Expert Systems最新文献

英文中文

Advancing Disability Healthcare Solutions Through Privacy-Preserving Federated Learning With Theme Framework 通过主题框架的隐私保护联合学习推进残疾人医疗保健解决方案

IF 3 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems

Pub Date : 2024-12-12 DOI: 10.1111/exsy.13807

Madallah Alruwaili, Muhammad Hameed Siddiqi, Muhammad Idris, Salman Alruwaili, Abdullah Saleh Alanazi, Faheem Khan

The application of machine learning, particularly federated learning, in collaborative model training, has demonstrated significant potential for enhancing diversity and efficiency in outcomes. In the healthcare domain, particularly healthcare with disabilities, the sensitive nature of data presents a significant challenge as sharing even the computation on these data can risk exposing personal health information. This research addresses the problem of enabling shared model training for healthcare data—particularly with disabilities decreasing the risk of leaking or compromising sensitive information. Technologies such as federated learning provide solution for decentralised model training but fall short in addressing concerns related to trust building, accountability and control over participation and data. We propose a framework that integrates federated learning with advanced identity management as well as privacy and trust management technologies. Our framework called Theme (Trusted Healthcare Machine Learning Environment) leverages digital identities (e.g., W3C decentralised identifiers and verified credentials) and policy enforcements to regulate participation. This is to ensure that only authorised and trusted entities can contribute to the model training. Additionally, we introduce the mechanisms to track contributions per participant and offer the flexibility for participants to opt out of model training at any point. Participants can choose to be either contributors (providers) or consumers (model users) or both, and they can also choose to participate in subset of activities. This is particularly important in healthcare settings, where individuals and healthcare institutions have the flexibility to control how their data are used without compromising the benefits. In summary, this research work contributes to privacy preserving shared model training leveraging federated learning without exposing sensitive data; trust and accountability mechanisms; contribution tracking per participant for accountability and back-tracking; and fine-grained control and autonomy per participant. By addressing the specific needs of healthcare data for people with disabilities or such institutions, the Theme framework offers a robust solution to balance the benefits of shared machine learning with critical need to protecting sensitive data.

机器学习，特别是联邦学习，在协作模型训练中的应用，已经显示出增强结果多样性和效率的巨大潜力。在医疗保健领域，特别是残疾人医疗保健领域，数据的敏感性带来了重大挑战，因为即使共享这些数据的计算也可能有暴露个人健康信息的风险。本研究解决了为医疗保健数据（特别是残疾人）启用共享模型训练的问题，从而降低了泄露或泄露敏感信息的风险。联邦学习等技术为分散的模型训练提供了解决方案，但在解决与信任建立、问责制以及对参与和数据的控制相关的问题方面存在不足。我们提出了一个将联邦学习与高级身份管理以及隐私和信任管理技术相结合的框架。我们的框架名为Theme（可信医疗机器学习环境），利用数字身份（例如，W3C分散的标识符和经过验证的凭据）和政策实施来规范参与。这是为了确保只有授权和可信的实体才能为模型培训做出贡献。此外，我们引入了跟踪每个参与者贡献的机制，并为参与者提供了在任何时候选择退出模型培训的灵活性。参与者可以选择成为参与者（提供者）或消费者（模型用户），或者两者兼而有之，并且他们还可以选择参与活动的子集。这在医疗保健环境中尤其重要，因为个人和医疗保健机构可以灵活地控制如何使用其数据，而不会损害其利益。总之，这项研究工作有助于在不暴露敏感数据的情况下利用联邦学习保护共享模型训练的隐私；信任和问责机制；跟踪每个参与者的贡献，以便问责和回溯；以及每个参与者的细粒度控制和自主权。通过满足残疾人或此类机构对医疗保健数据的特定需求，Theme框架提供了一个强大的解决方案，可以在共享机器学习的好处与保护敏感数据的关键需求之间取得平衡。

