Multi-tiered Artificial Neural Networks model for intrusion detection in smart homes
In recent years cybersecurity has become a major concern in the adaptation of smart applications. A secure and trusted mechanism can provide peace of mind for users, especially in smart homes where a large number of IoT devices are used. Artificial Neural Networks (ANN) provide promising results for...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | Intelligent Systems with Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667305322000898 |
| _version_ | 1811225335412293632 |
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| author | Shaleeza Sohail Zongwen Fan Xin Gu Fariza Sabrina |
| author_facet | Shaleeza Sohail Zongwen Fan Xin Gu Fariza Sabrina |
| author_sort | Shaleeza Sohail |
| collection | DOAJ |
| description | In recent years cybersecurity has become a major concern in the adaptation of smart applications. A secure and trusted mechanism can provide peace of mind for users, especially in smart homes where a large number of IoT devices are used. Artificial Neural Networks (ANN) provide promising results for detecting any security attacks on smart applications. However, due to the complex nature of the model used for this technique, it is not easy for common users to trust ANN-based security solutions. Also, the selection of the right hyperparameters for ANN architecture plays a crucial role in the accurate detection of security attacks. This paper proposes Multi-tiered ANN Model for Intrusion Detection (MAMID) that is a novel and scalable solution for optimal hyperparameter selection to detect security attacks with high accuracy. The explainability analysis of the predictions made by the model is also provided for establishing trust among users. The approach considers a subset of the dataset for quick, scalable and optimal selection of hyperparameters to reduce the overhead of the process of ANN architecture design. Using a very recent IoT dataset the proposed approach showed high performance for intrusion detection with 99.9%, 99.7%, and 97.7% accuracy for binary, category, and subcategory classification of attacks. To the best of the authors' knowledge, no previous research work has been able to achieve attack detection at the subcategory level beyond 90%. |
| first_indexed | 2024-04-12T09:05:18Z |
| format | Article |
| id | doaj.art-dac091aeafbf4c448384018a35440cea |
| institution | Directory Open Access Journal |
| issn | 2667-3053 |
| language | English |
| last_indexed | 2024-04-12T09:05:18Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Intelligent Systems with Applications |
| spelling | doaj.art-dac091aeafbf4c448384018a35440cea2022-12-22T03:39:07ZengElsevierIntelligent Systems with Applications2667-30532022-11-0116200152Multi-tiered Artificial Neural Networks model for intrusion detection in smart homesShaleeza Sohail0Zongwen Fan1Xin Gu2Fariza Sabrina3School of Information Technology, King's Own Institute, Sydney, NSW 2000, Australia; College of Engineering, Science and Environment, The University of Newcastle, Sydney, NSW 2000, AustraliaCollege of Computer Science and Technology, Huaqiao University, Xiamen 361021, China; Corresponding author. All authors contributed equally to this work.College of Engineering, Science and Environment, The University of Newcastle, Callaghan, NSW 2308, AustraliaSchool of Engineering and Technology, Central Queensland University, Sydney, NSW 2000, AustraliaIn recent years cybersecurity has become a major concern in the adaptation of smart applications. A secure and trusted mechanism can provide peace of mind for users, especially in smart homes where a large number of IoT devices are used. Artificial Neural Networks (ANN) provide promising results for detecting any security attacks on smart applications. However, due to the complex nature of the model used for this technique, it is not easy for common users to trust ANN-based security solutions. Also, the selection of the right hyperparameters for ANN architecture plays a crucial role in the accurate detection of security attacks. This paper proposes Multi-tiered ANN Model for Intrusion Detection (MAMID) that is a novel and scalable solution for optimal hyperparameter selection to detect security attacks with high accuracy. The explainability analysis of the predictions made by the model is also provided for establishing trust among users. The approach considers a subset of the dataset for quick, scalable and optimal selection of hyperparameters to reduce the overhead of the process of ANN architecture design. Using a very recent IoT dataset the proposed approach showed high performance for intrusion detection with 99.9%, 99.7%, and 97.7% accuracy for binary, category, and subcategory classification of attacks. To the best of the authors' knowledge, no previous research work has been able to achieve attack detection at the subcategory level beyond 90%.http://www.sciencedirect.com/science/article/pii/S2667305322000898CybersecurityArtificial Neural NetworksSubcategory attack classificationHyperparameter selectionExplainability |
| spellingShingle | Shaleeza Sohail Zongwen Fan Xin Gu Fariza Sabrina Multi-tiered Artificial Neural Networks model for intrusion detection in smart homes Intelligent Systems with Applications Cybersecurity Artificial Neural Networks Subcategory attack classification Hyperparameter selection Explainability |
| title | Multi-tiered Artificial Neural Networks model for intrusion detection in smart homes |
| title_full | Multi-tiered Artificial Neural Networks model for intrusion detection in smart homes |
| title_fullStr | Multi-tiered Artificial Neural Networks model for intrusion detection in smart homes |
| title_full_unstemmed | Multi-tiered Artificial Neural Networks model for intrusion detection in smart homes |
| title_short | Multi-tiered Artificial Neural Networks model for intrusion detection in smart homes |
| title_sort | multi tiered artificial neural networks model for intrusion detection in smart homes |
| topic | Cybersecurity Artificial Neural Networks Subcategory attack classification Hyperparameter selection Explainability |
| url | http://www.sciencedirect.com/science/article/pii/S2667305322000898 |
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