An efficient multi-factor authentication scheme based CNNs for securing ATMs over cognitive-IoT

Abstract

Nowadays, the identity verification of banks’ clients at Automatic Teller Machines (ATMs) is a very critical task. Clients’ money, data, and crucial information need to be highly protected. The classical ATM verification method using a combination of credit card and password has a lot of drawbacks like Burglary, robbery, expiration, and even sudden loss. Recently, iris-based security plays a vital role in the success of the Cognitive Internet of Things (C-IoT)-based security framework. The iris biometric eliminates many security issues, especially in smart IoT-based applications, principally ATMs. However, integrating an efficient iris recognition system in critical IoT environments like ATMs may involve many complex scenarios. To address these issues, this article proposes a novel efficient full authentication system for ATMs based on a bank’s mobile application and a visible light environments-based iris recognition. It uses the deep Convolutional Neural Network (CNN) as a feature extractor, and a fully connected neural network (FCNN)—with Softmax layer—as a classifier. Chaotic encryption is also used to increase the security of iris template transmission over the internet. The study and evaluation of the effects of several kinds of noisy iris images, due to noise interference related to sensing IoT devices, bad acquisition of iris images by ATMs, and any other system attacks. Experimental results show highly competitive and satisfying results regards to accuracy of recognition rate and training time. The model has a low degradation of recognition accuracy rates in the case of using noisy iris images. Moreover, the proposed methodology has a relatively low training time, which is a useful parameter in a lot of critical IoT based applications, especially ATMs in banking systems.