IoT intrusion detection method for unbalanced samples
In recent years, network traffic increases exponentially with the iteration of devices, while more and more attacks are launched against various applications.It is significant to identify and classify attacks at the traffic level.At the same time, with the explosion of Internet of Things (IoT) devic...
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POSTS&TELECOM PRESS Co., LTD
2023-02-01
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| Series: | 网络与信息安全学报 |
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| Online Access: | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2023005 |
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| _version_ | 1841529635980967936 |
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| author | ANTONG P Wen CHEN Lifa WU |
| author_facet | ANTONG P Wen CHEN Lifa WU |
| author_sort | ANTONG P |
| collection | DOAJ |
| description | In recent years, network traffic increases exponentially with the iteration of devices, while more and more attacks are launched against various applications.It is significant to identify and classify attacks at the traffic level.At the same time, with the explosion of Internet of Things (IoT) devices in recent years, attacks on IoT devices are also increasing, causing more and more damages.IoT intrusion detection is able to distinguish attack traffic from such a large volume of traffic, secure IoT devices at the traffic level, and stop the attack activity.In view of low detection accuracy of various attacks and sample imbalance at present, a random forest based intrusion detection method (Resample-RF) was proposed, which consisted of three specific methods: optimal sample selection algorithm, feature merging algorithm based on information entropy, and multi-classification greedy transformation algorithm.Aiming at the problem of unbalanced samples in the IoT environment, an optimal sample selection algorithm was proposed to increase the weight of small samples.Aiming at the low efficiency problem of random forest feature splitting, a feature merging method based on information entropy was proposed to improve the running efficiency.Aiming at the low accuracy problem of random forest multi-classification, a multi-classification greedy transformation method was proposed to further improve the accuracy.The method was evaluated on two public datasets.F1 reaches 0.99 on IoT-23 dataset and 1.0 on Kaggle dataset, both of which have good performance.The experimental results show that the proposed model can effectively identify the attack traffic from the massive traffic, better prevent the attack of hackers on the application, protect the IoT devices, and thus protect the related users. |
| format | Article |
| id | doaj-art-a6c79fec43e24217b053094ff653a6c6 |
| institution | Kabale University |
| issn | 2096-109X |
| language | English |
| publishDate | 2023-02-01 |
| publisher | POSTS&TELECOM PRESS Co., LTD |
| record_format | Article |
| series | 网络与信息安全学报 |
| spelling | doaj-art-a6c79fec43e24217b053094ff653a6c62025-01-15T03:16:30ZengPOSTS&TELECOM PRESS Co., LTD网络与信息安全学报2096-109X2023-02-01913013959577408IoT intrusion detection method for unbalanced samplesANTONG PWen CHENLifa WUIn recent years, network traffic increases exponentially with the iteration of devices, while more and more attacks are launched against various applications.It is significant to identify and classify attacks at the traffic level.At the same time, with the explosion of Internet of Things (IoT) devices in recent years, attacks on IoT devices are also increasing, causing more and more damages.IoT intrusion detection is able to distinguish attack traffic from such a large volume of traffic, secure IoT devices at the traffic level, and stop the attack activity.In view of low detection accuracy of various attacks and sample imbalance at present, a random forest based intrusion detection method (Resample-RF) was proposed, which consisted of three specific methods: optimal sample selection algorithm, feature merging algorithm based on information entropy, and multi-classification greedy transformation algorithm.Aiming at the problem of unbalanced samples in the IoT environment, an optimal sample selection algorithm was proposed to increase the weight of small samples.Aiming at the low efficiency problem of random forest feature splitting, a feature merging method based on information entropy was proposed to improve the running efficiency.Aiming at the low accuracy problem of random forest multi-classification, a multi-classification greedy transformation method was proposed to further improve the accuracy.The method was evaluated on two public datasets.F1 reaches 0.99 on IoT-23 dataset and 1.0 on Kaggle dataset, both of which have good performance.The experimental results show that the proposed model can effectively identify the attack traffic from the massive traffic, better prevent the attack of hackers on the application, protect the IoT devices, and thus protect the related users.http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2023005traffic analysisIoTintrusion detectionrandom forestunbalanced sample |
| spellingShingle | ANTONG P Wen CHEN Lifa WU IoT intrusion detection method for unbalanced samples 网络与信息安全学报 traffic analysis IoT intrusion detection random forest unbalanced sample |
| title | IoT intrusion detection method for unbalanced samples |
| title_full | IoT intrusion detection method for unbalanced samples |
| title_fullStr | IoT intrusion detection method for unbalanced samples |
| title_full_unstemmed | IoT intrusion detection method for unbalanced samples |
| title_short | IoT intrusion detection method for unbalanced samples |
| title_sort | iot intrusion detection method for unbalanced samples |
| topic | traffic analysis IoT intrusion detection random forest unbalanced sample |
| url | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2023005 |
| work_keys_str_mv | AT antongp iotintrusiondetectionmethodforunbalancedsamples AT wenchen iotintrusiondetectionmethodforunbalancedsamples AT lifawu iotintrusiondetectionmethodforunbalancedsamples |