{"title":"Advancing Disability Healthcare Solutions Through Privacy-Preserving Federated Learning With Theme Framework","authors":"Madallah Alruwaili, Muhammad Hameed Siddiqi, Muhammad Idris, Salman Alruwaili, Abdullah Saleh Alanazi, Faheem Khan","doi":"10.1111/exsy.13807","DOIUrl":"https://doi.org/10.1111/exsy.13807","url":null,"abstract":"<div>\u0000 \u0000 The application of machine learning, particularly federated learning, in collaborative model training, has demonstrated significant potential for enhancing diversity and efficiency in outcomes. In the healthcare domain, particularly healthcare with disabilities, the sensitive nature of data presents a significant challenge as sharing even the computation on these data can risk exposing personal health information. This research addresses the problem of enabling shared model training for healthcare data—particularly with disabilities decreasing the risk of leaking or compromising sensitive information. Technologies such as federated learning provide solution for decentralised model training but fall short in addressing concerns related to trust building, accountability and control over participation and data. We propose a framework that integrates federated learning with advanced identity management as well as privacy and trust management technologies. Our framework called Theme (Trusted Healthcare Machine Learning Environment) leverages digital identities (e.g., W3C decentralised identifiers and verified credentials) and policy enforcements to regulate participation. This is to ensure that only authorised and trusted entities can contribute to the model training. Additionally, we introduce the mechanisms to track contributions per participant and offer the flexibility for participants to opt out of model training at any point. Participants can choose to be either contributors (providers) or consumers (model users) or both, and they can also choose to participate in subset of activities. This is particularly important in healthcare settings, where individuals and healthcare institutions have the flexibility to control how their data are used without compromising the benefits. In summary, this research work contributes to privacy preserving shared model training leveraging federated learning without exposing sensitive data; trust and accountability mechanisms; contribution tracking per participant for accountability and back-tracking; and fine-grained control and autonomy per participant. By addressing the specific needs of healthcare data for people with disabilities or such institutions, the Theme framework offers a robust solution to balance the benefits of shared machine learning with critical need to protecting sensitive data.\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142851281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predictive Analysis of Global Terrorist Attacks Using Lexical Patterns Across Multiple Datasets 利用跨多个数据集的词汇模式对全球恐怖袭击进行预测分析

IF 3 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems

Pub Date : 2024-12-12 DOI: 10.1111/exsy.13808

Mohammed Salem Atoum, Ala Abdulsalam Alarood, Eesa Abdullah Alsolmi, Areej Obeidat, Moutaz Alazab

Worldwide terrorist activities continue to pose a significant threat to global security and stability. The unpredictable nature of these acts necessitates advanced analytical approaches to enhance prevention and response strategies. This study examines undetectable word extensions across multiple datasets, using terrorism-related datasets as a case study. This research aims to overcome constraints in current predictive models associated with terrorist attack prediction. While many studies have used the GTD for predicting global terrorist attacks, this study expands beyond GTD by evaluating a corpus of terrorism incidents to enhance predictive analysis through lexical usage. The study employs several machine learning algorithms including Decision Tree (DT), Bootstrap Aggregating (BA), Random Forest (RF), Extra Trees (ET) and XGBoost (XG) algorithms for evaluation. Our approach integrates multiple datasets to reduce dependence on GTD alone. Findings indicate that RF performs best on the GTD database, with 90.20% accuracy in predicting worldwide terrorist attacks. DT achieves 90.40% accuracy when applied to the TF–IDF dataset. XG demonstrates superior performance across various aggregation settings and feature sets, achieving 95.77% accuracy in forecasting worldwide terrorist acts. XG's consistent and effective performance across various contexts highlights its versatility. Its high adaptability and robust performance position it as the preferred algorithm for conducting predictive research on global terrorist acts using the available datasets. Our research findings underscore the importance of incorporating diverse datasets to enhance understanding of terrorist activities and improve predictive capabilities.

