Types of attacks and their descriptions.
<div><p>This paper examines the escalating challenge of detecting cyber-attacks within Internet of Things (IoT) networks, where conventional security measures often falter in addressing the speed and complexity of contemporary threats. In response to the necessity for more precise, effic...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | |
| منشور في: |
2025
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| _version_ | 1852015536872882176 |
|---|---|
| author | Quxi Kuang (22493270) |
| author2 | Xianglin Kuang (22493273) |
| author2_role | author |
| author_facet | Quxi Kuang (22493270) Xianglin Kuang (22493273) |
| author_role | author |
| dc.creator.none.fl_str_mv | Quxi Kuang (22493270) Xianglin Kuang (22493273) |
| dc.date.none.fl_str_mv | 2025-10-24T17:43:41Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0333899.t001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Types_of_attacks_and_their_descriptions_/30441479 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biotechnology Biological Sciences not elsewhere classified Information Systems not elsewhere classified support vector machines attacks within internet artificial neural network ann ), achieving adaptive security solutions 7 %, precision deep learning models 93 %, recall various cyber threats deep learning 93 %, contemporary threats detecting cyber 93 %. xlink "> time detection robust solution research contributes paper examines marquardt algorithm highly suitable findings indicate escalating challenge based approach attack detection accuracy rate |
| dc.title.none.fl_str_mv | Types of attacks and their descriptions. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <div><p>This paper examines the escalating challenge of detecting cyber-attacks within Internet of Things (IoT) networks, where conventional security measures often falter in addressing the speed and complexity of contemporary threats. In response to the necessity for more precise, efficient, and adaptive security solutions, we propose a deep learning-based approach that employs feedforward neural networks optimized through the Levenberg-Marquardt algorithm. Our findings indicate that this method markedly surpasses traditional machine learning and deep learning models, such as Support Vector Machines (SVM), Random Forest, and Artificial Neural Network (ANN), achieving an accuracy rate of 99.7%, precision of 99.93%, recall of 99.93%, and an F1-score of 99.93%. Furthermore, the model demonstrates minimal misclassifications and effectively processes substantial data volumes, rendering it highly suitable for the real-time detection of various cyber threats. This system substantially reduces false positive rates and enhances the classification accuracy of different attack types within IoT networks. This research contributes to the advancement of cybersecurity in IoT environments by providing a scalable and robust solution for identifying emerging cyber threats.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_87ce3bbc262b44ef137fb7dba78aa96a |
| identifier_str_mv | 10.1371/journal.pone.0333899.t001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30441479 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Types of attacks and their descriptions.Quxi Kuang (22493270)Xianglin Kuang (22493273)BiotechnologyBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedsupport vector machinesattacks within internetartificial neural networkann ), achievingadaptive security solutions7 %, precisiondeep learning models93 %, recallvarious cyber threatsdeep learning93 %,contemporary threatsdetecting cyber93 %.xlink ">time detectionrobust solutionresearch contributespaper examinesmarquardt algorithmhighly suitablefindings indicateescalating challengebased approachattack detectionaccuracy rate<div><p>This paper examines the escalating challenge of detecting cyber-attacks within Internet of Things (IoT) networks, where conventional security measures often falter in addressing the speed and complexity of contemporary threats. In response to the necessity for more precise, efficient, and adaptive security solutions, we propose a deep learning-based approach that employs feedforward neural networks optimized through the Levenberg-Marquardt algorithm. Our findings indicate that this method markedly surpasses traditional machine learning and deep learning models, such as Support Vector Machines (SVM), Random Forest, and Artificial Neural Network (ANN), achieving an accuracy rate of 99.7%, precision of 99.93%, recall of 99.93%, and an F1-score of 99.93%. Furthermore, the model demonstrates minimal misclassifications and effectively processes substantial data volumes, rendering it highly suitable for the real-time detection of various cyber threats. This system substantially reduces false positive rates and enhances the classification accuracy of different attack types within IoT networks. This research contributes to the advancement of cybersecurity in IoT environments by providing a scalable and robust solution for identifying emerging cyber threats.</p></div>2025-10-24T17:43:41ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0333899.t001https://figshare.com/articles/dataset/Types_of_attacks_and_their_descriptions_/30441479CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/304414792025-10-24T17:43:41Z |
| spellingShingle | Types of attacks and their descriptions. Quxi Kuang (22493270) Biotechnology Biological Sciences not elsewhere classified Information Systems not elsewhere classified support vector machines attacks within internet artificial neural network ann ), achieving adaptive security solutions 7 %, precision deep learning models 93 %, recall various cyber threats deep learning 93 %, contemporary threats detecting cyber 93 %. xlink "> time detection robust solution research contributes paper examines marquardt algorithm highly suitable findings indicate escalating challenge based approach attack detection accuracy rate |
| status_str | publishedVersion |
| title | Types of attacks and their descriptions. |
| title_full | Types of attacks and their descriptions. |
| title_fullStr | Types of attacks and their descriptions. |
| title_full_unstemmed | Types of attacks and their descriptions. |
| title_short | Types of attacks and their descriptions. |
| title_sort | Types of attacks and their descriptions. |
| topic | Biotechnology Biological Sciences not elsewhere classified Information Systems not elsewhere classified support vector machines attacks within internet artificial neural network ann ), achieving adaptive security solutions 7 %, precision deep learning models 93 %, recall various cyber threats deep learning 93 %, contemporary threats detecting cyber 93 %. xlink "> time detection robust solution research contributes paper examines marquardt algorithm highly suitable findings indicate escalating challenge based approach attack detection accuracy rate |