Fraud Detection in Telecommunications Industry: Bridging the Gap with Random Rough Subspace Based Neural Network Ensemble Method

Fraud has been very common in the society and it affects private enterprises as well as public entities. Telecommunication companies worldwide suffer from customers who use the provided services without paying. There are also different types of telecommunication fraud such as subscription fraud, clip on fraud, call forwarding, cloning fraud, roaming fraud and calling card fraud. Thus, detection and prevention of these frauds are the main targets of the telecommunication industry. This paper addresses the various techniques of detecting fraud, giving the limitations of each technique and proposes random rough subspace-based neural network ensemble method for effective fraud detection. Keywords: Fraud, Fraud detection, Random rough subspace, Neural network, Telecommunication

Development of a Method for Fraud Severity Measurement Based On Usage Profiling

The nature of fraud has changed from cloning fraud to subscription fraud, which makes specialized detection methodologies inadequate. Instead, the focus is on the detection methodologies that based on the subscriber’s calling activity or calling pattern, which can be roughly divided into two main categories: absolute analysis and differential analysis. Absolute analysis is capable at detecting the extremes of fraudulent activity. However, absolute analysis cannot trap all types of fraud especially usage behavior fraud related. An alternative approach to this problem is to perform a differential analysis against subscriber’s behavioral patterns. Certain behavioral patterns may be considered anomalous or abnormal for certain subscriber and potentially indicative of fraud but would be considered acceptable for another. In order to overcome the uncertainty in behavioral patterns, in this research, we propose to conduct the usage profiling at individual subscriber level. Usage profiling is a process of generating calling statistic based on predefined categories, which involve some form of aggregation from subscriber’s calling activity or CDR. Usage profiling process will generate two forms of usage profile : usage profile history (UPH) and current usage profile (CUP). In fraud detection system, comparison of these two types of usage profile will generate a measure known as fraud severity measurement. Implementation of the Hellinger distance for measuring a fraud severity, lack of detection accuracy as this method does not properly define the measurement scale as the Hellinger distance method will generate variation of values for fraud severity measurement. Therefore, it is very difficult to define the actual severity level of detected fraud. In this research, we propose a new method for measuring fraud severity. The advantages of the method are detection accuracy and detection speed. With the new method, the severity measurement scale is properly defined and the detection speed is faster than the Hellinger distanc

Comparing Data Mining Classification Algorithms in Detection of Simbox Fraud

Fraud detection in telecommunication industry has been a major challenge. Various fraud management systems are being used in the industry to detect and prevent increasingly sophisticated fraud activities. However, such systems are rule-based and require a continuous monitoring by subject matter experts. Once a fraudster changes its fraudulent behavior, a modification to the rules is required. Sometimes, the modification involves building a whole new set of rules from scratch, which is a toilsome task that may by repeated many times. In recent years, datamining techniques have gained popularity in fraud detection in telecommunication industry. Unlike rule based Simbox detection, data mining algorithms are able to detect fraud cases when there is no exact match with a predefined fraud pattern, this comes from the fuzziness and the statistical nature that is built into the data mining algorithms. To better understand the performance of data mining algorithms in fraud detection, this paper conducts comparisons among four major algorithms: Boosted Trees Classifier, Support Vector Machines, Logistic Classifier, and Neural Networks. Results of the work show that Boosted Trees and Logistic Classifiers performed the best among the four algorithms with a false-positive ratio less than 1%. Support Vector Machines performed almost like Boosted Trees and Logistic Classifier, but with a higher false-positive ratio of 8%. Neural Networks had an accuracy rate of 60% with a false positive ratio of 40%. The conclusion is that Boosted Trees and Support Vector Machines classifiers are among the better algorithms to be used in the Simbox fraud detections because of their high accuracy and low false-positive ratios

Consumer-facing technology fraud : economics, attack methods and potential solutions

The emerging use of modern technologies has not only benefited society but also attracted fraudsters and criminals to misuse the technology for financial benefits. Fraud over the Internet has increased dramatically, resulting in an annual loss of billions of dollars to customers and service providers worldwide. Much of such fraud directly impacts individuals, both in the case of browser-based and mobile-based Internet services, as well as when using traditional telephony services, either through landline phones or mobiles. It is important that users of the technology should be both informed of fraud, as well as protected from frauds through fraud detection and prevention systems. In this paper, we present the anatomy of frauds for different consumer-facing technologies from three broad perspectives - we discuss Internet, mobile and traditional telecommunication, from the perspectives of losses through frauds over the technology, fraud attack mechanisms and systems used for detecting and preventing frauds. The paper also provides recommendations for securing emerging technologies from fraud and attacks

