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Detection of intrusion plays an important part in data protection. Intruders will carry out attacks from a compromised user account without being identified. The key technology is the effective detection of sundry threats inside the network. However, process automation is experiencing expanded use of information communication systems, due to high versatility of interoperability and ease off 34 administration. Traditional knowledge technology intrusion detection systems are not completely tailored to process automation. The combined use of fuzziness-based and RNN-IDS is therefore highly suited to high-precision classification, and its efficiency is better compared to that of conventional machine learning approaches. This model increases the accuracy of intrusion detection using Machine Learning Methodologies and fuzziness has been used to identify various categories of hazards, and a machine learning approach has been used to prevent intrusions. As a result, the hypothesis of security breaches is often observed by tracking system audit reports for suspicious trends of system use, and access controls for granting or limiting the degree of access to the network are often established as the result of an improvement in the detection accuracy of intrusions which is extremely effective.

In this cashless economy era, Information and Communication Technology (ICT) plays a vital role in making payments using various payment modes. The mobile wallet app is an innovative technology for avoiding the usage of physical cash. The mobile wallet records all kinds of transactions with a clear payment reference and makes it accountable for tax payments. There are countless reasons for using mobile wallets which makes service providers confused and leads them to offer unattractive features in the wallet apps making the offer as a failure. This paper attempts to collect the data from the mobile wallet users and provide a clear understanding of the reasons for using mobile wallets. Secondly, the customer preferences towards Google Pay and PayTm are analyzed in detail with primary data collected from mobile wallet users to suggest a model for improving the business. This research was conducted to understand the customer's inclination towards the use of mobile wallets.

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