The Impact of Device Type Number on IoT Device Classification

Ahmet Emre Ergün; Özgü Can

Makale detayları

The Impact of Device Type Number on IoT Device Classification

Yazarlar:

Ahmet Emre Ergün | Özgü Can

Yayıncı:

Karyay Karadeniz Yayımcılık

Ana Başlıklar:

Bilgi Güvenliği Yönetimi | Bilgi Sistemleri (Diğer)

Dergi:

Black Sea Journal of Engineering and Science Vol.7 Issue 3, pp.488-494

Belge Tipi:

Makale

Yayın Tarihi:

5/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001315136

pISSN:

2619-8991

Anahtar kelimeler:

Özet:

 Today, connected systems are widely used with the recent developments in technology. The internet-connected devices create data traffic when communicating with each other. These data may contain extremely confidential information. Observers can obtain confidential information from the traffic when the security of this traffic cannot be adequately ensured. This confidential information can be personal information as well as information about the type of device used by the person. Attackers could use machine learning to analyze encrypted data traffic patterns from IoT devices to infer sensitive information, even without decrypting the actual content. For example, if someone uses IoT devices for health monitoring or smoke detection, attackers could leverage machine learning to discern victims habits or identify health conditions. An increase in the number of IoT devices may decrease the accuracy of classification when using machine learning. This paper presents the importance of the effect of device type number on the classification of IoT devices. Therefore, inference attacks on privacy with machine learning algorithms, attacks on machine learning models, and the padding method that is commonly used against such attacks are presented. Moreover, experiments are carried out by using the dataset of the traffic generated by the Internet of Things IoT devices. For this purpose, Random Forest, Decision Tree, and k-Nearest Neighbors k-NN classification algorithms are compared, and the accuracy rate changes according to the number of devices are presented. According to the results, the Random Forest and Decision Tree algorithms are found to be more effective than the k-NN algorithm. When considering a scenario with two device types, the Random Forest and Decision Tree algorithms achieved an accuracy rate of 98, outperforming the k-NN algorithm, which had an accuracy rate of 95.

 Today, connected systems are widely used with the recent developments in technology. The internet-connected devices create data traffic when communicating with each other. These data may contain extremely confidential information. Observers can obtain confidential information from the traffic when the security of this traffic cannot be adequately ensured. This confidential information can be personal information as well as information about the type of device used by the person. Attackers could use machine learning to analyze encrypted data traffic patterns from IoT devices to infer sensitive information, even without decrypting the actual content. For example, if someone uses IoT devices for health monitoring or smoke detection, attackers could leverage machine learning to discern victims habits or identify health conditions. An increase in the number of IoT devices may decrease the accuracy of classification when using machine learning. This paper presents the importance of the effect of device type number on the classification of IoT devices. Therefore, inference attacks on privacy with machine learning algorithms, attacks on machine learning models, and the padding method that is commonly used against such attacks are presented. Moreover, experiments are carried out by using the dataset of the traffic generated by the Internet of Things IoT devices. For this purpose, Random Forest, Decision Tree, and k-Nearest Neighbors k-NN classification algorithms are compared, and the accuracy rate changes according to the number of devices are presented. According to the results, the Random Forest and Decision Tree algorithms are found to be more effective than the k-NN algorithm. When considering a scenario with two device types, the Random Forest and Decision Tree algorithms achieved an accuracy rate of 98, outperforming the k-NN algorithm, which had an accuracy rate of 95.

