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1

Unlocking the future of secure automatic machines : leveraging facereg with HRC & LBPH

Vijaywargiya Yamini, Mishra Mahak, Doohan Nitika Vats

Journal of Automation Mobile Robotics and Intelligent Systems

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2024

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Vol. 18, No. 1

67--73

EN

We propose a Computer Vision and Machine Learning equipped model that secures the ATM from fraudulent activities by leveraging the use of Haar cascade (HRC) and Local Binary Pattern Histogram (LBPH) classifier for face detection and recognition correspondingly, which in turn detect fraud by utilizing features, like PIN and face recognition, help to identify and authenticate the user by checking with the trained dataset and trigger real-time alert mail if the user turns out to be unauthorized also. It does not allow them to log in into the machine, which resolves the ATM security issue. this system is evaluated on the dataset of real-world ATM camera feeds, which shows an accuracy of 90%. It can effectively detect many frauds, including identity theft and unauthorized access which makes it even more reliable.

2

Rozpoznawanie urządzeń i wykrywanie nieautoryzowanego poboru energii elektrycznej na podstawie pomiaru napięcia

Kowalczuk Zdzisław, Grzegorek Marek

Pomiary Automatyka Robotyka

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2020

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R. 24, nr 4

5--9

PL

W pracy rozpatrywany jest problem wykrywania nieautoryzowanego poboru energii z sieci elektrycznej przez identyfikację podłączonych urządzeń. Estymacja stanu sieci, rozumianej jako zbiór podpiętych układów, wraz z ustaloną listą urządzeń dopuszczonych, pozwala określić, czy w danej chwili ma miejsce pobór nieautoryzowany. W celu wykrywania urządzeń, proponuje się wykorzystać prostą metodę opartą na analizie wysokoczęstotliwościowego szumu elektromagnetycznego (EMI) indukowanego w sygnale napięcia zasilającego. Rozwiązanie to pozwala na centralny pomiar, w jednym miejscu – bez konieczności instalacji czujników w licznych punktach potencjalnego poboru prądu. Bazując na danych pomiarowych sygnału EMI, zrealizowano symulator syntezujący dane przypominające rzeczywiste spektrogramy. Dzięki zastosowaniu symulatora możliwe jest uzyskanie informacji o stanie sieci w różnych konfiguracjach w celu przeprowadzenia procedury projektowania detektora z użyciem uczenia pod nadzorem, co również jest prezentowane w pracy.

EN

The paper examines the problem of detecting unauthorized energy consumption from the electric network by identifying connected devices. The estimation of the network condition, understood as a set of connected systems, together with a set list of approved devices, allows to determine whether an unauthorized consumption is taking place at a given moment. In order to detect devices, it is proposed to use a simple method based on the analysis of high-frequency electromagnetic noise (EMI) induced in the supply voltage signal. This solution allows for central measurement in one place – without the need to install sensors in numerous points of potential current consumption. Based on the measurement data of the EMI signal, a simulator was implemented that synthesized data resembling actual spectrograms. Thanks to the use of the simulator, it is possible to obtain information about the state of the network in various configurations in order to carry out the detector design procedure using supervised learning, which is also presented in the paper.

3

An Online Stream Monitoring Algorithm for Fraud Detection in the Transport of Goods

Białoń Paweł M.

Journal of Telecommunications and Information Technology

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2020

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nr 4

79--85

EN

The process of monitoring vehicles used in road transports plays an important role in detecting fraud committed by drivers. Algorithm designers face a number of challenges, including large number of vehicles monitored, demands related to online calculations, and ability to easily explain fraud alarms triggered to supervisors who make final decisions about actions to be taken. In this paper, we propose rather general, lightweight stream, online heuristics. The vehicle’s position is periodically controlled by a GNSS device. The algorithm detects potential illegal activities along the route between the origin and the destination. Anomalies in the vehicle’s trajectory are detected, based on a multi-resolution analysis of the economy of routes. The economy metric is easily understood and verifiable by controllers. The solution is also capable of identifying clearly suspicious trajectories that popular geofencing approaches would overlook. The scale on which the solution may be adopted is obtained thanks to the stream – like nature of the algorithm: essentially, the resources used do not increase along with the size of the input stream (the number of GNSS frames generated for the vehicle). An experiment illustrating the algorithm’s viability is presented as well.

4

Ensemble-based Method of Fraud Detection at Self-checkouts in Retail

Vitynskyi P., Tkachenko R., Izonin I.

ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes

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2019

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Vol. 8, No 2

3--8

EN

The authors consider the problem of fraud detection at self-checkouts in retail in condition of unbalanced data set. A new ensemble-based method is proposed for its effective solution. The developed method involves two main steps: application of the preprocessing procedures and the Random Forest algorithm. The step-by-step implementation of the preprocessing stage involves the sequential execution of such procedures over the input data: scaling by maximal element in a column with row-wise scaling by Euclidean norm, weighting by correlation and applying polynomial extension. For polynomial extension Ito decomposition of the second degree is used. The simulation of the method was carried out on real data. Evaluating performance was based on the use of cost matrix. The experimental comparison of the effectiveness of the developed ensemble-based method with a number of existing (simples and ensembles) demonstrates the best performance of the developed method. Experimental studies of changing the parameters of the Random Forest both for the basic algorithm and for the developed method demonstrate a significant improvement of the investigated efficiency measures of the latter. It is the result of all steps of the preprocessing stage of the developed method use.