Augmenting Telephone Spam Blacklists by Mining Large CDR Datasets-Reference-Cited by-同舟云学术

Augmenting Telephone Spam Blacklists by Mining Large CDR Datasets

Published:2018-05-29 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2018 on Asia Conference on Computer and Communications Security
language:
Short-container-title:

Author:

Liu Jienan¹,Rahbarinia Babak²,Perdisci Roberto¹,Du Haitao³,Su Li³

Affiliation:

1. University of Georgia, Athens, GA, USA

2. Auburn University Montgomery, Montgomery, AL, USA

3. China Mobile Research Institute, Beijing, China

Funder

National Science Foundation (USA)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3196494.3196553

Reference23 articles.

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2. Behavior-based approach to detect spam over IP telephony attacks

3. ChuBao . 2016. 2016 China Spam Phone Call Trend Analysis Report. http://www.cnii.com.cn/industry/2016-09/29/content_1784329.htm. (2016). ChuBao . 2016. 2016 China Spam Phone Call Trend Analysis Report. http://www.cnii.com.cn/industry/2016-09/29/content_1784329.htm. (2016).

4. Federal Trade Commission . {n. d.}. Caller ID Spoofing and Call Authentication Technology. https://www.ftc.gov/sites/default/files/documents/public_events/robocalls-all-rage-ftc-summit/robocalls-part5-caller-id-spoofing.pdf. (. {n. d.}). Federal Trade Commission . {n. d.}. Caller ID Spoofing and Call Authentication Technology. https://www.ftc.gov/sites/default/files/documents/public_events/robocalls-all-rage-ftc-summit/robocalls-part5-caller-id-spoofing.pdf. (. {n. d.}).

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