A Lightweight Perceptron-Based Intrusion Detection System for Fog Computing
Fog computing is a paradigm that extends cloud computing and services to the edge of the network in order to address the inherent problems of the cloud, such as latency and lack of mobility support and location-awareness. The fog is a decentralized platform capable of operating and processing data l...
| Auteurs principaux: | , , , , |
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| Format: | Article |
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MDPI
2019
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| Sujets: |
| _version_ | 1825721418729390080 |
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| author | Khater, Belal Sudqi Wahab, Ainuddin Wahid Abdul Idris, Mohd Yamani Idna Hussain, Mohammed Abdulla Ibrahim, Ashraf Ahmed |
| author_facet | Khater, Belal Sudqi Wahab, Ainuddin Wahid Abdul Idris, Mohd Yamani Idna Hussain, Mohammed Abdulla Ibrahim, Ashraf Ahmed |
| author_sort | Khater, Belal Sudqi |
| collection | UM |
| description | Fog computing is a paradigm that extends cloud computing and services to the edge of the network in order to address the inherent problems of the cloud, such as latency and lack of mobility support and location-awareness. The fog is a decentralized platform capable of operating and processing data locally and can be installed in heterogeneous hardware which makes it ideal for Internet of Things (IoT) applications. Intrusion Detection Systems (IDSs) are an integral part of any security system for fog and IoT networks to ensure the quality of service. Due to the resource limitations of fog and IoT devices, lightweight IDS is highly desirable. In this paper, we present a lightweight IDS based on a vector space representation using a Multilayer Perceptron (MLP) model. We evaluated the presented IDS against the Australian Defense Force Academy Linux Dataset (ADFA-LD) and Australian Defense Force AcademyWindows Dataset (ADFA-WD), which are new generation system calls datasets that contain exploits and attacks on various applications. The simulation shows that by using a single hidden layer and a small number of nodes, we are able to achieve a 94% Accuracy, 95% Recall, and 92% F1-Measure in ADFA-LD and 74% Accuracy, 74% Recall, and 74% F1-Measure in ADFA-WD. The performance is evaluated using a Raspberry Pi. |
| first_indexed | 2024-03-06T05:50:03Z |
| format | Article |
| id | um.eprints-20048 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:50:03Z |
| publishDate | 2019 |
| publisher | MDPI |
| record_format | dspace |
| spelling | um.eprints-200482019-01-17T04:59:04Z http://eprints.um.edu.my/20048/ A Lightweight Perceptron-Based Intrusion Detection System for Fog Computing Khater, Belal Sudqi Wahab, Ainuddin Wahid Abdul Idris, Mohd Yamani Idna Hussain, Mohammed Abdulla Ibrahim, Ashraf Ahmed QA75 Electronic computers. Computer science Fog computing is a paradigm that extends cloud computing and services to the edge of the network in order to address the inherent problems of the cloud, such as latency and lack of mobility support and location-awareness. The fog is a decentralized platform capable of operating and processing data locally and can be installed in heterogeneous hardware which makes it ideal for Internet of Things (IoT) applications. Intrusion Detection Systems (IDSs) are an integral part of any security system for fog and IoT networks to ensure the quality of service. Due to the resource limitations of fog and IoT devices, lightweight IDS is highly desirable. In this paper, we present a lightweight IDS based on a vector space representation using a Multilayer Perceptron (MLP) model. We evaluated the presented IDS against the Australian Defense Force Academy Linux Dataset (ADFA-LD) and Australian Defense Force AcademyWindows Dataset (ADFA-WD), which are new generation system calls datasets that contain exploits and attacks on various applications. The simulation shows that by using a single hidden layer and a small number of nodes, we are able to achieve a 94% Accuracy, 95% Recall, and 92% F1-Measure in ADFA-LD and 74% Accuracy, 74% Recall, and 74% F1-Measure in ADFA-WD. The performance is evaluated using a Raspberry Pi. MDPI 2019 Article PeerReviewed Khater, Belal Sudqi and Wahab, Ainuddin Wahid Abdul and Idris, Mohd Yamani Idna and Hussain, Mohammed Abdulla and Ibrahim, Ashraf Ahmed (2019) A Lightweight Perceptron-Based Intrusion Detection System for Fog Computing. Applied Sciences, 9 (1). p. 178. ISSN 2076-3417, DOI https://doi.org/10.3390/app9010178 <https://doi.org/10.3390/app9010178>. https://doi.org/10.3390/app9010178 doi:10.3390/app9010178 |
| spellingShingle | QA75 Electronic computers. Computer science Khater, Belal Sudqi Wahab, Ainuddin Wahid Abdul Idris, Mohd Yamani Idna Hussain, Mohammed Abdulla Ibrahim, Ashraf Ahmed A Lightweight Perceptron-Based Intrusion Detection System for Fog Computing |
| title | A Lightweight Perceptron-Based Intrusion Detection System for Fog Computing |
| title_full | A Lightweight Perceptron-Based Intrusion Detection System for Fog Computing |
| title_fullStr | A Lightweight Perceptron-Based Intrusion Detection System for Fog Computing |
| title_full_unstemmed | A Lightweight Perceptron-Based Intrusion Detection System for Fog Computing |
| title_short | A Lightweight Perceptron-Based Intrusion Detection System for Fog Computing |
| title_sort | lightweight perceptron based intrusion detection system for fog computing |
| topic | QA75 Electronic computers. Computer science |
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