Conference Papers
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Item Portable Executable Header Based Ransomware Detection using Power Iteration and Artificial Neural Network(Institute of Electrical and Electronics Engineers Inc., 2023) Singh, M.P.; Karkhur, Y.In the present world, the dependency on different devices connected to the internet is increasing at a rapid rate day by day. Devices like smart watches, mobile phones, personal computers, etc., are part of our day-to-day life. We rely on them for most of our daily tasks. Since these devices are frequently used, they contain users' personal information and other essential data. More and more people use the internet due to emerging technology and intelligent devices, increasing the risk of misusing confidential information and other user-specific crucial data. With the development of cryptocurrency, Ransomware is one of the emerging attacks that prevent authentic users from accessing systems, resources, or data and enables adversaries to control access to such information. This paper presents an Artificial Neural Network (ANN) based model that uses the 'Power Iteration' method and Portable Executable (PE) Headers to detect various types of Ransomware. We analyze the performance of the proposed model by experimenting with a dataset created using the PE files collected from multiple sources and demonstrate its better detection capability. . © 2023 IEEE.Item Estimation of Implied Volatility for Ethereum Options Using Numerical Approximation Methods(Springer Science and Business Media Deutschland GmbH, 2023) Sapna, S.; Mohan, B.R.This study demonstrates the use of numerical approximation techniques like Newton-Raphson Method, Bisection Method, