Caller-REP: Detecting unwanted calls with caller social strength

Azad, Muhammad Ajmal; Morla, Ricardo

doi:10.1016/j.cose.2013.07.006

Cited by 31 publications

(29 citation statements)

References 6 publications

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“…Balasubramaniyan et al propose to use call duration as a measure to generate a reputation score called CallRank . Similarly, Azad and Morola use call rate, call duration and out‐degree distributions of a caller to set a reputation threshold for spammer detection.…”

Section: Prior Workmentioning

confidence: 99%

SpamDetector: Detecting spam callers in Voice over Internet Protocol with graph anomalies

Swarnkar

Hubballi

2018

Security and Privacy

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SPam over Internet Telephony is a major issue in Voice over Internet Protocol (VoIP) systems where a large number of automated unsolicited calls are made to users of VoIP. The economy of communication that VoIP brings is a lucrative proposition to spammers. In this paper, we describe a method to detect spammers in VoIP by identifying anomalies in the CallGraph. This directed, weighted graph is generated using call data records of users where a set of differentiating call parameters are used to derive weights on the edges. We identify anomalies in the graph by considering the local neighborhood of a node under consideration and assign a label based on how similar the node is in comparison to its neighbors. The similarity between a node and its neighbor is measured through a parameter known as SpamOutlierFactor. We experiment with a large simulated user base and show that the proposed method can detect spam callers.

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Section: Prior Workmentioning

confidence: 99%

SpamDetector: Detecting spam callers in Voice over Internet Protocol with graph anomalies

Swarnkar

Hubballi

2018

Security and Privacy

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“…Azad et al vary R SPITters = 10%, 20% and 30% in Ref. [11] while Chaisamran et al vary R SPITters from 1% to 10% in Ref. [29].…”

Section: Classification Accuracy Versus R Spittersmentioning

confidence: 99%

“…It is a similar setting to Ref. [11] which considers 25% of all callers as SPITters. It seems to excessive to consider the cost to obtain SIP addresses.…”

Section: Datasetmentioning

confidence: 99%

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Unsupervised Clustering-based SPITters Detection Scheme

Toyoda

Sasase

2015

Journal of Information Processing

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VoIP/SIP is taking place of conventional telephony because of very low call charge but it is also attractive for SPITters who advertise or spread phishing calls toward many callees. Although there exist many feature-based SPIT detection methods, none of them provides the flexibility against multiple features and thus complex threshold settings and training phases cannot be avoided. In this paper, we propose an unsupervised and threshold-free SPITters detection scheme based on a clustering algorithm. Our scheme does not use multiple features directly to trap SPITters but uses them to find the dissimilarity among each caller pair and tries to separate the callers into a SPITters cluster and a legitimate one based on the dissimilarity. By computer simulation, we show that the combination of Random Forests dissimilarity and PAM clustering brings the best classification accuracy and our scheme works well when the SPITters account for more than 20% of the entire caller.

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“…In particular, a social trust-based approach is receiving much attention due to growing SNS-based voice communication services. A social trust-based approach judges the legitimacy of a call with a trust value calculated from caller-callee relationships [2,3,4,5]. We especially pay attention to the scheme [5] since it can correctly classify unknown users.…”

mentioning

confidence: 99%

Two-stage SPIT detection scheme with betweenness centrality and social trust

2015

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Detecting SPIT (Spam over Internet Telephony) is an urgent demand in voice communication services. In this paper, we propose a twostage SPIT detection scheme using BC (Betweenness Centrality) and social trust to decrease misdetection of a call from low-frequent users as SPIT. BC indicates user's centrality in the entire network and the BC against legitimate users gradually increases with time even if users seldom call. We first use BC to identify a call request from a low-frequent user then judge the call legitimacy by using social trust. By the computer simulation, we show that our scheme improves the detection accuracy. Keywords: SPIT detection, VoIP, security, social trust Classification: Internet , vol. 21, no. 2, pp. 188-197, 2013, vol. 21, no. 2, pp. 188-197, . DOI:10.2197 U. Brandes, "A faster algorithm for betweenness centrality,"

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Caller-REP: Detecting unwanted calls with caller social strength

Cited by 31 publications

References 6 publications

SpamDetector: Detecting spam callers in Voice over Internet Protocol with graph anomalies

SpamDetector: Detecting spam callers in Voice over Internet Protocol with graph anomalies

Unsupervised Clustering-based SPITters Detection Scheme

Two-stage SPIT detection scheme with betweenness centrality and social trust

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