全球恐怖活动继续对全球安全与稳定构成重大威胁。这些行为的不可预测性要求采用先进的分析方法来加强预防和应对策略。本研究以恐怖主义相关数据集为案例，研究了多个数据集中无法检测到的单词扩展。这项研究旨在克服当前与恐怖袭击预测相关的预测模型中存在的制约因素。虽然许多研究已将 GTD 用于预测全球恐怖袭击，但本研究通过评估恐怖主义事件语料库，超越了 GTD 的范围，通过词汇用法加强了预测分析。本研究采用了多种机器学习算法，包括决策树 (DT)、自举法聚合 (BA)、随机森林 (RF)、额外树 (ET) 和 XGBoost (XG) 算法进行评估。我们的方法整合了多个数据集，以减少对 GTD 本身的依赖。结果表明，RF 在 GTD 数据库中表现最佳，预测全球恐怖袭击的准确率为 90.20%。DT 应用于 TF-IDF 数据集时，准确率达到 90.40%。XG 在各种聚合设置和特征集上都表现出了卓越的性能，在预测全球恐怖行动方面达到了 95.77% 的准确率。XG 在各种情况下均表现出一致而有效的性能，这凸显了它的多功能性。其高度的适应性和稳健的性能使其成为利用现有数据集开展全球恐怖行为预测研究的首选算法。我们的研究成果强调了结合各种数据集以加强对恐怖活动的了解和提高预测能力的重要性。

{"title":"Predictive Analysis of Global Terrorist Attacks Using Lexical Patterns Across Multiple Datasets","authors":"Mohammed Salem Atoum, Ala Abdulsalam Alarood, Eesa Abdullah Alsolmi, Areej Obeidat, Moutaz Alazab","doi":"10.1111/exsy.13808","DOIUrl":"https://doi.org/10.1111/exsy.13808","url":null,"abstract":"<div>\u0000 \u0000 Worldwide terrorist activities continue to pose a significant threat to global security and stability. The unpredictable nature of these acts necessitates advanced analytical approaches to enhance prevention and response strategies. This study examines undetectable word extensions across multiple datasets, using terrorism-related datasets as a case study. This research aims to overcome constraints in current predictive models associated with terrorist attack prediction. While many studies have used the GTD for predicting global terrorist attacks, this study expands beyond GTD by evaluating a corpus of terrorism incidents to enhance predictive analysis through lexical usage. The study employs several machine learning algorithms including Decision Tree (DT), Bootstrap Aggregating (BA), Random Forest (RF), Extra Trees (ET) and XGBoost (XG) algorithms for evaluation. Our approach integrates multiple datasets to reduce dependence on GTD alone. Findings indicate that RF performs best on the GTD database, with 90.20% accuracy in predicting worldwide terrorist attacks. DT achieves 90.40% accuracy when applied to the TF–IDF dataset. XG demonstrates superior performance across various aggregation settings and feature sets, achieving 95.77% accuracy in forecasting worldwide terrorist acts. XG's consistent and effective performance across various contexts highlights its versatility. Its high adaptability and robust performance position it as the preferred algorithm for conducting predictive research on global terrorist acts using the available datasets. Our research findings underscore the importance of incorporating diverse datasets to enhance understanding of terrorist activities and improve predictive capabilities.\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142860900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RETRACTION: Optimization Using Internet of Agent Based Stacked Sparse Autoencoder Model for Heart Disease Prediction RETRACTION：利用基于互联网的代理堆叠稀疏自动编码器模型进行心脏病预测优化

IF 3 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems

Pub Date : 2024-10-16 DOI: 10.1111/exsy.13751

RETRACTION: V. Baviskar, M. Verma, P. Chatterjee, G. Singal and T. R. Gadekallu, “ Optimization Using Internet of Agent Based Stacked Sparse Autoencoder Model for Heart Disease Prediction,” Expert Systems (Early View): e13359, https://doi.org/10.1111/exsy.13359.

The above article, published online on 10 June 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editor-in-Chief, David Camacho; and John Wiley & Sons Ltd. The article was submitted as part of a guest-edited special issue. Following publication, it has come to the attention of the journal that parts of the methods in the article lack sufficient detail such that the research cannot be reproduced. A relevant discussion and discrimination for different cardiovascular diseases is missing. The editors have therefore decided to retract this article. The authors disagree with the retraction.

