Call Behavioral Analysis to Thwart SPIT Attacks on VoIP Networks

Sengar, Hemant; Wang, Xinyuan; Nichols, Arthur S.

doi:10.1007/978-3-642-31909-9_31

Cited by 17 publications

(9 citation statements)

References 2 publications

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“…Features like call frequency and average call duration are used to compare previous call pattern with the current one , . Sengar et al use day, time of calling, and call duration parameters of users to create a pattern and use Mahalanobis distance between the current observation and pattern to detect anomalies.…”

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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“…These and more are proofs that simply using spam detectors in e-mails to solve spam in VoIP is not the right solution [11].…”

Section: Spitmentioning

confidence: 99%

Comparison of signaling and media approaches to detect VoIP SPIT attack

Gad

2018

2018 International Conference on Innovative Trends in Computer Engineering (ITCE)

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IP networks became the most dominant type of information networks nowadays. It provides a number of services and makes it easy for users to be connected. IP networks provide an efficient way with a large number of services compared to other ways of voice communication. This leads to the migration to make voice calls via IP networks. Despite the wide range of IP networks services, availability, and its capabilities, there still a large number of security threats that affect IP networks and for sure affecting other services based on it and voice is one of them. This paper discusses reasons of migration from making voice calls via IP networks and leaving legacy networks, requirements to be available in IP networks to support voice transport, and concentrating on SPIT attack and its detection methods. Experiments took place to compare the different approaches used to detect spam over VoIP networks.

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“…However, the current generation of telephone sets do not provide an option to give feedback of a call to service provider's system. Sengar et al [18] used callers calling behavior (day and time of calling, call duration etc.) to detect an onslaught of spam attack.…”

Section: Related Workmentioning

confidence: 99%

A Voice Spam Filter to Clean Subscribers’ Mailbox

Iranmanesh

Sengar

Wang

2013

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. With the growing popularity of VoIP and its large customer base, the incentives of telemarketers for voice spam has been increasing in the recent years. If the threat of voice spam remains unchecked, it could become a problem as serious as email spam today. Compared to email spam, voice spam will be much more obnoxious and time consuming nuisance for telephone subscribers to filter out. In this paper, we propose a contentbased approach to protect telephone subscribers voice mailboxes from voice spam. In particular, based on Dynamic Time Warping (DTW), we develop a speaker independent speech recognition system to make content comparison of speech messages. Using our system, the voice messages left on the media server by callers are matched against a set of spam filtering rules involving the study of call behavioral pattern and the analysis of message content. The uniqueness of our spam filtering approach lies in its independence on the generation of voice spam, regardless whether spammers play same spam content recorded in many different ways, such as human or machine generated voice, male or female voice, and different accents. We validate the efficacy of the proposed scheme through real experiments, and our experimental results show that it can effectively filter out spam from the subscribers' voice mailbox with 0.67% false positive rate and 8.33% false negative rate.

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Call Behavioral Analysis to Thwart SPIT Attacks on VoIP Networks

Cited by 17 publications

References 2 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

Comparison of signaling and media approaches to detect VoIP SPIT attack

A Voice Spam Filter to Clean Subscribers’ Mailbox

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