Efficient Detection of Spam over Internet Telephony by Machine Learning Algorithms

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F22%3A10250888" target="_blank" >RIV/61989100:27740/22:10250888 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27240/22:10250888

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9996400?source=authoralert" target="_blank" >https://ieeexplore.ieee.org/document/9996400?source=authoralert</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2022.3231384" target="_blank" >10.1109/ACCESS.2022.3231384</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Efficient Detection of Spam over Internet Telephony by Machine Learning Algorithms

Popis výsledku v původním jazyce

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&apos;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.

Název v anglickém jazyce

Efficient Detection of Spam over Internet Telephony by Machine Learning Algorithms

Popis výsledku anglicky

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&apos;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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

20203 - Telecommunications

Projekt

<a href="/cs/project/EF15_003%2F0000466" target="_blank" >EF15_003/0000466: Umělá inteligence a uvažování</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

—

Svazek periodika

10

Číslo periodika v rámci svazku

neuvedeno

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

133412-133426

Kód UT WoS článku

000906230200001

EID výsledku v databázi Scopus

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