Business firms and households sometimes seek for extra-funding to fulfill certain needs. The demand which arises from the need of extra funds is fulfilled by the credit market. Banks and others financial lending institutions are the key players in this market (Gaigaliene and Cesnys, 2018). Loan is one of the most important products of most financial institutions. All financial lenders try to find effective business strategies for persuading customers to apply for loans. However, there are some borrowers who default in loan payments (Begum and Deniz, 2019). During a loan term, default may occur when the borrower fails to make required payments. Therefore, an assessment of a borrower's default risk over time is essential to enable timely risk management. Credit officers determine whether borrowers can fulfill their requirements using manually analysis of borrower's credit history. In the last decade, this trend has changed over time with technological advancement (Rehman, 2017).In recent years, financial lending institutions are using automated loan default models as credit risk scoring tools when granting loans to potential borrowers (Bao et al., 2019). Machine Learning (ML) algorithms have been applied to assess the credit risk of borrowers in financial lending institutions (Djeundj and Crook, 2018). Reliable models for credit risks play an important role in loss control and revenue maximization (Luo and Nie, 2016). Earlier research treated loan default prediction as a binary classification problem, where a loan is classified as either creditworthy or non-creditworthy (Rosenberg and Gleit, 1994). Linear Discriminant Analysis (LDA) and logistic regression (LR) are two most popular tools for constructing credit scoring models (Wiginton, 1980). Subsequently, other classification algorithm such as, Artificial neural networks (ANN) Gulsoy and Kulluk (2019) support vector machines (SVM) Alaka et al. (2018), decision trees (DT) Liu et al. (2015), and Bayesian classifier (BC) Carta et al. (2020), have been used to estimate borrowers' probability of default. Recently, time-to-default modeling has attracted increasing research interest (Dirick et al., 2017). Time-to-default data fall into the category of lifetime data in general, which is commonly analyzed by survival analysis (SA) (Malekipirbazari and Aksakalli, 2015). In loan prediction, two types of errors inevitably lead to inefficiency in prediction

IntroductionThe VPN technology has adapted to access the Enterprises Intranet. Now VPN security is a big issue for almost every organization in order to provide protection for system infrastructure. The Truth is that no one assertions full evidence on security scheme since the internet usage is growing exponentially across the globe. Particularly, according toRama and Anup (2020), revealed that in COVID-19 Pandemic, Internet usage has been improved by up to 90% due to the culture Work from-home by nearly every business. VPN technology offers a fashion of protecting data information being carried over the Internet, by granting remote users to set up a secure virtual private 'tunnel' to get into an internal network, gain access to available resources, data information and communications through an unsafe network such as the Internet cyberspace (United States Patent Burns, 2018). The review Giris and Vishal (2021) stated that, many businesses protected themselves from the cyberspace by means of firewalls and VPN encryption techniques. VPN considered to be an effective and efficient mechanism to transport traffic on an unsafe network. It comprises a union of encrypting, authentication and tunnel. VPNs has been established and proved to be that effective to substitute previous system of lease lines to make private network in a business enterprise (Natalia, 2021 andMazlan et al., 2010). VPNs usually oppressed by business enterprises to link central office/Head-office with subdivision office, main office for distant employees or roaming users, business collaborator sites and remote network users of their join network. There are numerous unlike types of VPNs available. The most commonly types of VPN used in nowadays are as follows: (a) Point-to-Point Tunnelling Protocol VPN (PPTP VPN, PPTP VPN) is an easy method for VPN and can also be called as Dial-up VPN. It is software-built VPN that uses an existing internet network to create VPN connection. Using this existing connection, a remote client will be able to link to remote network because of secure VPN tunnel created by the software between these remote endpoints