Content-Based Detection and Prevention of Spam over IP Telephony - System Design, Prototype and First Results

Lentzen, Dirk; Grutzek, Gary; Knospe, Heiko; Pörschmann, Christoph

doi:10.1109/icc.2011.5963108

Cited by 23 publications

(12 citation statements)

References 12 publications

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“…Session initiation protocol (SIP) and real‐time protocol have been widely used session establishment between the caller and the callee, and exchange of voice signals between caller and the callee. The content‐based approaches either analyse content of SIP signalling messages or analyse real‐time protocol contents in a real and a non‐real time . The speech processing requires sophisticated system and signal processing resources for analysing the speech content in real time, and also requires system resources for storing updated voice data sample for pattern matching.…”

Section: Spit Mitigation Schemesmentioning

confidence: 99%

“…Several approaches have been designed for combating the SPIT callers in a telephone network. These approaches can be grouped into following categories: (i) a content‐based approach that processes the speech stream flowing between caller and the callee on fly and block callers if stream is found to be containing unwanted message ; (ii) a list‐based approach that maintains a database of blacked, white, and under observation users ; (iii) a turing and CAPTCHA test‐based approaches that asks caller to solve a specific challenge ; and (iv) a reputation‐based systems that computes reputation of the caller by obtaining feedback from the callee or uses information from the call detail records (CDRs) .…”

Section: Introductionmentioning

confidence: 99%

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Clustering VoIP caller for SPIT identification

Azad

Morla

Arshad

2016

Security Comm Networks

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The number of unsolicited and advertisement telephony calls over traditional and Internet telephony has rapidly increased over recent few years. Every year, the telecommunication regulators, law enforcement agencies and telecommunication operators receive a very large number of complaints against these unsolicited, unwanted calls. These unwanted calls not only bring financial loss to the users of the telephony but also annoy them with unwanted ringing alerts. Therefore, it is important for the operators to block telephony spammers at the edge of the network so to gain trust of their customers. In this paper, we propose a novel spam detection system by incorporating different social network features for combating unwanted callers at the edge of the network. To this extent the reputation of each caller is computed by processing call detailed records of user using three social network features that are the frequency of the calls between caller and the callee, the duration between caller and the callee and the number of outgoing partners associated with the caller. Once the reputation of the caller is computed, the caller is then places in a spam and non‐spam clusters using unsupervised machine learning. The performance of the proposed approach is evaluated using a synthetic dataset generated by simulating the social behaviour of the spammers and the non‐spammers. The evaluation results reveal that the proposed approach is highly effective in blocking spammer with 2% false positive rate under a large number of spammers. Moreover, the proposed approach does not require any change in the underlying VoIP network architecture, and also does not introduce any additional signalling delay in a call set‐up phase. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Spit Mitigation Schemesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Clustering VoIP caller for SPIT identification

Azad

Morla

Arshad

2016

Security Comm Networks

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“…Owing to the exponential growth of the spam calls, researchers have paved various ways for mitigating the SPIT calls using various techniques such as policy based, turing test based, reputation based, payment at risk, Completely Automated Public Turing test to tell the Computers and Humans Apart (CAPTCHA), machine learning algorithms based and content based over the period of 2006 to 2015 …”

Section: Related Workmentioning

confidence: 99%

Detection and prevention of spam over Internet telephony in Voice over Internet Protocol networks using Markov chain with incremental SVM

Vennila¹,

Manikandan²,

Suresh³

2016

Int J Communication

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This paper is mainly focused on the detection and prevention of the spammers such as telemarketing callers in Voice over Internet Protocol networks. The existing spam over Internet telephony (SPIT) detection mechanisms use call characteristics features such as reputation rate, call rejection rate, and user feedback that might increase the computation overhead and communication overhead. In this paper, a 2-tier model is proposed and implemented for detecting, preventing, and mitigating SPIT callers. The 2-tier model consists of a Markov chain (MC) and an incremental support vector machine (ISVM). The MC, in the first tier, detects the telemarketing callers, whereas the ISVM classifier in the second tier segregates these callers from the legitimate users. Also, the ISVM gradually mitigates the arrival of telemarketing callers at the recipient side. The performance of the proposed MC-ISVM classifier is tested using an experimental test bed, and the results show that the 2-tier model reports a promising blockage probability rate of 0.9 against spammers with a false positive rate of less than 0.01.

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“…The studies in [19][20] propose a content-based identification mechanism for VoIP SPAM. These studies rely on an analysis of the audio content of the VoIP call.…”

Section: Introductionmentioning

confidence: 99%

A VoIP SPAM reduction framework

Ebrahim

2013

2013 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)

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SPAM over Internet Telephony (SPIT) has become a critical problem especially because VoIP systems are widely deployed. Identifying VoIP SPAM is more problematic compared with e-mail SPAM mainly because of the different nature of each of them. In this paper, a new approach is introduced to reduce VoIP SPAM by ranking VoIP callers based on a set of parameters. These parameters include the caller's reputation, the feedbacks (if any) collected from the callee, and whether the callee responds or ignores the call. Additionally, a set of dummy directory numbers is introduced in the callee's domain. These numbers work as traps for certain kinds of VoIP spammers who try to guess the directory numbers in the victim's domain or overwhelm this domain by dialing random directory numbers. The VoIP caller is scored based on an identification process that uses these parameters and, hence, the caller's reputation is adjusted. This score is communicated to the callee for each incoming call and the final decision to accept/reject the call is taken by the callee himself. Additionally, this score can be utilized as an acceptance/rejection probability to automatically accept//reject the incoming call. A theoretical analysis is carried out for the proposed framework. Furthermore, the effectiveness of the proposed framework in dealing with VoIP SPAM is verified by simulation studies.

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Content-Based Detection and Prevention of Spam over IP Telephony - System Design, Prototype and First Results

Cited by 23 publications

References 12 publications

Clustering VoIP caller for SPIT identification

Clustering VoIP caller for SPIT identification

Detection and prevention of spam over Internet telephony in Voice over Internet Protocol networks using Markov chain with incremental SVM

A VoIP SPAM reduction framework

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