Performance Evaluation of Machine Learning based Robocalls Detection Models in Telephony Networks

Q1 Mathematics
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引用次数: 0

Abstract

Many techniques have been proposed to detect and prevent spam over Internet telephony. Human spam calls can be detected more accurately with these techniques. However, robocalls, a type of voice spammer whose calling patterns are similar to those of legitimate users, cannot be detected as effectively. This paper proposes a model for robocall detection using a machine learning approach. Voice data recordings were collected and the relevant features for study were selected. The selected features were then used to formulate six (6) detection models. The formulated models were simulated and evaluated using some performance metrics to ascertain the model with the best performance. The C4.5 decision tree algorithm gave the best evaluation result with an accuracy of 99.15%, a sensitivity of 0.991%, a false alarm rate of 0.009%, and a precision of 0.992%. As a result, it was concluded that this approach can be used to detect and filter both machine-initiated and human-initiated spam calls.
基于机器学习的电话网络自动呼叫检测模型的性能评估
已经提出了许多技术来检测和防止互联网电话上的垃圾邮件。使用这些技术可以更准确地检测人类垃圾邮件呼叫。然而,机器人语音是一种语音垃圾邮件发送者,其呼叫模式与合法用户的呼叫模式相似,无法有效检测到。本文提出了一种使用机器学习方法的机器人语音检测模型。收集语音数据记录,并选择相关特征进行研究。然后使用所选择的特征来制定六(6)个检测模型。使用一些性能指标对公式化的模型进行了模拟和评估,以确定具有最佳性能的模型。C4.5决策树算法给出了最佳的评估结果,准确率为99.15%,灵敏度为0.991%,误报率为0.009%,精度为0.992%。结果表明,该方法可用于检测和过滤机器发起和人工发起的垃圾邮件呼叫。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.10
自引率
0.00%
发文量
33
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