Efficient Detection of Spam Over Internet Telephony by Machine Learning Algorithms
Recent trends show a growing interest in VoIP services and indicate that guaranteeing security in VoIP services and preventing hacker communities from attacking telecommunication solutions is a challenging task. Spam over Internet Telephony (SPIT) is a type of attack which is a significant detriment...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9996400/ |
| _version_ | 1828084465353621504 |
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| author | Ladislav Behan Jan Rozhon Jakub Safarik Filip Rezac Miroslav Voznak |
| author_facet | Ladislav Behan Jan Rozhon Jakub Safarik Filip Rezac Miroslav Voznak |
| author_sort | Ladislav Behan |
| collection | DOAJ |
| description | Recent trends show a growing interest in VoIP services and indicate that guaranteeing security in VoIP services and preventing hacker communities from attacking telecommunication solutions is a challenging task. Spam over Internet Telephony (SPIT) is a type of attack which is a significant detriment to the user’s experience. A number of techniques have been produced to detect SPIT calls. We reviewed these techniques and have proposed a new approach for quick, efficient and highly accurate detection of SPIT calls using neural networks and novel call parameters. The performance of this system was compared to other state-of-art machine learning algorithms on a real-world dataset, which has been published online and is publicly available. The results of the study demonstrated that new parameters may help improve the effectiveness and accuracy of applied machine learning algorithms. The study explored the entire process of designing a SPIT detection algorithm, including data collection and processing, defining suitable parameters, and final evaluation of machine learning models. |
| first_indexed | 2024-04-11T04:25:23Z |
| format | Article |
| id | doaj.art-d1a2df42f17c4d5da9906fbfce60b82e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T04:25:23Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d1a2df42f17c4d5da9906fbfce60b82e2022-12-30T00:00:11ZengIEEEIEEE Access2169-35362022-01-011013341213342610.1109/ACCESS.2022.32313849996400Efficient Detection of Spam Over Internet Telephony by Machine Learning AlgorithmsLadislav Behan0https://orcid.org/0000-0001-6147-1947Jan Rozhon1Jakub Safarik2https://orcid.org/0000-0002-3360-2302Filip Rezac3Miroslav Voznak4https://orcid.org/0000-0001-5135-7980Department of Telecommunications, Faculty of Electrical Engineering and Computer Science, VSB—Technical University of Ostrava, Ostrava, CzechiaDepartment of Telecommunications, Faculty of Electrical Engineering and Computer Science, VSB—Technical University of Ostrava, Ostrava, CzechiaIT4Innovations National Supercomputing Center, VSB—Technical University of Ostrava, Ostrava, CzechiaDepartment of Telecommunications, Faculty of Electrical Engineering and Computer Science, VSB—Technical University of Ostrava, Ostrava, CzechiaDepartment of Telecommunications, Faculty of Electrical Engineering and Computer Science, VSB—Technical University of Ostrava, Ostrava, CzechiaRecent trends show a growing interest in VoIP services and indicate that guaranteeing security in VoIP services and preventing hacker communities from attacking telecommunication solutions is a challenging task. Spam over Internet Telephony (SPIT) is a type of attack which is a significant detriment to the user’s experience. A number of techniques have been produced to detect SPIT calls. We reviewed these techniques and have proposed a new approach for quick, efficient and highly accurate detection of SPIT calls using neural networks and novel call parameters. The performance of this system was compared to other state-of-art machine learning algorithms on a real-world dataset, which has been published online and is publicly available. The results of the study demonstrated that new parameters may help improve the effectiveness and accuracy of applied machine learning algorithms. The study explored the entire process of designing a SPIT detection algorithm, including data collection and processing, defining suitable parameters, and final evaluation of machine learning models.https://ieeexplore.ieee.org/document/9996400/Data miningmachine learningneural networkSIPspamSPIT |
| spellingShingle | Ladislav Behan Jan Rozhon Jakub Safarik Filip Rezac Miroslav Voznak Efficient Detection of Spam Over Internet Telephony by Machine Learning Algorithms IEEE Access Data mining machine learning neural network SIP spam SPIT |
| title | Efficient Detection of Spam Over Internet Telephony by Machine Learning Algorithms |
| title_full | Efficient Detection of Spam Over Internet Telephony by Machine Learning Algorithms |
| title_fullStr | Efficient Detection of Spam Over Internet Telephony by Machine Learning Algorithms |
| title_full_unstemmed | Efficient Detection of Spam Over Internet Telephony by Machine Learning Algorithms |
| title_short | Efficient Detection of Spam Over Internet Telephony by Machine Learning Algorithms |
| title_sort | efficient detection of spam over internet telephony by machine learning algorithms |
| topic | Data mining machine learning neural network SIP spam SPIT |
| url | https://ieeexplore.ieee.org/document/9996400/ |
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