撤回：V. Baviskar、M. Verma、P. Chatterjee、G. Singal 和 T. R. Gadekallu，" Optimization Using Internet of Agent Based Stacked Sparse Autoencoder Model for Heart Disease Prediction，" Expert Systems (Early View): e13359，https://doi.org/10.1111/exsy.13359。上述文章于 2023 年 6 月 10 日在线发表于 Wiley Online Library (wileyonlinelibrary.com)，经期刊主编 David Camacho 和 John Wiley & Sons Ltd.（约翰-威利父子有限公司）同意，已被撤回。这篇文章是作为特邀编辑特刊的一部分提交的。文章发表后，本刊注意到文章中的部分方法不够详细，导致研究无法复制。文章缺少对不同心血管疾病的相关讨论和鉴别。因此，编辑决定撤回这篇文章。作者不同意撤稿。

引用次数: 0

RETRACTION: A Predictive Typological Content Retrieval Method for Real-time Applications Using Multilingual Natural Language Processing RETRACTION：使用多语种自然语言处理的实时应用预测性类型学内容检索方法

IF 3 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems

Pub Date : 2024-10-15 DOI: 10.1111/exsy.13752

RETRACTION: S. Baskar, S. Dhote, T. Dhote, G. Jayanandini, D. Akila and S. Doss, “ A Predictive Typological Content Retrieval Method for Real-time Applications Using Multilingual Natural Language Processing,” Expert Systems 41, no. 6 (2024): e13172, https://doi.org/10.1111/exsy.13172.

The above article, published online on 28 October 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editor-in-Chief, David Camacho; and John Wiley & Sons Ltd. The article was submitted as part of a guest-edited special issue. Following publication, it has come to the attention of the journal that this article was not reviewed in line with the journal's peer review standards. The editors have therefore decided to retract this article. The authors disagree with the retraction.

撤回：S. Baskar、S. Dhote、T. Dhote、G. Jayanandini、D. Akila 和 S. Doss，" A Predictive Typological Content Retrieval Method for Real-time Applications Using Multilingual Natural Language Processing，" Expert Systems 41，no. 6 (2024)：e13172，https://doi.org/10.1111/exsy.13172。上述文章于 2022 年 10 月 28 日在线发表于 Wiley Online Library (wileyonlinelibrary.com)，经期刊主编大卫-卡马乔（David Camacho）与 John Wiley & Sons Ltd.（John Wiley & Sons Ltd.）协商，已被撤回。这篇文章是作为特邀编辑特刊的一部分提交的。文章发表后，本刊注意到这篇文章没有按照本刊的同行评审标准进行评审。因此，编辑决定撤回这篇文章。作者不同意撤稿。

引用次数: 0

RETRACTION: Natural Language Processing With Deep Learning Enabled Hybrid Content Retrieval Model for Digital Library Management RETRACTION：数字图书馆管理的自然语言处理与深度学习混合内容检索模型

IF 3 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems

Pub Date : 2024-10-15 DOI: 10.1111/exsy.13753

RETRACTION: M. Ragab, A. Almuhammadi, R. F. Mansour and S. Kadry, “ Natural Language Processing With Deep Learning Enabled Hybrid Content Retrieval Model for Digital Library Management,” Expert Systems 41, no. 6 (2024): e13135, https://doi.org/10.1111/exsy.13135.

The above article, published online on 13 September 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editor-in-Chief, David Camacho; and John Wiley & Sons Ltd. The article was submitted as part of a guest-edited special issue. Following publication, it has come to the attention of the journal that this article was accepted on the basis of a compromised peer review process. The editors have therefore decided to retract this article. The authors disagree with the retraction.

撤稿：M. Ragab、A. Almuhammadi、R. F. Mansour 和 S. Kadry，" Natural Language Processing With Deep Learning Enabled Hybrid Content Retrieval Model for Digital Library Management，" Expert Systems 41，no. 6 (2024)：e13135，https://doi.org/10.1111/exsy.13135。上述文章于 2022 年 9 月 13 日在线发表于 Wiley Online Library (wileyonlinelibrary.com)，经期刊主编大卫-卡马乔（David Camacho）与 John Wiley & Sons Ltd.（John Wiley & Sons Ltd.）协商，已被撤回。这篇文章是作为特邀编辑特刊的一部分提交的。文章发表后，本刊注意到，这篇文章是在有损同行评审程序的基础上被接受的。因此，编辑决定撤回这篇文章。作者不同意撤稿。

引用次数: 0

RETRACTION: Hybrid Multi Agent Optimization for Optimal Battery Storage Using Micro Grid RETRACTION：利用微电网优化电池存储的混合多代理优化技术

IF 3 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems

Pub Date : 2024-09-26 DOI: 10.1111/exsy.13743

RETRACTION: N. Bacanin, “Hybrid Multi Agent Optimization for Optimal Battery Storage Using Micro Grid,” Expert Systems 40, no. 4 (2023): e12995. https://doi.org/10.1111/exsy.12995.

