Automated Teller Machine

In recent years, Automated Teller Machines (ATMs) are witnessed as the most widely used gadgets which required personal identification numbers (PINs) for authentication along with ATM cards. But PINs are suspected of many threats like a man-in-the-middle attack, spoofing, and eavesdropping, etc., which can affect secure and private information. In this paper, we present a novel secure authentication system for ATMs. In this paper, we replace ATM card with facial expression authentication and makes ATMs intelligent enough to recognize users and give them access to their accounts. Replacement of cards will solve the current problem of skimming data from cards with the use of skimmers.

Authentication based on biometric provides various advantages in ATMs (Automated Teller Machine) systems. The weakness of existing authentication scheme in ATM is the usage of PIN (Personal Identification Number) numbers as password. Because PIN numbers are easily traceable and misused. In order to achieve security and to overcome these illegal activities, our proposed system is developed to provide better security to the ATMs. Here, the PIN numbers are replaced with biometric security. The main objective of the work is to eliminate the use of ATM cards completely and to ensure better security. In this proposal, we provide the idea of using Aadhaar number as user ID and fingerprint as password. After biometric verification, the user will be allowed to proceed with the transaction. In case of three successive wrong attempts, the account will be blocked.

Providing security to bank customers is the prime responsibility of banking institutions. These institutions upgrade their services to provide convenience-based banking exposure. Automatic Teller Machines (ATMs) assist the bank's customers to operate independently. ATMs verify the authenticity of the user through a 4-digit personal identification number (PIN) only. The State Bank of India (SBI) has initiated a double security check system to minimize the probabilities of banking frauds through ATMs. SBI customers must provide a one-time password (OTP) along with a PIN to complete the cash withdrawal transaction through the SBI ATM. The objective of the study is to analyze the impact of financial security enhancement in ATMs cash transactions through one-time password on the probabilities of banking frauds through ATMs. The study has used primary data collected through a questionnaire from 422 respondents. The study resulted that one-time password has enhanced the financial security function of ATMs and now the probabilities of ATM frauds has been reduced substantially.

With the increase of automated teller machine (ATM) frauds, new authentication mechanisms are developed to overcome security problems of personal identification numbers (PIN). Those mechanisms are usually judged on speed, security, and memorability in comparison with traditional PIN entry systems. It remains unclear, however, what appropriate values for PIN-based ATM authentication actually are. We conducted a field study and two smaller follow-up studies on real-world ATM use, in order to provide both a better understanding of PIN-based ATM authentication, and on how alternative authentication methods can be compared and evaluated. Our results show that there is a big influence of contextual factors on security and performance in PIN-based ATM use. Such factors include distractions, physical hindrance, trust relationships, and memorability. From these findings, we draw several implications for the design of alternative ATM authentication systems, such as resilience to distraction and social compatibility.

The increasing reliance on Automated Teller Machines (ATMs) has highlighted the urgent need for advanced authentication mechanisms to safeguard user transactions against fraud and unauthorized access. However, traditional methods such as Personal Identification Numbers (PINs) and passwords remain vulnerable to attacks, while unimodal biometric systems face challenges of inter-class variance, environmental interference, and non-universality. Specifically, single-modality approaches and conventional ATM cameras fall short in capturing reliable biometric features under varying conditions, while the effectiveness of bimodal approaches in such environments has not been adequately investigated. Therefore, this study developed a bimodal biometric authentication system integrating face and iris recognition with Gray Level Co-Occurrence Matrix (GLCM) for enhanced ATM security. The system leverages GLCM for powerful texture feature extraction from both modalities, capturing intricate spatial relationships that are difficult to spoof. The extracted feature vectors were used to train Support Vector Machines (SVM) with a Radial Basis Function (RBF) kernel as classifiers for both face and iris recognition. The final authentication decision was made using Boolean OR rule fusion. The system achieved a remarkable accuracy of 98.2%, with a False Acceptance Rate (FAR) of 1.8% and a False Rejection Rate (FRR) of 1.2%. These results demonstrably outperformed comparable unimodal systems and existing biometric ATMs, validating the proposed framework as a highly secure and efficient solution for financial authentication systems.

Nowadays we are experiencing a radical increase in skimming in the Automated Teller Machine (ATM) systems. So, actuation in advancement and security of the ATM machines is required.An automated teller machine (ATM) is an electronic telecommunications device that helps customers of banking departments in transactions and transfer of money in their accounts. The customer enters their unique personal identification number (PIN), i.e. stored in the chip of the card. Due to an increase in the installation of ATM and the number of ATM cardholders, the number of cases of fraudulence has also increased radically. The advancement in technology has resulted in an increase in various skimming activities.So, developments are incorporated in the existing systems to make it more secure, convenient and reliable. The employed secured system must have high speed and must be durable. The presented design is unique because of biometric scanners such as Iris scanner and the two-way check with fingerprint scanner makes it more reliable. The iris scanner being the primary security check lets the system access the further steps for transaction. Fingerprint scanner embedded in the ATM card acts as the secondary security check for the system. The transaction procedure is successful only if the input data by the card holder matches with the database. It consumes less energy that makes it suitable for use. The suggested modified system is pragmatic moreover economical when correlating to the alternative existing classification and affirmation processes of ATMs.

