Lightweight Internet of Things Botnet Detection Using One-Class Classification
Like smart phones, the recent years have seen an increased usage of internet of things (IoT) technology. IoT devices, being resource constrained due to smaller size, are vulnerable to various security threats. Recently, many distributed denial of service (DDoS) attacks generated with the help of IoT...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/10/3646 |
| _version_ | 1797495830245015552 |
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| author | Kainat Malik Faisal Rehman Tahir Maqsood Saad Mustafa Osman Khalid Adnan Akhunzada |
| author_facet | Kainat Malik Faisal Rehman Tahir Maqsood Saad Mustafa Osman Khalid Adnan Akhunzada |
| author_sort | Kainat Malik |
| collection | DOAJ |
| description | Like smart phones, the recent years have seen an increased usage of internet of things (IoT) technology. IoT devices, being resource constrained due to smaller size, are vulnerable to various security threats. Recently, many distributed denial of service (DDoS) attacks generated with the help of IoT botnets affected the services of many websites. The destructive botnets need to be detected at the early stage of infection. Machine-learning models can be utilized for early detection of botnets. This paper proposes one-class classifier-based machine-learning solution for the detection of IoT botnets in a heterogeneous environment. The proposed one-class classifier, which is based on one-class KNN, can detect the IoT botnets at the early stage with high accuracy. The proposed machine-learning-based model is a lightweight solution that works by selecting the best features leveraging well-known filter and wrapper methods for feature selection. The proposed strategy is evaluated over different datasets collected from varying network scenarios. The experimental results reveal that the proposed technique shows improved performance, consistent across three different datasets used for evaluation. |
| first_indexed | 2024-03-10T01:55:11Z |
| format | Article |
| id | doaj.art-a17fcfe13e614817a208bfba2f52be6f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T01:55:11Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a17fcfe13e614817a208bfba2f52be6f2023-11-23T12:58:47ZengMDPI AGSensors1424-82202022-05-012210364610.3390/s22103646Lightweight Internet of Things Botnet Detection Using One-Class ClassificationKainat Malik0Faisal Rehman1Tahir Maqsood2Saad Mustafa3Osman Khalid4Adnan Akhunzada5Department of Computer Science, COMSATS University Islamabad, Abbottabad 22060, PakistanDepartment of Computer Science, COMSATS University Islamabad, Abbottabad 22060, PakistanDepartment of Computer Science, COMSATS University Islamabad, Abbottabad 22060, PakistanDepartment of Computer Science, COMSATS University Islamabad, Abbottabad 22060, PakistanDepartment of Computer Science, COMSATS University Islamabad, Abbottabad 22060, PakistanFaculty of Computing and Informatics, University Malaysia Sabah, Kota Kinabalu 88400, MalaysiaLike smart phones, the recent years have seen an increased usage of internet of things (IoT) technology. IoT devices, being resource constrained due to smaller size, are vulnerable to various security threats. Recently, many distributed denial of service (DDoS) attacks generated with the help of IoT botnets affected the services of many websites. The destructive botnets need to be detected at the early stage of infection. Machine-learning models can be utilized for early detection of botnets. This paper proposes one-class classifier-based machine-learning solution for the detection of IoT botnets in a heterogeneous environment. The proposed one-class classifier, which is based on one-class KNN, can detect the IoT botnets at the early stage with high accuracy. The proposed machine-learning-based model is a lightweight solution that works by selecting the best features leveraging well-known filter and wrapper methods for feature selection. The proposed strategy is evaluated over different datasets collected from varying network scenarios. The experimental results reveal that the proposed technique shows improved performance, consistent across three different datasets used for evaluation.https://www.mdpi.com/1424-8220/22/10/3646internet of things (IoT)one-class KNNbotnet detectionclassification |
| spellingShingle | Kainat Malik Faisal Rehman Tahir Maqsood Saad Mustafa Osman Khalid Adnan Akhunzada Lightweight Internet of Things Botnet Detection Using One-Class Classification Sensors internet of things (IoT) one-class KNN botnet detection classification |
| title | Lightweight Internet of Things Botnet Detection Using One-Class Classification |
| title_full | Lightweight Internet of Things Botnet Detection Using One-Class Classification |
| title_fullStr | Lightweight Internet of Things Botnet Detection Using One-Class Classification |
| title_full_unstemmed | Lightweight Internet of Things Botnet Detection Using One-Class Classification |
| title_short | Lightweight Internet of Things Botnet Detection Using One-Class Classification |
| title_sort | lightweight internet of things botnet detection using one class classification |
| topic | internet of things (IoT) one-class KNN botnet detection classification |
| url | https://www.mdpi.com/1424-8220/22/10/3646 |
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