The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey <s> III. THE STATE OF THE ART <s> Due to the dramatic increase of fraud which results in loss of billions of dollars worldwide each year, several modern techniques in detecting fraud are continually developed and applied to many business fields. Fraud detection involves monitoring the behavior of populations of users in order to estimate, detect, or avoid undesirable behavior. Undesirable behavior is a broad term including delinquency, fraud, intrusion, and account defaulting. This paper presents a survey of current techniques used in credit card fraud detection, telecommunication fraud detection, and computer intrusion detection. The goal of this paper is to provide a comprehensive review of different techniques to detect frauds. <s> BIB001 </s> The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey <s> III. THE STATE OF THE ART <s> Global energy generation and delivery systems are transitioning to a new computerized "smart grid". One of the principle components of the smart grid is an advanced metering infrastructure (AMI). AMI replaces the analog meters with computerized systems that report usage over digital communication interfaces, e.g., phone lines. However, with this infrastructure comes new risk. In this paper, we consider adversary means of defrauding the electrical grid by manipulating AMI systems. We document the methods adversaries will use to attempt to manipulate energy usage data, and validate the viability of these attacks by performing penetration testing on commodity devices. Through these activities, we demonstrate that not only is theft still possible in AMI systems, but that current AMI devices introduce a myriad of new vectors for achieving it. <s> BIB002 </s> The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey <s> III. THE STATE OF THE ART <s> Utilities experience significant revenue fatalities due to Non-Technical Losses (NTLs). These losses are principally caused by fraud activities deliberately performed by the consumers. NTLs lead to a series of additional losses, including damage to grid infrastructure and reduction of grid reliability. This paper presents arigorous review of technologies, methodologies and active approaches aimed at improving with emphasis in the accurate estimation and reduction of NTLs. Additionally, the sources of NTLs along with the possible impacts of NTLs on the economy is also focused. <s> BIB003 </s> The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey <s> III. THE STATE OF THE ART <s> Power system is a highly interconnected network which delivers electric power to the electricity users. Sustaining the secure and reliable delivery of electric power requires continuous monitoring of the system. To process the large volumes of data obtained from the measuring devices, it is essential to investigate effective data enhancement techniques. In this paper, a comprehensive study on the applications of data mining in power systems is presented. Data visualization, clustering, outliers detection and classification are investigated as four major areas of data mining and related works in power systems which utilize each of these methods are presented in an organized and structured fashion. <s> BIB004 </s> The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey <s> III. THE STATE OF THE ART <s> With the proliferation of smart grid research, the Advanced Metering Infrastructure (AMI) has become the first ubiquitous and fixed computing platform. However, due to the unique characteristics of AMI, such as complex network structure, resource-constrained smart meter, and privacy-sensitive data, it is an especially challenging issue to make AMI secure. Energy theft is one of the most important concerns related to the smart grid implementation. It is estimated that utility companies lose more than $25 billion every year due to energy theft around the world. To address this challenge, in this paper, we discuss the background of AMI and identify major security requirements that AMI should meet. Specifically, an attack tree based threat model is first presented to illustrate the energy-theft behaviors in AMI. Then, we summarize the current AMI energy-theft detection schemes into three categories, i.e., classification-based, state estimation-based, and game theory-based ones, and make extensive comparisons and discussions on them. In order to provide a deep understanding of security vulnerabilities and solutions in AMI and shed light on future research directions, we also explore some open challenges and potential solutions for energy-theft detection. <s> BIB005
NTL detection can be treated as a special case of fraud detection, for which a general survey is provided in  and BIB001 . It highlights expert systems and machine learning as key methods to detect fraudulent behavior in applications such as credit card fraud, computer intrusion and telecommunications fraud. This section is focused on an overview of the existing AI methods for detecting NTLs. For other surveys of the past efforts in the field, readers are referred to BIB003 and BIB004 . Overviews of possible methods to manipulate a smart metering infrastructure are provided in BIB005 and BIB002 .