비밀번호를 입력하는 과정에서 가장 큰 보안 위협은 비밀번호를 훔쳐보는 것이다. 비밀번호 훔쳐보기는 비밀번호를 입력하는 과정을 옆에서 지켜보고 비밀번호를 획득하려고 하는 행위로서 비밀번호를 획득하는 가장 전통적인 방법이며 강력한 보안 위협이다. 본 논문에서는 인지심리학에 기초한 비밀번호 훔쳐보기로부터 안전한 기술인 역동 인증 체계(DAS)라 불리는 비밀번호 입력 기술을 제안하였다. 그리고 제안한 역동 인증 체계의 비밀번호 훔쳐보기에 대한 안전성을 직관적인 관점, 훔쳐보기 실험, 이론적인 분석으로 구분하여 검증하였다. 비밀번호 훔쳐보기로부터 안정성이 입증된 역동 인증 체계를 내장시킨 비밀번호 입력기를 설계하여 구현하였다. 구현한 비밀번호 입력기는 일반 패스워드 입력방식 보다 훔쳐보기 공격자의 비밀번호 획득 확률을 현저하게 낮출 수 있으므로 은행에서 운영하는 금융자동화기기에 적용 및 운영되기에 적합한 것으로 평가를 받아 금융기관에서 도입하여 활용되고 있다. When entering the PIN(personal identification number), the greatest security threat is shoulder surfing attack. Shoulder surfing attack is watching the PIN being entered from over the shoulder to obtain the number, and it is the most common and at the same time the most powerful security threat of stealing the PIN. In this paper, a psychology based PINpad technology referred to as DAS(Dynamic Authentication System) that safeguards from shoulder surfing attack was proposed. Also, safety of the proposed DAS from shoulder surfing attack was tested and verified through intuitive viewpoint, shoulder surfing test, and theoretical analysis. Then, a PINpad with an internal DAS that was certified for its safety from shoulder surfing attack was designed and produced. Because the designed PINpad significantly decreases the chances for shoulder surfing attackers being able to steal the PIN when compared to the ordinary PINpad, it was determined to be suitable for use at ATM(automated teller machine)s operated by banks and therefore has been introduced and is being used by many financial institutions.

A Fingerprint-based Authentication Framework for ATM Machines The security of the transactions on the Automated Teller Machine (ATM) has been raising many concerns in some parts of the world. These concerns are due to series of limitations in the existing designs of the various service points. The current use of Personal Identification Number (PIN) for ATM user’s verification and identification has been susceptible to unauthorized access, misplacement, forgetfulness, card swallowing among others; thereby narrowing down the machine’s acceptability and patronage. In this paper, a framework for fingerprint authenticated ATM application is presented. The framework comprises of modules for fingerprint enrolment, database and verification. The verification module comprises of fingerprint enhancement, feature extraction and matching sub-modules, which all rely on suitable mathematical models to function. There is also platform for financial transaction including withdrawal and balance enquiries. The implementation was carried out using Windows 7 as operational platform while C# and Microsoft SQL server served as the frontend and backend engines respectively. Tests conducted on the evaluation of the application using False Rejection Rate (FRR), False Acceptance Rate (FAR) and Average Matching Time (AMT) show the adequacy and the suitability of the proposed framework for ATM user verification and authentication.

Nowadays Automated Teller Machines (ATMs) are widely used by people. People are dependent upon ATMs to conveniently meet their day-to-day needs. As it is an important factor there comes security. ATMs are electronic machines that are operated by customers to deposit or withdraw cash from banks. It is observed that the number of crimes related to ATMs is increased hence there is a need to provide better security to ATM machines. There are different technologies that are used to provide security to ATM machine which includes – RFID technology, fingerprint, face recognition, iris scan, OTP, reference number, random keypad, etc. In a traditional ATM system card and PIN numbers are used for authentication, where security plays a big concern, such as losing cards, stolen pin numbers, card cloning, shoulder surfing, fake keyboard, skimming, etc. In order to overcome these issues, this paper discusses various systems where ATM cards and pins are replaced by biometrics and how they made ATMs more secure.

