IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Online identification of hierarchical heavy hitters: algorithms, evaluation, and applications
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Using association rules for fraud detection in web advertising networks
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Neighborhood Formation and Anomaly Detection in Bipartite Graphs
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Netprobe: a fast and scalable system for fraud detection in online auction networks
Proceedings of the 16th international conference on World Wide Web
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Behavioral detection of malware on mobile handsets
Proceedings of the 6th international conference on Mobile systems, applications, and services
Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems
Towards automated performance diagnosis in a large IPTV network
Proceedings of the ACM SIGCOMM 2009 conference on Data communication
pBMDS: a behavior-based malware detection system for cellphone devices
Proceedings of the third ACM conference on Wireless network security
Proceedings of the 17th ACM conference on Computer and communications security
Spectrum based fraud detection in social networks
Proceedings of the 17th ACM conference on Computer and communications security
Andbot: towards advanced mobile botnets
LEET'11 Proceedings of the 4th USENIX conference on Large-scale exploits and emergent threats
Identifying suspicious activities through DNS failure graph analysis
ICNP '10 Proceedings of the The 18th IEEE International Conference on Network Protocols
Combining file content and file relations for cloud based malware detection
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Facilitating real-time graph mining
Proceedings of the fourth international workshop on Cloud data management
Discovery of emergent malicious campaigns in cellular networks
Proceedings of the 29th Annual Computer Security Applications Conference
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With widespread adoption and growing sophistication of mobile devices, fraudsters have turned their attention from landlines and wired networks to cellular networks. While security threats to wireless data channels and applications have attracted the most attention, voice-related fraud activities also represent a serious threat to mobile users. In particular, we have seen increasing numbers of incidents where fraudsters deploy malicious apps, e.g., disguised as gaming apps to entice users to download; when invoked, these apps automatically - and without users' knowledge - dial certain (international) phone numbers which charge exorbitantly high fees. Fraudsters also frequently utilize social engineering (e.g., SMS or email spam, Facebook postings) to trick users into dialing these exorbitant fee-charging numbers. In this paper, we develop a novel methodology for detecting voice-related fraud activities using only call records. More specifically, we advance the notion of voice call graphs to represent voice calls from domestic callers to foreign recipients and propose a Markov Clustering based method for isolating dominant fraud activities from these international calls. Using data collected over a two year period from one of the largest cellular networks in the US, we evaluate the efficacy of the proposed fraud detection algorithm and conduct systematic analysis of the identified fraud activities. Our work sheds light on the unique characteristics and trends of fraud activities in cellular networks, and provides guidance on improving and securing hardware/software architecture to prevent these fraud activities.