Network and cybersecurity applications of defense in adversarial attacks: A state-of-the-art using machine learning and deep learning methods

Journal of Intelligent Systems, Journal Year: 2024, Volume and Issue: 33(1)

Published: Jan. 1, 2024

Abstract This study aims to perform a thorough systematic review investigating and synthesizing existing research on defense strategies methodologies in adversarial attacks using machine learning (ML) deep methods. A methodology was conducted guarantee literature analysis of the studies sources such as ScienceDirect, Scopus, IEEE Xplore, Web Science. question shaped retrieve articles published from 2019 April 2024, which ultimately produced total 704 papers. rigorous screening, deduplication, matching inclusion exclusion criteria were followed, hence 42 included quantitative synthesis. The considered papers categorized into coherent classification including three categories: security enhancement techniques, attack mechanisms, innovative mechanisms solutions. In this article, we have presented comprehensive earlier opened door potential future by discussing depth four challenges motivations attacks, while recommendations been discussed. science mapping also performed reorganize summarize results address issues trustworthiness. Moreover, covers large variety network cybersecurity applications subjects, intrusion detection systems, anomaly detection, ML-based defenses, cryptographic techniques. relevant conclusions well demonstrate what achieved against attacks. addition, revealed few emerging tendencies deficiencies area be remedied through better more dependable mitigation methods advanced persistent threats. findings crucial implications for community researchers, practitioners, policy makers artificial intelligence applications.

Language: Английский

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Fuzzy Decision‐Making Framework for Evaluating Hybrid Detection Models of Trauma Patients

Expert Systems, Journal Year: 2025, Volume and Issue: 42(3)

Published: Feb. 13, 2025

ABSTRACT This study introduces a new multi‐criteria decision‐making (MCDM) framework to evaluate trauma injury detection models in intensive care units (ICUs). research addresses the challenges associated with diverse machine learning (ML) models, inconsistencies, conflicting priorities, and importance of metrics. The developed methodology consists three phases: dataset identification pre‐processing, hybrid model development, an evaluation/benchmarking framework. Through meticulous is tailored focus on adult patients. Forty were by combining eight ML algorithms four filter‐based feature‐selection methods principal component analysis (PCA) as dimensionality reduction method, these evaluated using seven weight coefficients for metrics are determined 2‐tuple Linguistic Fermatean Fuzzy‐Weighted Zero‐Inconsistency (2TLF‐FWZIC) method. Vlsekriterijumska Optimizcija I Kompromisno Resenje (VIKOR) approach applied rank models. According 2TLF‐FWZIC, classification accuracy (CA) precision obtained highest weights 0.2439 0.1805, respectively, while F1, training time, test time lowest 0.1055, 0.0886, 0.1111, respectively. benchmarking results revealed following top‐performing models: Gini index logistic regression (GI‐LR), decision tree (GI_DT), information gain (IG_DT), VIKOR Q score values 0.016435, 0.023804, 0.042077, proposed MCDM assessed examined systematic ranking, sensitivity analysis, validation best‐selected two unseen datasets, mode explainability SHapley Additive exPlanations (SHAP) We benchmarked against other benchmark studies achieved 100% across six key areas. provides several insights into empirical synthesis this study. It contributes advancing medical informatics enhancing understanding selection ICUs.

Language: Английский

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Prioritizing complex health levels beyond autism triage using fuzzy multi-criteria decision-making

Complex & Intelligent Systems, Journal Year: 2024, Volume and Issue: 10(5), P. 6159 - 6188

Published: June 4, 2024

Abstract This study delves into the complex prioritization process for Autism Spectrum Disorder (ASD), focusing on triaged patients at three urgency levels. Establishing a dynamic solution is challenging resolving conflicts or trade-offs among ASD criteria. research employs fuzzy multi-criteria decision making (MCDM) theory across four methodological phases. In first phase, identifies dataset, considering 19 critical medical and sociodemographic criteria The second phase introduces new Decision Matrix (DM) designed to manage effectively. third focuses extension of Fuzzy-Weighted Zero-Inconsistency (FWZIC) construct weights using Single-Valued Neutrosophic 2-tuple Linguistic (SVN2TL). fourth formulates Multi-Attributive Border Approximation Area Comparison (MABAC) method rank within each level. Results from SVN2TL-FWZIC offer significant insights, including higher values "C12 = Laughing no reason" "C16 Notice sound bell" with 0.097358 0.083832, indicating their significance in identifying potential symptoms. base prioritizing triage levels MABAC, encompassing behavioral dimensions. methodology undergoes rigorous evaluation through sensitivity analysis scenarios, confirming consistency results points. compares benchmark studies, distinct points, achieves remarkable 100% congruence these prior investigations. implications this are far-reaching, offering valuable guide clinical psychologists cases patients.

