TY - GEN
T1 - Exploring anti-spam models in large scale VoIP systems
AU - Patankar, Pushkar
AU - Nam, Gunwoo
AU - Kesidis, George
AU - Das, Chita R.
N1 - Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2008
Y1 - 2008
N2 - Although the problem of spam detection in email is well understood and has been extensively researched, a significant portion of emails today are spam. A most widely used method to detect spam involves content filtering, where the spam detector scans the received email for keywords. However, the same approach cannot be applied to detect Voice over IP (VoIP) spam, since a call has to be categorized as a legitimate or a spam (each to a degree with a certain reliability) before the connection is established. Also, spammers over IP can potentially generate orders of magnitude more spam volume, at far less cost, and with greater anonymity than telemarketers using the Public Switch Telephone Network (PSTN). The spam problem in VoIP is further compounded by the absence of a do-not-call-list, which has been the main reason for the reduction of spam calls in PSTN. Thus, the spam issue for VoIP is as important as those pertaining to quality-of-service (QoS) of the voice traffic itself. To this end, we propose two different anti-spam frameworks for large scale VoIP systems. The first one is a centralized SIP-based spam detection framework that relies on SIP messages during the call establishment phase to identify spam calls, and the second one is a distributed referral social network model, where a user is assigned a reputation score by its neighbors. Based on the reputation, a callee can decide either to accept or decline a call. Our simulation results indicate that the referral model can provide better anti-spam capabilities by isolating a spammer faster than the SIP based approach, and can also correctly identify spam calls over 98% of time.
AB - Although the problem of spam detection in email is well understood and has been extensively researched, a significant portion of emails today are spam. A most widely used method to detect spam involves content filtering, where the spam detector scans the received email for keywords. However, the same approach cannot be applied to detect Voice over IP (VoIP) spam, since a call has to be categorized as a legitimate or a spam (each to a degree with a certain reliability) before the connection is established. Also, spammers over IP can potentially generate orders of magnitude more spam volume, at far less cost, and with greater anonymity than telemarketers using the Public Switch Telephone Network (PSTN). The spam problem in VoIP is further compounded by the absence of a do-not-call-list, which has been the main reason for the reduction of spam calls in PSTN. Thus, the spam issue for VoIP is as important as those pertaining to quality-of-service (QoS) of the voice traffic itself. To this end, we propose two different anti-spam frameworks for large scale VoIP systems. The first one is a centralized SIP-based spam detection framework that relies on SIP messages during the call establishment phase to identify spam calls, and the second one is a distributed referral social network model, where a user is assigned a reputation score by its neighbors. Based on the reputation, a callee can decide either to accept or decline a call. Our simulation results indicate that the referral model can provide better anti-spam capabilities by isolating a spammer faster than the SIP based approach, and can also correctly identify spam calls over 98% of time.
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U2 - 10.1109/ICDCS.2008.71
DO - 10.1109/ICDCS.2008.71
M3 - Conference contribution
AN - SCOPUS:51849143096
SN - 9780769531724
T3 - Proceedings - The 28th International Conference on Distributed Computing Systems, ICDCS 2008
SP - 85
EP - 92
BT - Proceedings - The 28th International Conference on Distributed Computing Systems, ICDCS 2008
T2 - 28th International Conference on Distributed Computing Systems, ICDCS 2008
Y2 - 17 July 2008 through 20 July 2008
ER -