Behavior-based approach to detect spam over IP telephony attacks

Chikha, Randa Jabeur Ben; Abbes, Tarek; Chikha, Wassim Ben; Bouhoula, Adel

doi:10.1007/s10207-015-0281-1

Cited by 14 publications

(9 citation statements)

References 23 publications

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“…Figure 5 presents a comparison of ML algorithm performance and indicates that Logistic Regression and Neural Network were the most powerful and efficient algorithms in applying the naive approach. Chikha et al [24] used several ML algorithms (Naive Bayes, Bagging, AdaBoost, and Multilayer Perceptron) to detect SPIT. However, the authors reported only the resulting AUC for each algorithm and did not examine the individual algorithms in detail.…”

Section: ) Construction Of a Naive ML Modelmentioning

confidence: 99%

“…The main aim of the current study was to apply algorithms to aid in detecting SPIT users. Many studies which investigated similar methods substantiated their research on artificially generated data ( [19], [23], [24], [36], [41]) rather than analysis of real-world data ( [26], [27]). The current study applied only real-world data collected from multiple sources (VoIP providers, honeypots, mobile devices and online data services) [21], [22], [33].…”

Section: Introductionmentioning

confidence: 99%

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Efficient Detection of Spam Over Internet Telephony by Machine Learning Algorithms

et al. 2022

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Recent trends show a growing interest in VoIP services and indicate that guaranteeing security in VoIP services and preventing hacker communities from attacking telecommunication solutions is a challenging task. Spam over Internet Telephony (SPIT) is a type of attack which is a significant detriment to the user's experience. A number of techniques have been produced to detect SPIT calls. We reviewed these techniques and have proposed a new approach for quick, efficient and highly accurate detection of SPIT calls using neural networks and novel call parameters. The performance of this system was compared to other state-ofart machine learning algorithms on a real-world dataset, which has been published online and is publicly available. The results of the study demonstrated that new parameters may help improve the effectiveness and accuracy of applied machine learning algorithms. The study explored the entire process of designing a SPIT detection algorithm, including data collection and processing, defining suitable parameters, and final evaluation of machine learning models.

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Section: ) Construction Of a Naive ML Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient Detection of Spam Over Internet Telephony by Machine Learning Algorithms

et al. 2022

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“…A semi-supervised clustering is used on call parameters to mark each call as spam or legitimate [29]. Whereas, [30] compares ten machine learning methods to classify callers into legitimate and spammers. However, machine learning requires supervised training data and high processing.…”

Section: Related Workmentioning

confidence: 99%

Detecting Nuisance Calls over Internet Telephony Using Caller Reputation

et al. 2021

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Internet telephony permit callers to manage self-asserted profiles without any subscription contract nor identification proof. These cost-free services have attracted many telemarketers and spammers who generate unsolicited nuisance calls. Upon detection, they simply rejoin the network with a new identity to continue their malicious activities. Nuisance calls are highly disruptive when compared to email and social spam. They not only include annoying telemarketing calls but also contain scam and voice phishing which involves security risk for subscribers. Therefore, it remains a major challenge for Internet telephony providers to detect and avoid nuisance calls efficiently. In this paper, we present a new approach that uses caller reputation to detect different kinds of nuisance calls generated in the network. The reputation is computed in a hybrid manner by extracting information from call data records and using recommendations from reliable communicating participants. The behavior of the caller is assessed by extracting call features such as call-rate, call duration, and call density. Long term and short term reputations are computed to quickly detect the changing behavior of callers. Furthermore, our approach involves an efficient mechanism to combat whitewashing attacks performed by malicious callers to continue generating nuisance calls in the network. We conduct simulations to compute the performance of our proposed model. The experiments conclude that the proposed reputation model is an effective method to detect different types of nuisance calls while avoiding false detection of legitimate calls.

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“…Several solutions have been proposed for blocking the SPIT caller. These solutions operate independently as the standalone systems, and can be grouped into several classes: black-list or white-list based systems [12][13][14], systems analyzing the social behavior and reputation of the caller [15][16][17][18][19][20][21][22][23][24], authenticating the caller by challenging him in the form of CAPTCHA and Turing test [25][26][27]8], imposing extra cost on the caller if he is flagged as unwanted [28] by recipients of call, systems processing speech content [29][30][31][32], analyzing the linguistics from the speech streams [33,34], and statistical systems that analyzes the flow of packets during the call setup phase [35,36] or analyze caller's behavior from the logged CDRs [37,38]. A single standalone SPIT solution can also be employed by combining many individual systems in the form of a collaborative multistage system [28,[39][40][41][42].…”

Section: Standalone Anti-spit Systemsmentioning

confidence: 99%