The above article, published online on 14 March 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the author, N. Bacanin; the journal Editor-in-Chief, David Camacho; and John Wiley & Sons Ltd. The article was submitted as part of a guest-edited special issue. Following publication, it has come to the attention of the journal that this article was accepted on the basis of a compromised peer review process. Furthermore, parts of the methods and figures in the article lack sufficient detail such that the research cannot be reproduced. Therefore the decision to retract this article was taken.

撤回：N. Bacanin, "Hybrid Multi Agent Optimization for Optimal Battery Storage Using Micro Grid," Expert Systems 40, no.4 (2023): e12995. https://doi.org/10.1111/exsy.12995.The 上述文章于 2022 年 3 月 14 日在线发表于 Wiley Online Library (wileyonlinelibrary.com)，经作者 N. Bacanin、期刊主编 David Camacho 和 John Wiley & Sons Ltd.（约翰-威利父子有限公司）协商，已被撤回。这篇文章是作为特邀编辑特刊的一部分提交的。文章发表后，该杂志注意到，这篇文章是在同行评审过程中受到损害的基础上被接受的。此外，文章中的部分方法和图表缺乏足够的细节，导致研究无法复制。因此，决定撤回这篇文章。

引用次数: 0

RETRACTION: Internet of Agents System for Age and Gender Classification Using Grasshopper Optimization With Deep Convolution Neural Networks RETRACTION：利用蚱蜢优化和深度卷积神经网络进行年龄和性别分类的代理互联网系统

IF 3 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems

Pub Date : 2024-09-26 DOI: 10.1111/exsy.13746

RETRACTION: A. K. Dutta, B. Qureshi, Y. Albagory, M. Alsanea, D. Gupta, and A. Khanna, “ Internet of Agents System for Age and Gender Classification Using Grasshopper Optimization With Deep Convolution Neural Networks,” Expert Systems 40, no. 4 (2023): e13115. https://doi.org/10.1111/exsy.13115.

The above article, published online on 02 August 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editor-in-Chief, David Camacho; and John Wiley & Sons Ltd. The article was submitted as part of a guest-edited special issue. Following publication, it has come to the attention of the journal that parts of the methods in the article lack sufficient detail such that the research cannot be reproduced. Furthermore, images of individuals have been used in figures 1, 3, 4 and 5 without any information provided regarding copyright and consent to use images. The editors have therefore decided to retract this article.

返回：A. K. Dutta、B. Qureshi、Y. Albagory、M. Alsanea、D. Gupta 和 A. Khanna，" 使用深度卷积神经网络的蚱蜢优化进行年龄和性别分类的互联网代理系统"，《专家系统》第 40 期，no.4 (2023): e13115. https://doi.org/10.1111/exsy.13115. 上述文章于 2022 年 8 月 2 日在线发表于 Wiley Online Library (wileyonlinelibrary.com)，经期刊主编大卫-卡马乔（David Camacho）与 John Wiley & Sons Ltd.（John Wiley & Sons Ltd.）协商，已被撤回。这篇文章是作为特邀编辑特刊的一部分提交的。文章发表后，本刊注意到文章中的部分方法不够详细，导致研究无法复制。此外，图 1、图 3、图 4 和图 5 中使用了个人图像，但未提供任何有关版权和同意使用图像的信息。因此，编辑决定撤回这篇文章。

{"title":"RETRACTION: Internet of Agents System for Age and Gender Classification Using Grasshopper Optimization With Deep Convolution Neural Networks","authors":"","doi":"10.1111/exsy.13746","DOIUrl":"https://doi.org/10.1111/exsy.13746","url":null,"abstract":"\u0000 \u0000 RETRACTION: A. K. Dutta, B. Qureshi, Y. Albagory, M. Alsanea, D. Gupta, and A. Khanna, “ Internet of Agents System for Age and Gender Classification Using Grasshopper Optimization With Deep Convolution Neural Networks,” Expert Systems 40, no. 4 (2023): e13115. https://doi.org/10.1111/exsy.13115.\u0000 The above article, published online on 02 August 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editor-in-Chief, David Camacho; and John Wiley & Sons Ltd. The article was submitted as part of a guest-edited special issue. Following publication, it has come to the attention of the journal that parts of the methods in the article lack sufficient detail such that the research cannot be reproduced. Furthermore, images of individuals have been used in figures 1, 3, 4 and 5 without any information provided regarding copyright and consent to use images. The editors have therefore decided to retract this article.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 12","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.13746","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142708401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A comprehensive survey on deep learning‐based intrusion detection systems in Internet of Things (IoT) 基于深度学习的物联网（IoT）入侵检测系统综述

