Improving Network-Based Anomaly Detection in Smart Home Environment
The Smart Home (SH) has become an appealing target of cyberattacks. Due to the limitation of hardware resources and the various operating systems (OS) of current SH devices, existing security features cannot protect such an environment. Generally, the traffic patterns of an SH IoT device under attac...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-07-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/15/5626 |
| _version_ | 1797432660846444544 |
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| author | Xiaonan Li Hossein Ghodosi Chao Chen Mangalam Sankupellay Ickjai Lee |
| author_facet | Xiaonan Li Hossein Ghodosi Chao Chen Mangalam Sankupellay Ickjai Lee |
| author_sort | Xiaonan Li |
| collection | DOAJ |
| description | The Smart Home (SH) has become an appealing target of cyberattacks. Due to the limitation of hardware resources and the various operating systems (OS) of current SH devices, existing security features cannot protect such an environment. Generally, the traffic patterns of an SH IoT device under attack often changes in the Home Area Network (HAN). Therefore, a Network-Based Intrusion Detection System (NIDS) logically becomes the forefront security solution for the SH. In this paper, we propose a novel method to assist classification machine learning algorithms generate an anomaly-based NIDS detection model, hence, detecting the abnormal SH IoT device network behaviour. Three network-based attacks were used to evaluate our NIDS solution in a simulated SH test-bed environment. The detection model generated by traditional and ensemble classification Mechanical Learning (ML) methods shows outstanding overall performance. The accuracy of all detection models is over 98.8%. |
| first_indexed | 2024-03-09T10:05:50Z |
| format | Article |
| id | doaj.art-ceb9b505582048c5899780d24a1fc2a4 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T10:05:50Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ceb9b505582048c5899780d24a1fc2a42023-12-01T23:09:38ZengMDPI AGSensors1424-82202022-07-012215562610.3390/s22155626Improving Network-Based Anomaly Detection in Smart Home EnvironmentXiaonan Li0Hossein Ghodosi1Chao Chen2Mangalam Sankupellay3Ickjai Lee4Discipline of Information Technology, College of Science & Engineering, James Cook University, Townsville, QLD 4811, AustraliaDiscipline of Information Technology, College of Science & Engineering, James Cook University, Townsville, QLD 4811, AustraliaDiscipline of Information Technology, College of Science & Engineering, James Cook University, Townsville, QLD 4811, AustraliaDiscipline of Information Technology, College of Science & Engineering, James Cook University, Townsville, QLD 4811, AustraliaDiscipline of Information Technology, College of Science & Engineering, James Cook University, Townsville, QLD 4811, AustraliaThe Smart Home (SH) has become an appealing target of cyberattacks. Due to the limitation of hardware resources and the various operating systems (OS) of current SH devices, existing security features cannot protect such an environment. Generally, the traffic patterns of an SH IoT device under attack often changes in the Home Area Network (HAN). Therefore, a Network-Based Intrusion Detection System (NIDS) logically becomes the forefront security solution for the SH. In this paper, we propose a novel method to assist classification machine learning algorithms generate an anomaly-based NIDS detection model, hence, detecting the abnormal SH IoT device network behaviour. Three network-based attacks were used to evaluate our NIDS solution in a simulated SH test-bed environment. The detection model generated by traditional and ensemble classification Mechanical Learning (ML) methods shows outstanding overall performance. The accuracy of all detection models is over 98.8%.https://www.mdpi.com/1424-8220/22/15/5626smart home securityanomaly detectionmechanical learning |
| spellingShingle | Xiaonan Li Hossein Ghodosi Chao Chen Mangalam Sankupellay Ickjai Lee Improving Network-Based Anomaly Detection in Smart Home Environment Sensors smart home security anomaly detection mechanical learning |
| title | Improving Network-Based Anomaly Detection in Smart Home Environment |
| title_full | Improving Network-Based Anomaly Detection in Smart Home Environment |
| title_fullStr | Improving Network-Based Anomaly Detection in Smart Home Environment |
| title_full_unstemmed | Improving Network-Based Anomaly Detection in Smart Home Environment |
| title_short | Improving Network-Based Anomaly Detection in Smart Home Environment |
| title_sort | improving network based anomaly detection in smart home environment |
| topic | smart home security anomaly detection mechanical learning |
| url | https://www.mdpi.com/1424-8220/22/15/5626 |
| work_keys_str_mv | AT xiaonanli improvingnetworkbasedanomalydetectioninsmarthomeenvironment AT hosseinghodosi improvingnetworkbasedanomalydetectioninsmarthomeenvironment AT chaochen improvingnetworkbasedanomalydetectioninsmarthomeenvironment AT mangalamsankupellay improvingnetworkbasedanomalydetectioninsmarthomeenvironment AT ickjailee improvingnetworkbasedanomalydetectioninsmarthomeenvironment |