Protecting Girls from Harassment and Fraudulent Calls: A Voice-to-Text Approach

The rise in harassment calls and fraud, particularly targeting girls, has resulted in adverse consequences including psychological distress and, in extreme cases, suicide. Furthermore, fraudulent calls urging individuals to click on malicious links have led to substantial financial losses. This study presents a comprehensive approach to address this challenge for the development of an innovative detection system. Additionally, we introduce a novel prototype that employs a voice-to-text approach to transcribe phone calls, utilizing Natural Language Processing (NLP) techniques as well as Machine Learning (ML) algorithms to identify harassment or fraud-related content. When a malicious call is detected, the system automatically alerts parents or guardians and the nearest police station to prevent tragic outcomes such as suicides among targeted girls and financial fraud. By focusing on both preventive measures and advanced detection, this integrated approach aims to promote a safer communication environment and a more inclusive society

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

Next Generation Machine Learning Based Real Time Fraud Detection

Define a real time monitoring architecture that can scale as the network of devices monitored grows. From the research work carried out and the knowledge about the nature of the business, it was possible to develop a clustering methodology over the data streams that allows to detect patterns on entities. The methodology used is based on the concept of micro-cluster, which is a structure that maintains a summary of the patterns detected on entities.In telecommunications there are several schemes to defraud the telecommunications companies causing great financial losses. We can considerer three major categories in telecom fraud based on who the fraudsters are targeting. These categories are: Traffic Pumping Schemes, Defraud Telecom Service Providers, Conducted Over the Telephone. Traffic Pumping Schemes use "access stimulation" techniques to boost traffic to a high cost destination, which then shares the revenue with the fraudster. Defraud Telecom Service Providers are the most complicated, and exploit telecom service providers using SIP trunking, regulatory loopholes, and more. Conducted Over the Telephone, also known as "Phone Fraud", this category covers all types of general fraud that are perpetrated over the telephone. Telecommunications fraud negatively impacts everyone, including good paying customers. The losses increase the companies operating costs. While telecom companies take every measure to stop the fraud and reduce their losses, the criminals continue their attacks on companies with perceived weaknesses. The telecom business is facing a serious hazard growing as fast as the industry itself. Communications Fraud Control Association (CFCA) stated that telecom fraud represented nearly $30 billion globally in 2017 cite{telecomengine}. Another problem is to stay on top of the game with effective anti-fraud technologies. The need to ensure a secure and trustable Internet of Things (IoT) network brings the challenge to continuously monitor massive volumes of machine data in streaming. Therefore a different approach is required in the scope of Fraud Detection, where detection engines need to detect risk situations in real time and be able to adapt themselves to evolving behavior patterns. Machine learning based online anomaly detection can support this new approach. For applications involving several data streams, the challenge of detecting anomalies has become harder over time, as data can dynamically evolve in subtle ways following changes in the underlying infrastructure. The goal of this paper is to research existing online anomaly detection algorithms to select a set of candidates in order to test them in Fraud Detection scenarios

Systems And Methods For Detecting Call Provenance From Call Audio

Various embodiments of the invention are detection systems and methods for detecting call provenance based on call audio. An exemplary embodiment of the detection system can comprise a characterization unit, a labeling unit, and an identification unit. The characterization unit can extract various characteristics of networks through which a call traversed, based on call audio. The labeling unit can be trained on prior call data and can identify one or more codecs used to encode the call, based on the call audio. The identification unit can utilize the characteristics of traversed networks and the identified codecs, and based on this information, the identification unit can provide a provenance fingerprint for the call. Based on the call provenance fingerprint, the detection system can identify, verify, or provide forensic information about a call audio source.Georgia Tech Research Corporatio

Consumer-facing technology fraud: Economics, attack methods and potential solutions

The emerging use of modern technologies has not only benefited society but also attracted fraudsters and criminals to misuse the technology for financial benefits. Fraud over the Internet has increased dramatically, resulting in an annual loss of billions of dollars to customers and service providers worldwide. Much of such fraud directly impacts individuals, both in the case of browser-based and mobile-based Internet services, as well as when using traditional telephony services, either through landline phones or mobiles. It is important that users of the technology should be both informed of fraud, as well as protected from frauds through fraud detection and prevention systems. In this paper, we present the anatomy of frauds for different consumer-facing technologies from three broad perspectives - we discuss Internet, mobile and traditional telecommunication, from the perspectives of losses through frauds over the technology, fraud attack mechanisms and systems used for detecting and preventing frauds. The paper also provides recommendations for securing emerging technologies from fraud and attacks.N/