As technology progresses, traditional authentication methods like Personal Identification Numbers (PINs) and passwords are becoming more prone to security breaches. To address this issue, biometric-based authentication systems are being seen as a reliable solution to boost security in different areas, such as Automated Teller Machines (ATMs). This study delves into the deployment, advantages, obstacles, and future potential of a biometric-based ATM system. By examining current research, real-life examples, and technological innovations, the aim of this paper is to shed light on the efficiency and viability of biometric authentication in ATM systems. Key Words: Enhanced Security, Authentication, Efficiency, Reliability

Automated Teller Machine (ATM) security system currently still uses magnetic cards and static PIN as its security system, which create many security holes. This security hole in many cases caused many bank customers to lose money mysteriously. In this paper a two-factor authentication system which uses ATM card and dynamic PIN is proposed to overcome this security hole. In this paper, a prototype of an ATM and m-banking application were built. The ATM prototype uses several components such as the Raspberry Pi 3B, smart card, smart card reader / writer, keypad number and LCD monitor. Dynamic PINs are generated using the CSPRNG-SHA1-MWC random number generator. In developing the prototypes, the framework used in this study is based on mobile applications and cloud computing. To evaluate the quality of the prototype, we performed qualitative and quantitative tests. Qualitatively we tested the prototype using a questionnaire using 165 sample respondents to provide an opinion about the safety and comfort of our prototype and quantitatively we measured the prototype to find out the level of randomness of the generated PIN and the QoS of the designed prototype.

Two-factor authentication techniques using combination of magnetic cards and personal identification numbers (PINs) are widely used in many applications including automatic teller machines and point of sales. Similar to other valuable personal possessions, cards can be easily stolen by pickpockets. Furthermore, recent security reports show that magnetic cards can be easily duplicated using fake card readers and PINs can be obtained by shoulder surfing legitimate users' PIN entry processes. With this combination, criminals can easily break into users' accounts which represents a great threat. In this paper, we propose a new PIN entry scheme which is resistant against shoulder-surfing attacks conducted by shoulder-surfers with normal cognitive capabilities. Additionally, this scheme offers a relatively good level of security when the shoulder-surfer can record the entire login procedure for one or two times with a video device. Mathematical analysis of the proposed scheme is also presented.

The growth in electronic transactions has resulted in a greater demand for fast and accurate user identification and authentication. Access codes for buildings, banks accounts and computer systems often use personal identification numbers (PIN's) for identification and security clearances. Conventional method of identification based on possession of ID cards or exclusive knowledge like a social security number or a password are not all together reliable. An embedded fingerprint biometric authentication scheme for automated teller machine (ATM) banking systems is proposed in this paper. In this scheme, a fingerprint biometric technique is fused with the ATM for person authentication to ameliorate the security level.

This safety alert aims to raise public awareness regarding increasing incidents of ATM-related crimes, including card skimming, shoulder surfing, and physical theft. Criminals are employing advanced tactics such as hidden cameras, fake keypads, and remote data capture to steal personal banking information and commit fraud. Additionally, physical attacks near ATM machines, especially during nighttime or in poorly lit areas, have also been reported. The alert emphasizes the importance of vigilance while using ATM machines and outlines safety measures such as covering the keypad during PIN entry, avoiding isolated machines, inspecting the ATM for suspicious devices, and being aware of surroundings. Financial institutions are advised to enhance surveillance, improve lighting, and educate customers on secure ATM practices. Proactive measures by both users and institutions are crucial in mitigating risks and ensuring secure banking transactions. The purpose of this project is to enhance the safety and security of Automated Teller Machine (ATM) users by identifying risks, raising awareness, and implementing preventative measures against ATM- related crimes. With the rise in fraudulent activities such as skimming, card trapping, PIN theft, and physical assaults, there is a critical need to educate the public and improve security infrastructure around ATM usage. Keywords: Survelience Camera, ATm Safety, Theif Protection, GPS location alert, Siren alert,

DNA evidence is nowadays used for the investigation of a wide range of crimes. Once reserved mostly for violent cases such as rape and murder, biological material recovery is not only restricted to such crime scenes anymore. As DNA technology is getting cheaper and its results faster, there has been a growing interest in using DNA to help solving volume crimes, mostly property crimes. In this work, an analysis of more than 4200 samples of biological material recovered from more than 1000 cases of property crime offenses processed by the Brazilian Federal Police Forensic Genetics laboratory is described. Most of the property crime offenses included: (1) Automated Teller Machine (ATM) thefts, skimming or Personal Identification Number (PIN) capturing scams, (2) post office burglaries or armed robberies, (3) Federal government buildings burglaries. Success rate at DNA recovering and STR typing showed great variability, mostly due the nature of biological source, but an average of 52% of samples presented usable DNA and in 44% of the cases at least one genetic profile reached the minimal criteria for insertion in CODIS. Results obtained in this work showed what types of evidence are usually collected in property crimes and which ones provide the best results for DNA typing. These results can be used to better guide crime scene evidence collection practices in property offenses, making it more efficient and cost effective.