using the binary ASSOA search strategy we developed *** to the suggested approach,the number of features picked is reduced while maximizing classification accuracy.A ten-feature dataset from the University of California,Irvine(UCI)repository was used to test the proposed method’s performance *** other state-of-the-art approaches,including binary grey wolf optimization(bGWO),binary hybrid grey wolf and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hybrid GWO and genetic algorithm 4028 CMC,2023,vol.74,no.2(bGWO-GA),binary firefly algorithm(bFA),and *** results confirm the superiority and effectiveness of the proposed algorithm for solving the problem of feature selection.

关键词： Metaheuristics adaptive squirrel search algorithm optimization methods binary optimizer

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Towards a User-Friendly Web Application Firewall 11

Towards a User-Friendly Web Application Firewall

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11th IEEE International Conference on Intelligent Computing and Information systems, ICICIS 2023

作者： Omar, Fatima Ahmed, Dalia Elnakib, Omar Ahmed, Mohamed Farhan, Nada Hindy, Hanan Abdel-Hamid, Mahmoud Badawi, Yehia Ain Shams University Faculty of Computer and Information Science Cyber Security Department Cairo Egypt Ain Shams University Faculty of Computer and Information Science Computer Systems Department Cairo Egypt Ain Shams University Faculty of Computer and Information Science Cyber Security Department Giza Egypt Ain Shams University Faculty of Computer and Information Science Computer Science Department Cairo Egypt

ISBN: (纸本)9798350322101

Web Application Firewall (WAF) systems play a vital role in protecting web applications from various security vulnerabilities. However, existing WAF solutions are often costly and complex to configure, limiting their accessibility to smaller organizations. This paper aims to develop an affordable and user-friendly WAF framework that mitigates the most common security vulnerabilities identified in the Open Web Application Security Project (OWASP) Top 10. The proposed WAF framework provides protection against attacks such as Cross-Site Scripting (XSS), Structured Query Language Injection (SQLi), Operating System (OS) Command Injection, File Upload, Denial of Service (DoS), Cross Site Request Forgery (CSRF), Path Traversal, and XML External Entity (XXE) attacks. The framework offers an easy-to-use interface with features for easy configuration of mitigation settings. Users can monitor web traffic and receive real-time alerts to quickly identify and respond to potential security threats. Proxy techniques are used to intercept and handle user requests, sanitizing them using the mitigation techniques to prevent the inclusion of malicious code or unauthorized data. Additionally, two machine learning models were implemented. The first model detects malicious requests, and the second model classifies attacks based on their type. Mitigation techniques are employed to filter requests and prevent specific attack types. Regular expressions (Regex) are used to remove malicious code related to XSS, SQL injection, OS Command Injection, File Upload, CSRF, XXE, and Path Traversal attacks. Rate limiting is implemented to mitigate DoS attacks. © 2023 IEEE.

关键词： Denial-of-service attack

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A Partial MaxSAT Approach to Nonmonotonic Reasoning with System W 37

A Partial MaxSAT Approach to Nonmonotonic Reasoning with Sys...

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37th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2024

作者： Beierle, Christoph Spang, Aron Haldimann, Jonas FernUniversität in Hagen Knowledge Based Systems Faculty of Mathematics and Computer Science Hagen58084 Germany

The only recently introduced System W is a nonmonotonic inductive inference operator exhibiting some notable properties like extending rational closure and satisfying syntax splitting postulates for inference from conditional belief bases. A semantic model of system W is given by its underlying preferred structure of worlds, a strict partial order on the set of propositional interpretations, also called possible worlds, over the signature of the belief base. Existing implementations of system W are severely limited by the number of propositional variables that occur in a belief base because of the exponentially growing number of possible worlds. In this paper, we present an approach to realizing nonmonotonic reasoning with system W by using partial maximum satisfiability (PMaxSAT) problems and exploiting the power of current PMaxSAT solvers. An evaluation of our approach demonstrates that it outperforms previous implementations of system W and scales reasoning with system W up to a new dimension. Copyright © 2024 by the authors.

关键词： Semantics

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Pose-Critical Keypoint Attention model for Dynamic Yoga Pose Classification 2

Pose-Critical Keypoint Attention model for Dynamic Yoga Pose...

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2nd International Conference on computer Vision and Internet of Things, ICCVIoT 2024

作者： Meghana, J.H. Chethan, H.K. Prakash, S.P. Shiva Department of Studies in Computer Science University of Mysore Karnataka Mysore India Department of Computer Science and Engineering Maharaja Institute of Technology Karnataka Mysore India Department of Information Science and Engineering & Computer Science and Business Systems JSS Science and Technology University Karnataka Mysuru India

ISBN: (纸本)9781837242672

Yoga pose detection and classification have garnered considerable attention in recent years due to their significant applications in various domains, including fitness, health monitoring, and rehabilitation programs. Accurate detection and classification of yoga poses not only enhance the effectiveness of fitness regimes but also facilitate the assessment of practitioners' health and recovery processes. However, existing methods often struggle with the challenges posed by high-dimensional data processing, particularly in real-time applications. This paper introduces the Pose-Critical Keypoint Attention (PCKA) model, a novel approach designed to overcome the limitations of traditional methods in yoga pose analysis. The PCKA model employs an advanced attention mechanism to dynamically prioritize the most relevant keypoints from a comprehensive set of 33 extracted keypoints. This dynamic focus allows for efficient classification without the drawbacks associated with static keypoint reduction, ensuring that the model remains adaptable and robust across various poses. Extensive experiments evaluate the performance of the PCKA model against conventional classification frameworks. The results indicate that this model significantly outperforms traditional approaches, achieving an impressive accuracy of 91.08%, precision of 89.70%, recall of 91.93%, and an F1-score of 90.78%. These metrics demonstrate the model's capability to deliver accurate pose classification while providing realtime feedback for corrective measures, which is crucial for users striving to improve their yoga practices. © The Institution of Engineering & Technology 2024.

关键词： Data handling

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Stepwise AI Interpretive Approach for Multimodal Data Fusion 6

Stepwise AI Interpretive Approach for Multimodal Data Fusion

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6th International Conference on Intelligent Computing in Data sciences, ICDS 2024

作者： Long, Bowen Liu, Enjie Qiu, Renxi Duan, Yanqing University of Bedfordshire Faculty of Computer Science and Technology Luton United Kingdom University of Bedfordshire Faculty of Business Luton United Kingdom

ISBN: (纸本)9798350351200

In recent years, Artificial Intelligence technology has excelled in various tasks and is taking the world by storm. However, the various transformations in neural networks make it difficult to make sense of the reasons why decisions are made. For this reason, trustworthy AI techniques have started gaining popularity. AI interpretability serves as an anchor point in the field of data fusion for multimodal AI, providing in-depth insights. The paper proposed a Stepwise AI Interpretative (SAII) approach using different pairing methods of 'one-To-one' and 'many-To-many' in an attempt to illustrate/demonstrate the interpretability of the process of pairing images and text. A counterfactual instantiation method was used to compare the whole-local relationship between a set of images and their associated descriptive text. The approach was evaluated via 'task performance'. © 2024 IEEE.

关键词： Data fusion

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