VoIP Profiler: Profiling Voice over IP User Communication Behavior

Batthalla, Sainath; Swarnkar, Mayank; Hubballi, Neminath; Natu, Maitreya

doi:10.1109/ares.2016.19

Cited by 5 publications

(7 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A call will be allowed if the current call pattern is similar to the previous call pattern of the caller, otherwise, the call is blocked. 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%

“…Call parameters : Weights on the edges of CallGraph are assigned based on the calling behavior of VoIP users. There are many call parameters to detect behavior of a VoIP user but it is important to select those parameters which can differentiate between nonspam and spam VoIP users. In our case, we consider the following three parameters which can be leveraged to differentiate both kinds of users: Successful call rate : This is the ratio of successful calls made by a caller i to the total number of calls attempted by that caller to another user j .…”

Section: Proposed Workmentioning

confidence: 99%

“…Features like call frequency and average call duration are used to compare previous call pattern with the current one. 11,22,23 Sengar et al 24 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%

“…The idea of using anomalies is motivated by the fact that spam users usually have different interaction patterns like higher calling frequency, large number of short duration calls, etc., which when used to derive appropriate weights on edges help differentiate these users. Similar features have been used in few other methods for detecting spam callers. However, these methods use only average values of features and hence they fail to work if a determined spam user maintains these averages by artificial calls.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Swarnkar

Hubballi

2018

Security and Privacy

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Prior Workmentioning

confidence: 99%

Section: Proposed Workmentioning

confidence: 99%

Section: Prior Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Swarnkar

Hubballi

2018

Security and Privacy

Self Cite

View full text Add to dashboard Cite

show abstract

“…Literature [7] discusses the method of playing WMA music files online based on ASP technology and Access database and analyzes the key technologies in detail. Literature [8] proposed a grade analysis method to detect abnormal signals in music. Literature [9] proposed a new hybrid method to determine abnormal data based on GARCH, K-means, neural network, and other anomaly detection methods.…”

Section: Introductionmentioning

confidence: 99%

Voice Anomaly Detection and Music Website Teaching Design for 5G Internet of Things

Chen

2022

Scientific Programming

View full text Add to dashboard Cite

Information technology has brought tremendous changes to many industries. 5G and Internet of Things technology have also driven the development of the music industry. Network communication has replaced the traditional method and has become a brand-new method of music communication. An online music instructional teaching website based on Internet of Things is designed, the basic functions of the website are introduced, and a voice anomaly detection system was designed from the landing test, search function test, song display, administrator maintenance, and management of songs. The basic functions of the website were tested in four aspects. According to the needs of music websites, a voice anomaly detection system is designed. The system includes two aspects, namely, an abnormal sound classification system and a microphone array abnormal sound detection system; two music rhythm algorithms are proposed, based on international music detection. The music data of the wrong retrieval platform was tested on two different music algorithms, and it was found that the two different detection methods have different results for different types of music, and the Bossa-Brazil music type has the highest error rate. In order to detect the accuracy of the detection of the abnormal voice system, a comparison experiment of three different voice detection systems was set up under the condition of no noise and noise, to test their experimental accuracy. The music teaching system designed in this paper has the highest accuracy, which can meet most of the requirements and improve the efficiency of music detection. In the design of music teaching, because the music website has rich functions and various products, it improves the effect of online teaching and the efficiency of teaching design. Instructional design is applied to online learning to improve the dissemination of music and art, and high-quality pronunciation is also one of the important contents.

show abstract

Efficient Detection of Spam Over Internet Telephony by Machine Learning Algorithms

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

VoIP Profiler: Profiling Voice over IP User Communication Behavior

Cited by 5 publications

References 16 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

Voice Anomaly Detection and Music Website Teaching Design for 5G Internet of Things

Efficient Detection of Spam Over Internet Telephony by Machine Learning Algorithms

Contact Info

Product

Resources

About