IF 3.3 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems

Pub Date : 2024-09-14 DOI: 10.1111/exsy.13726

Qasem Abu Al‐Haija, Ayat Droos

The proliferating popularity of Internet of Things (IoT) devices has led to wide‐scale networked system implementations across multiple disciplines, including transportation, medicine, smart homes, and many others. This unprecedented level of interconnectivity has introduced new security vulnerabilities and threats. Ensuring security in these IoT settings is crucial for protecting against malicious activities and safeguarding data. Real‐time identification and response to potential intrusions and attacks are essential, and intrusion detection systems (IDS) are pivotal in this process. However, the dynamic and diverse nature of the IoT environment presents significant challenges to existing IDS solutions, which are often based on rule‐based or statistical approaches. Deep learning, a subset of artificial intelligence, has shown great potential to enhance IDS in IoT. Deep learning models can identify complex patterns and characteristics by utilizing artificial neural networks, automatically building hierarchical representations from data. This capability results in more precise and efficient intrusion detection in IoT‐based systems. The primary aim of this survey is to present an extensive overview of the current research on deep learning and IDS in the IoT domain. By examining existing literature, discussing mainstream datasets, and highlighting current challenges and potential prospects, this survey provides valuable insights into the prevailing scenario and future directions for using deep learning in IDS for IoT. The findings from this research aim to enhance intrusion detection techniques in IoT environments and promote the development of more effective antimalware solutions against cyber threats targeting IoT device systems.

随着物联网（IoT）设备的普及，包括交通、医疗、智能家居等多个领域都出现了大规模的联网系统实施。这种前所未有的互联水平带来了新的安全漏洞和威胁。在这些物联网环境中确保安全对于防范恶意活动和保护数据至关重要。实时识别和响应潜在的入侵和攻击至关重要，而入侵检测系统（IDS）在这一过程中举足轻重。然而，物联网环境的动态性和多样性给现有的 IDS 解决方案带来了巨大挑战，这些解决方案通常基于规则或统计方法。深度学习作为人工智能的一个子集，在增强物联网 IDS 方面显示出巨大的潜力。深度学习模型可以利用人工神经网络识别复杂的模式和特征，自动从数据中构建分层表示。这种能力可以在基于物联网的系统中实现更精确、更高效的入侵检测。本调查报告的主要目的是对当前物联网领域的深度学习和 IDS 研究进行广泛概述。通过研究现有文献、讨论主流数据集、强调当前挑战和潜在前景，本调查报告对物联网 IDS 中使用深度学习的普遍情况和未来方向提供了有价值的见解。本研究的发现旨在增强物联网环境中的入侵检测技术，并促进开发更有效的反恶意软件解决方案，以应对针对物联网设备系统的网络威胁。

{"title":"A comprehensive survey on deep learning‐based intrusion detection systems in Internet of Things (IoT)","authors":"Qasem Abu Al‐Haija, Ayat Droos","doi":"10.1111/exsy.13726","DOIUrl":"https://doi.org/10.1111/exsy.13726","url":null,"abstract":"The proliferating popularity of Internet of Things (IoT) devices has led to wide‐scale networked system implementations across multiple disciplines, including transportation, medicine, smart homes, and many others. This unprecedented level of interconnectivity has introduced new security vulnerabilities and threats. Ensuring security in these IoT settings is crucial for protecting against malicious activities and safeguarding data. Real‐time identification and response to potential intrusions and attacks are essential, and intrusion detection systems (IDS) are pivotal in this process. However, the dynamic and diverse nature of the IoT environment presents significant challenges to existing IDS solutions, which are often based on rule‐based or statistical approaches. Deep learning, a subset of artificial intelligence, has shown great potential to enhance IDS in IoT. Deep learning models can identify complex patterns and characteristics by utilizing artificial neural networks, automatically building hierarchical representations from data. This capability results in more precise and efficient intrusion detection in IoT‐based systems. The primary aim of this survey is to present an extensive overview of the current research on deep learning and IDS in the IoT domain. By examining existing literature, discussing mainstream datasets, and highlighting current challenges and potential prospects, this survey provides valuable insights into the prevailing scenario and future directions for using deep learning in IDS for IoT. The findings from this research aim to enhance intrusion detection techniques in IoT environments and promote the development of more effective antimalware solutions against cyber threats targeting IoT device systems.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"15 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142254408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MTFDN: An image copy‐move forgery detection method based on multi‐task learning MTFDN：基于多任务学习的图像复制移动伪造检测方法

