Leveraging Missing Values in Call Detail Record Using Naïve Bayes for Fraud Analysis

This article describes how in less than two decades, internet in mobile phones has grown from a curiosity to an essential element of modern life. Although, this mind-boggling growth has no doubt facilitated international commerce, trade, and travel, it is also being used in the planning and coordination of criminal activities. These types of attacks are often referred to as socio-technical attacks. These attacks are targeted at these sensitive points to society or national security and may have a devastating impact. Often, organized, sponsored, and trained groups are involved to disguise the intelligence system, deployed for the detection of such attacks. Prior detection of such attacks may reduce its impact. In this article, the authors have developed an efficient model to detect malicious node in huge and complex corpus of data associated with call detail record (CDR). This model analyses CDRs to identify covert nodes operating within society for malicious intent.

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Leveraging Missing Values in Call Detail Record Using Naïve Bayes for Fraud Analysis

Cited by 4 publications

References 12 publications

AI-JasCon: An Artificial Intelligent Containerization System for Bayesian Fraud Determination in Complex Networks

AI-JasCon: An Artificial Intelligent Containerization System for Bayesian Fraud Determination in Complex Networks

Fraud detection system: A survey

Models for the Detection of Malicious Intent People in Society

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