Detecting SPIT Attacks in VoIP Networks Using Convolutional Autoencoders: A Deep Learning Approach
Voice over Internet Protocol (VoIP) is a technology that enables voice communication to be transmitted over the Internet, transforming communication in both personal and business contexts by offering several benefits such as cost savings and integration with other communication systems. However, VoI...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/12/6974 |
| _version_ | 1827738752518193152 |
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| author | Waleed Nazih Khaled Alnowaiser Esraa Eldesouky Osama Youssef Atallah |
| author_facet | Waleed Nazih Khaled Alnowaiser Esraa Eldesouky Osama Youssef Atallah |
| author_sort | Waleed Nazih |
| collection | DOAJ |
| description | Voice over Internet Protocol (VoIP) is a technology that enables voice communication to be transmitted over the Internet, transforming communication in both personal and business contexts by offering several benefits such as cost savings and integration with other communication systems. However, VoIP attacks are a growing concern for organizations that rely on this technology for communication. Spam over Internet Telephony (SPIT) is a type of VoIP attack that involves unwanted calls or messages, which can be both annoying and pose security risks to users. Detecting SPIT can be challenging since it is often delivered from anonymous VoIP accounts or spoofed phone numbers. This paper suggests an anomaly detection model that utilizes a deep convolutional autoencoder to identify SPIT attacks. The model is trained on a dataset of normal traffic and then encodes new traffic into a lower-dimensional latent representation. If the network traffic varies significantly from the encoded normal traffic, the model flags it as anomalous. Additionally, the model was tested on two datasets and achieved F1 scores of 99.32% and 99.56%. Furthermore, the proposed model was compared to several traditional anomaly detection approaches and it outperformed them on both datasets. |
| first_indexed | 2024-03-11T02:48:45Z |
| format | Article |
| id | doaj.art-71e60bc2519843a98b36fc38dbbe0c77 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T02:48:45Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-71e60bc2519843a98b36fc38dbbe0c772023-11-18T09:07:10ZengMDPI AGApplied Sciences2076-34172023-06-011312697410.3390/app13126974Detecting SPIT Attacks in VoIP Networks Using Convolutional Autoencoders: A Deep Learning ApproachWaleed Nazih0Khaled Alnowaiser1Esraa Eldesouky2Osama Youssef Atallah3Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al Kharj 11942, Saudi Arabia Department of Computer Engineering, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al Kharj 11942, Saudi ArabiaDepartment of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al Kharj 11942, Saudi Arabia Department of Biomedical Engineering, Medical Research Institute, Alexandria University, El-Hadra Bahry, Alexandria 21561, EgyptVoice over Internet Protocol (VoIP) is a technology that enables voice communication to be transmitted over the Internet, transforming communication in both personal and business contexts by offering several benefits such as cost savings and integration with other communication systems. However, VoIP attacks are a growing concern for organizations that rely on this technology for communication. Spam over Internet Telephony (SPIT) is a type of VoIP attack that involves unwanted calls or messages, which can be both annoying and pose security risks to users. Detecting SPIT can be challenging since it is often delivered from anonymous VoIP accounts or spoofed phone numbers. This paper suggests an anomaly detection model that utilizes a deep convolutional autoencoder to identify SPIT attacks. The model is trained on a dataset of normal traffic and then encodes new traffic into a lower-dimensional latent representation. If the network traffic varies significantly from the encoded normal traffic, the model flags it as anomalous. Additionally, the model was tested on two datasets and achieved F1 scores of 99.32% and 99.56%. Furthermore, the proposed model was compared to several traditional anomaly detection approaches and it outperformed them on both datasets.https://www.mdpi.com/2076-3417/13/12/6974deep learningautoencodersnetwork securityVoIPSPIT |
| spellingShingle | Waleed Nazih Khaled Alnowaiser Esraa Eldesouky Osama Youssef Atallah Detecting SPIT Attacks in VoIP Networks Using Convolutional Autoencoders: A Deep Learning Approach Applied Sciences deep learning autoencoders network security VoIP SPIT |
| title | Detecting SPIT Attacks in VoIP Networks Using Convolutional Autoencoders: A Deep Learning Approach |
| title_full | Detecting SPIT Attacks in VoIP Networks Using Convolutional Autoencoders: A Deep Learning Approach |
| title_fullStr | Detecting SPIT Attacks in VoIP Networks Using Convolutional Autoencoders: A Deep Learning Approach |
| title_full_unstemmed | Detecting SPIT Attacks in VoIP Networks Using Convolutional Autoencoders: A Deep Learning Approach |
| title_short | Detecting SPIT Attacks in VoIP Networks Using Convolutional Autoencoders: A Deep Learning Approach |
| title_sort | detecting spit attacks in voip networks using convolutional autoencoders a deep learning approach |
| topic | deep learning autoencoders network security VoIP SPIT |
| url | https://www.mdpi.com/2076-3417/13/12/6974 |
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