IF 3.3 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems

Pub Date : 2024-09-14 DOI: 10.1111/exsy.13729

Peng Liang, Hang Tu, Amir Hussain, Ziyuan Li

Image copy‐move forgery, where an image region is copied and pasted within the same image, is a simple yet widely employed manipulation. In this paper, we rethink copy‐move forgery detection from the perspective of multi‐task learning and summarize two characteristics of this problem: (1) Homology and (2) Manipulated traces. Consequently, we propose a multi‐task forgery detection network (MTFDN) for image copy‐move forgery localization and source/target distinguishment. The network consists of a hard‐parameter sharing feature extractor, global forged homology detection (GFHD) and local manipulated trace detection (LMTD) modules. The difference of feature distribution between the GFHD module and the LMTD module is significantly reduced by sharing parameters. Experimental results on several benchmark copy‐move forgery datasets demonstrate the effectiveness of our proposed MTFDN.

图像复制移动伪造是指在同一图像中复制和粘贴一个图像区域，这是一种简单但却被广泛使用的操作。在本文中，我们从多任务学习的角度重新思考了复制移动伪造检测问题，并总结了该问题的两个特点：(1) 同源性和 (2) 被操纵的痕迹。因此，我们提出了一种用于图像复制移动伪造定位和来源/目标区分的多任务伪造检测网络（MTFDN）。该网络由硬参数共享特征提取器、全局伪造同源检测（GFHD）和局部操纵痕迹检测（LMTD）模块组成。通过共享参数，GFHD 模块和 LMTD 模块之间的特征分布差异显著缩小。在几个基准复制移动伪造数据集上的实验结果证明了我们提出的 MTFDN 的有效性。

引用次数: 0

STP‐CNN: Selection of transfer parameters in convolutional neural networks STP-CNN：选择卷积神经网络中的传递参数

IF 3.3 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems

Pub Date : 2024-09-12 DOI: 10.1111/exsy.13728

Otmane Mallouk, Nour‐Eddine Joudar, Mohamed Ettaouil

Nowadays, transfer learning has shown promising results in many applications. However, most deep transfer learning methods such as parameter sharing and fine‐tuning are still suffering from the lack of parameters transmission strategy. In this paper, we propose a new optimization model for parameter‐based transfer learning in convolutional neural networks named STP‐CNN. Indeed, we propose a Lasso transfer model supported by a regularization term that controls transferability. Moreover, we opt for the proximal gradient descent method to solve the proposed model. The suggested technique allows, under certain conditions, to control exactly which parameters, in each convolutional layer of the source network, which will be used directly or adjusted in the target network. Several experiments prove the performance of our model in locating the transferable parameters as well as improving the data classification.

如今，迁移学习在许多应用中都取得了可喜的成果。然而，大多数深度迁移学习方法，如参数共享和微调，仍存在缺乏参数传输策略的问题。在本文中，我们为卷积神经网络中基于参数的迁移学习提出了一种新的优化模型，命名为 STP-CNN。事实上，我们提出了一种 Lasso 转移模型，该模型由一个控制可转移性的正则化项支持。此外，我们还选择了近似梯度下降法来求解所提出的模型。在某些条件下，所建议的技术可以精确控制源网络每个卷积层中的参数，这些参数将直接用于目标网络或在目标网络中进行调整。一些实验证明了我们的模型在定位可转移参数和改进数据分类方面的性能。

引用次数: 0

下一页尾页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

Expert Systems

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