Language: Английский

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Fuzzy Evaluation and Benchmarking Framework for Robust Machine Learning Model in Real-Time Autism Triage Applications

International Journal of Computational Intelligence Systems, Journal Year: 2024, Volume and Issue: 17(1)

Published: June 17, 2024

Abstract In the context of autism spectrum disorder (ASD) triage, robustness machine learning (ML) models is a paramount concern. Ensuring ML faces issues such as model selection, criterion importance, trade-offs, and conflicts in evaluation benchmarking models. Furthermore, development must contend with two real-time scenarios: normal tests adversarial attack cases. This study addresses this challenge by integrating three key phases that bridge domains fuzzy multicriteria decision-making (MCDM). First, utilized dataset comprises authentic information, encompassing 19 medical sociodemographic features from 1296 autistic patients who received diagnoses via intelligent triage method. These were categorized into one labels: urgent, moderate, or minor. We employ principal component analysis (PCA) algorithms to fuse large number features. Second, fused forms basis for rigorously testing eight models, considering scenarios, evaluating classifier performance using nine metrics. The third phase developed robust framework encompasses creation decision matrix (DM) 2-tuple linguistic Fermatean opinion score method (2TLFFDOSM) multiple-ML perspectives, accomplished through individual external group aggregation ranks. Our findings highlight effectiveness PCA algorithms, yielding 12 components acceptable variance. ranking, logistic regression (LR) emerged top-performing terms 2TLFFDOSM (1.3370). A comparative five benchmark studies demonstrated superior our across all six checklist comparison points.

Language: Английский

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A novel dual-level multi-source information fusion approach for multicriteria decision making applications

Applied Intelligence, Journal Year: 2024, Volume and Issue: 54(22), P. 11577 - 11602

Published: Aug. 29, 2024

Language: Английский

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Enhancing Adult Autism Diagnostic Pathways: The Role of Clinical Triage in Efficient Service Provision

Journal of Clinical Medicine, Journal Year: 2025, Volume and Issue: 14(9), P. 2933 - 2933

Published: April 24, 2025

Background: Autism spectrum disorder (ASD) is a lifelong neurodevelopmental condition affecting 1.1% of adults. The increasing incidence ASD has led to pressurised diagnostic services. Objective: We aimed determine the number needed harm (NNH) criteria-informed triage assessment in an adult autism service UK. Methods: study was conducted at specialist Service West Yorkshire, UK, from November 2021 August 2022. All eligible referrals were accepted, with criteria requiring users be over 18 years old and without intellectual disability. evaluation consisted 60 cases. Results: None cases resulted clinical diagnosis ASD, yielding infinite (NNH), demonstrating that every case benefited process significant risk harm. Conclusions: Triage enables services gather comprehensive information about individual presentations needs, facilitating informed decision-making better utilisation. demonstrates safety effectiveness process, directions for further research discussed.

Language: Английский

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Application of Explainable Artificial Intelligence in Autism Spectrum Disorder Detection

Cognitive Computation, Journal Year: 2025, Volume and Issue: 17(3)

Published: May 20, 2025

Language: Английский

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EAMAPG: Explainable Adversarial Model Analysis via Projected Gradient Descent

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 188, P. 109788 - 109788

Published: Feb. 12, 2025

Language: Английский

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AI-assisted technology optimization in disability support systems using fuzzy rough MABAC decision-making

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 26, 2025

The selection of AI-assistive technologies for disability support systems involves a complex decision-making problem due to the presence uncertain evaluation criteria. traditional methods often do not succeed in addressing challenges leading potential inefficiencies resource allocation. Aggregation operators are fundamental tool manage overall information into single value. This characteristic aggregation helps ranking processes and scenarios. To overcome issues uncertainty keep mind advantages AOs, this article, we have proposed notion fuzzy rough Maclaurin symmetric mean (FRMSM) theory. MSM AOs reduce sensitivity huge amounts data formulation. As result, more accurate authentic results can be obtained. MABAC approach uses border approximation area, so reduces bias improves accuracy. Therefore, based on FRMSMS AOs. For application work, delivered an algorithm initiated illustrative example. We utilized work optimization AI-assisted systems. Thus, it shows its dealing with problems arising from process inherent will offer reliable stakeholders health assistive technology design sectors. Additionally, comparative analysis discussed that introduced is trustable as compared existing notions.

Language: Английский

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Enhancing XML-based Compiler Construction with Large Language Models: A Novel Approach

Mesopotamian Journal of Big Data, Journal Year: 2024, Volume and Issue: 2024, P. 23 - 39

Published: March 20, 2024

Considering the prevailing rule of Large Language Models (LLMs) applications and benefits XML in a compiler context. This manuscript explores synergistic integration with XML-based tools advanced computing technologies. Marking significant stride toward redefining construction data representation paradigms. As power internet proliferation advance, emerges as pivotal technology for representing, exchanging, transforming documents data. study builds on foundational work Chomsky's Context-Free Grammar (CFG). Recognized their critical role construction, to address mitigate speed penalties associated traditional systems parser generators through development an efficient generator employing techniques. Our research employs methodical approach harness sophisticated capabilities LLMs, alongside The key is automate grammar optimization, facilitate natural language processing capabilities, pioneer parsing algorithms. To demonstrate effectiveness, we thoroughly run experiments compare them other way, call attention efficiency, adaptability, user-friendliness help these integrations. And target will be elimination left-recursive grammars global schema LL(1) grammars, latter taking advantage technology, support construction. findings this not only underscore signification innovations field compilation but also indicate paradigm move towards use AI technologies context resolution programming issues. outlined methodology serves roadmap future which paves way open-source software sweep across all fields. Gradually ushering new era featuring better CFGs processed existing utilities basis.

Language: Английский

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