Security and Privacy in Communication Networks. 8th International ICST Conference, SecureComm 2012, Padua, Italy, September 3-5, 2012. Revised Selected Papers

Research Article

A Voice Spam Filter to Clean Subscribers’ Mailbox

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  • @INPROCEEDINGS{10.1007/978-3-642-36883-7_21,
        author={Seyed Iranmanesh and Hemant Sengar and Haining Wang},
        title={A Voice Spam Filter to Clean Subscribers’ Mailbox},
        proceedings={Security and Privacy in Communication Networks. 8th International ICST Conference, SecureComm 2012, Padua, Italy, September 3-5, 2012. Revised Selected Papers},
        proceedings_a={SECURECOMM},
        year={2013},
        month={2},
        keywords={VoIP voice spam content filtering Dynamic Time Warping},
        doi={10.1007/978-3-642-36883-7_21}
    }
    
  • Seyed Iranmanesh
    Hemant Sengar
    Haining Wang
    Year: 2013
    A Voice Spam Filter to Clean Subscribers’ Mailbox
    SECURECOMM
    Springer
    DOI: 10.1007/978-3-642-36883-7_21
Seyed Iranmanesh1,*, Hemant Sengar2,*, Haining Wang1,*
  • 1: College of William and Mary
  • 2: VoDaSec Solutions
*Contact email: sairan@cs.wm.edu, hsengar09@gmail.com, hnw@cs.wm.edu

Abstract

With the growing popularity of VoIP and its large customer base, the incentives of telemarketers for voice spam has been increasing in the recent years. If the threat of voice spam remains unchecked, it could become a problem as serious as email spam today. Compared to email spam, voice spam will be much more obnoxious and time consuming nuisance for telephone subscribers to filter out. In this paper, we propose a content-based approach to protect telephone subscribers voice mailboxes from voice spam. In particular, based on Dynamic Time Warping (DTW), we develop a speaker independent speech recognition system to make content comparison of speech messages. Using our system, the voice messages left on the media server by callers are matched against a set of spam filtering rules involving the study of pattern and the analysis of . The uniqueness of our spam filtering approach lies in its independence on the generation of voice spam, regardless whether spammers play same spam content recorded in many different ways, such as human or machine generated voice, male or female voice, and different accents. We validate the efficacy of the proposed scheme through real experiments, and our experimental results show that it can effectively filter out spam from the subscribers’ voice mailbox with 0.67% false positive rate and 8.33% false negative rate.