Design a fast Non‐Technical Loss fraud detector for smart grid

IEEE Trans.Inform.Forensic Secur.

Liang

2020

Self Cite

Integrated with cutting-edge equipment and technologies, smart grid takes prominent advantages over traditional power systems. However, hardware and software techniques also bring smart grid numerous security concerns, especially various cyberattacks. Malicious users can launch cyberattacks to tamper with smart meters anytime and anywhere, mainly for the purpose of stealing electricity. This makes electricity theft much easier to commit and more difficult to detect. Researchers have devised many approaches to identify malicious users. However, these approaches suffer from either poor accuracy or expensive cost of deploying monitoring devices. This paper aims to locate malicious users using a limited number of monitoring devices (called inspectors) within the shortest detection time. Before inspectors conduct any inspection, suspicions that users steal electricity are comprehensively assessed, mainly through analyzing prior records of electricity theft as well as deviations between the reported and predicted normal consumptions. On the basis of these suspicions, we further propose a suspicion assessment-based inspection (SAI) algorithm, in which the users with the highest suspicions will be first probed individually. Then, the other users will be probed by a binary tree-based inspection strategy. The binary tree is built according to users' suspicions. The inspection order of the nodes on the binary tree is also determined by the suspicions. The experiment results show that the SAI algorithm outperforms the existing methods.

Section: Related Workmentioning

confidence: 99%

SAI: A Suspicion Assessment-Based Inspection Algorithm to Detect Malicious Users in Smart Grid

Xia

IEEE Trans.Inform.Forensic Secur.

Liang

2020

Self Cite

“…Comparison based methods [11], [27]- [30], [48] are to record a correct value of energy usage and compare the reading of a meter with this value. We have proposed NFD [29] and FNFD [30], [48] which are comparison based methods. Both NFD and FNFD use an observer meter to record a correct value of energy usage for the purpose of comparison.…”

Section: Related Workmentioning

confidence: 99%

A novel detector to detect colluded non-technical loss frauds in smart grid

Han

2017

Computer Networks

Self Cite

A Non-Technical Loss (NTL) fraud occurs when a fraudster tampers with a smart meter so that the meter registers less electricity consumption than the actual consumed amount, and therefore the utility becomes the victim who suffers the corresponding economic loss. In the literature, many detection schemes have been proposed to detect NTL frauds. However, some NTL frauds are far more complicated than what the existing schemes expect. We recently discovered a new potential type of frauds, a variant of NTL frauds, called Colluded Non-Technical Loss (CNTL) frauds in the Smart Grid. In a CNTL fraud, multiple fraudsters can co-exist or collaborate to commit the fraud. Existing detection schemes cannot detect CNTL frauds since these methods do not consider the co-existing or collaborating fraudsters, and therefore cannot distinguish one from many fraudsters. In this paper, we propose a CNTL fraud detector to detect CNTL frauds. The proposed method can quickly detect a tampered meter based on recursive least squares. After identifying the tampered meter, the proposed scheme can detect different fraudsters using mathematical models. Our experiments show that our method is effective in detecting CNTL frauds.

“…The papers [52,53] propose NTL fraud detection (NFD) based on polynomial interpolation to model adversary's behaviors, and detect a tampered meter by comparing the difference between the results. The papers [54,55] propose a fast NFD (FNFD) method based on recursive least square to model an adversary's behaviors. The papers [56,57] study colluded NTL (CNTL) frauds in which multiple fraudsters can co-exist or collaborate to commit a fraud, and propose colluded NFD (CNFD) address the CNTL fraud problem.…”

Section: State-based Schemesmentioning

confidence: 99%

Difference-Comparison-based Malicious Meter Inspection in Neighborhood Area Networks in Smart Grid

Xia

Liang

et al. 2017

The Computer Journal

Self Cite

As the smart meters are vulnerable to physical attacks as well as cyber attacks, electricity theft in smart grids is much easier to commit and more difficult to detect than that in traditional power grids. In this paper, to facilitate the inspection of the malicious meters, a full and complete binary inspection tree whose leaves stand for smart meters is employed as a logical structure. We can logically configure an inspector (a meter for detection) at any node on the tree. By calculating the difference between the inspector's reading and the summation of the readings reported from the smart meters on the subtree of one node, as well as the difference between the total amount of stolen electricity on the subtrees of an internal node and its left child, we propose a difference-comparison-based inspection algorithm which allows the inspector to skip a large number of nodes on the tree and hence accelerates the detection speed of the malicious meters remarkably. Furthermore, for quickly identifying a complete set of malicious meters, we propose an adaptive reporting mechanism which adopts much shorter reporting periods during the inspection process. Analysis with proofs about the performance bounds of the proposed algorithm in terms of the number of inspection steps is provided. Simulations not only validate the theoretical analysis, but also show the superiority of the proposed algorithm over the existing works in terms of inspection steps, regardless of the ratio and the permutation of malicious meters.