Browsing by Author "Madwanna, Y."

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    Security Issues of Unified Payments Interface and Challenges: Case Study
    (Institute of Electrical and Electronics Engineers Inc., 2021) Madwanna, Y.; Khadse, M.; Chandavarkar, B.R.
    NPCI, which stands for National Payment Corporation of India, was the organisation behind the idea of UPI, a user-friendly system in which funds can be directly transferred from the bank account to the account using a mobile phone. UPI is based on the concept of 1 click 2-factor authentication. The first factor is the user's mobile phone itself, and the second factor is MPIN or bio-metrics. It is based on the IMPS(Immediate Payment Service), but there are considerable differences between both services, and we will observe it. With a foresight to make the Indian economy cashless, it helps people transfer funds in an immediate and real-time process. It has played a major role in the revolution of cashless transactions in India. Although significant UPI users are minor and much lesser compared to the Indian population, over 2.07 billion transactions per month have been made by UPI by October 2020, which makes it our essential part of our day-to-day life. This paper will discuss the working of UPI, how UPI is different from conventional cashless transaction methods. After that, we will discuss how the attacker can find the UPI's loopholes (here we reviewed UPI BHIM 1.0) and empty the victim's bank account. The attacker can make these attacks remotely, and these attacks can affect a single user to multiple users. We will also discuss how the attacker can achieve his/her goal using a malicious App. In the end, we will see how UPI BHIM 2.0 update was successful in covering this security loophole. © 2021 IEEE.
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    YARS-IDS: A Novel IDS for Multi-Class Classification
    (Institute of Electrical and Electronics Engineers Inc., 2023) Madwanna, Y.; Annappa, B.; Rashmi Adyapady, R.; Sneha, H.R.
    An Intrusion Detection System (IDS) is a defence system that provides safety and security against different threats and attacks, acting as a wall of defence against attackers. As internet usage increases, IDSs are becoming an essential part of day-to-day life. Various Machine Learning (ML) and Deep Learning (DL) based IDS are available, and the domain of IDS is still evolving and growing. Here this paper proposes two DL-based IDSs, first is a combination of LuNet and Bidirectional LSTM (Bi-LSTM) and other is a combination of Temporal Convolutional Network (TCN), CNN and Bi-LSTM. Such IDS must be fed with an efficient number of samples to keep them updated and accurate. The first model has been trained and tested against two benchmark datasets, NSL-KDD and UNSW-NB15. The second model has been trained and tested against the NSL-KDD dataset. To overcome the insufficient number of samples, the models have used a technique called Synthetic Minority Oversampling Technique (SMOTE). These models provided better experimental outcomes than traditional ML-based approaches and many DL approaches. They have better results in classification accuracy and, detection rate. The classification accuracy of the first model for UNSW-NB15 and NSL-KDD is 82.19% and 98.87% respectively. The classification accuracy of the second model for NSL-KDD is 98.8%. © 2023 IEEE.