| Title | A Near Real Time SMS Grey Traffic Detection |
| Publication Type | Conference Paper |
| Year of Publication | 2017 |
| Authors | van Do, Thanh, Engelstad, Paal, Feng, Boning, Do, Van Thuan |
| Conference Name | Proceedings of the 6th International Conference on Software and Computer Applications |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-4857-7 |
| Keywords | AI, artificial intelligence, cyber security, Human Behavior, human factor, human factors, machine learning, mobile network fraud, mobile network security, privacy, pubcrawl, resilience, Resiliency, Scalability, Vulnerability |
| Abstract | Lately, mobile operators experience threats from SMS grey routes which are used by fraudsters to evade SMS fees and to deny them millions in revenues. But more serious are the threats to the user's security and privacy and consequently the operator's reputation. Therefore, it is crucial for operators to have adequate solutions to protect both their network and their customers against this kind of fraud. Unfortunately, so far there is no sufficiently efficient countermeasure against grey routes. This paper proposes a near real time SMS grey traffic detection which makes use of Counting Bloom Filters combined with blacklist and whitelist to detect SMS grey traffic on the fly and to block them. The proposed detection has been implemented and proved to be quite efficient. The paper provides also comprehensive explanation of SMS grey routes and the challenges in their detection. The implementation and verification are also described thoroughly. |
| URL | http://doi.acm.org/10.1145/3056662.3056687 |
| DOI | 10.1145/3056662.3056687 |
| Citation Key | van_do_near_2017 |
A Near Real Time SMS Grey Traffic Detection