Who''s Calling? Malicious Call Detection Using Supervised Learning Algorithms
Date Issued
2014
Date
2014
Author(s)
Chen, Ting-Ni
Abstract
With rapid advancement in technologies, mobile phones have gained popularity and become indispensable. The growth of elecommunication has also given rise to malicious calling behaviors where users may encounter theft of identities or even financial losses. Due to the improvement of user interface
on mobile phone devices, most mobile phone users rely on caller IDs which link to contact book to identify callers. However, it is often difficult to detect whether an unknown ID is malicious or not without additional information.
Recent malicious caller identification establishes blacklists based on user reports. Detecting malicious callers in this fashion proofs to be difficult and inefficient due to the fact that user report is inconsistant and unreliable.
Since there might be differences between malicious and benign call patterns, the aim of this study is to automatically predicting whether an unknown ID is malicious or not by observing their past call histories. In this study, we collected phone call histories in two different countries and applied machine learning algorithms to detect whether an unknown ID is benign or malicious.
We evaluated the ability of different classifiers and compared the experimental results with conventional blacklist approach. Emperical results suggest that the proposed method is effective and can be a viable approach in detecting malicious calls.
Subjects
機器學習
監督式學習
分類
交叉驗證
Type
thesis
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