 Today, connected systems are widely used with the recent developments in technology. The internet-connected devices create data traffic when communicating with each other. These data may contain extremely confidential information. Observers can obtain confidential information from the traffic when the security of this traffic cannot be adequately ensured. This confidential information can be personal information as well as information about the type of device used by the person. Attackers could use machine learning to analyze encrypted data traffic patterns from IoT devices to infer sensitive information, even without decrypting the actual content. For example, if someone uses IoT devices for health monitoring or smoke detection, attackers could leverage machine learning to discern victims habits or identify health conditions. An increase in the number of IoT devices may decrease the accuracy of classification when using machine learning. This paper presents the importance of the effect of device type number on the classification of IoT devices. Therefore, inference attacks on privacy with machine learning algorithms, attacks on machine learning models, and the padding method that is commonly used against such attacks are presented. Moreover, experiments are carried out by using the dataset of the traffic generated by the Internet of Things IoT devices. For this purpose, Random Forest, Decision Tree, and k-Nearest Neighbors k-NN classification algorithms are compared, and the accuracy rate changes according to the number of devices are presented. According to the results, the Random Forest and Decision Tree algorithms are found to be more effective than the k-NN algorithm. When considering a scenario with two device types, the Random Forest and Decision Tree algorithms achieved an accuracy rate of 98, outperforming the k-NN algorithm, which had an accuracy rate of 95.

 Today, connected systems are widely used with the recent developments in technology. The internet-connected devices create data traffic when communicating with each other. These data may contain extremely confidential information. Observers can obtain confidential information from the traffic when the security of this traffic cannot be adequately ensured. This confidential information can be personal information as well as information about the type of device used by the person. Attackers could use machine learning to analyze encrypted data traffic patterns from IoT devices to infer sensitive information, even without decrypting the actual content. For example, if someone uses IoT devices for health monitoring or smoke detection, attackers could leverage machine learning to discern victims habits or identify health conditions. An increase in the number of IoT devices may decrease the accuracy of classification when using machine learning. This paper presents the importance of the effect of device type number on the classification of IoT devices. Therefore, inference attacks on privacy with machine learning algorithms, attacks on machine learning models, and the padding method that is commonly used against such attacks are presented. Moreover, experiments are carried out by using the dataset of the traffic generated by the Internet of Things IoT devices. For this purpose, Random Forest, Decision Tree, and k-Nearest Neighbors k-NN classification algorithms are compared, and the accuracy rate changes according to the number of devices are presented. According to the results, the Random Forest and Decision Tree algorithms are found to be more effective than the k-NN algorithm. When considering a scenario with two device types, the Random Forest and Decision Tree algorithms achieved an accuracy rate of 98, outperforming the k-NN algorithm, which had an accuracy rate of 95.

Makale detayları

The Impact of Device Type Number on IoT Device Classification

Yazarlar:

Ahmet Emre Ergün | Özgü Can

Yayıncı:

Karyay Karadeniz Yayımcılık

Ana Başlıklar:

Bilgi Güvenliği Yönetimi | Bilgi Sistemleri (Diğer)

Dergi:

Black Sea Journal of Engineering and Science Vol.7 Issue 3, pp.488-494

Belge Tipi:

Makale

Yayın Tarihi:

5/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001315136

pISSN:

2619-8991

Classification

Internet of things (IoT)

Machine learning

Padding

Privacy

Trade-offClassification

Internet of things (IoT)

Machine learning

Padding

Privacy

Trade-off

Sayı

Makale detayları

Yazarlar: Ahmet Emre Ergün | Özgü Can

Yayıncı: Karyay Karadeniz Yayımcılık

Ana Başlıklar: Bilgi Güvenliği Yönetimi | Bilgi Sistemleri (Diğer)

Dergi: Black Sea Journal of Engineering and Science Vol.7 Issue 3, pp.488-494

Belge Tipi: Makale

Yayın Tarihi: 5/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001315136

pISSN: 2619-8991

Sayı

Yazarlar:

Ahmet Emre Ergün | Özgü Can

Yayıncı:

Karyay Karadeniz Yayımcılık

Ana Başlıklar:

Bilgi Güvenliği Yönetimi | Bilgi Sistemleri (Diğer)

Dergi:

Black Sea Journal of Engineering and Science Vol.7 Issue 3, pp.488-494

Belge Tipi:

Makale

Yayın Tarihi:

5/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001315136

pISSN:

2619-8991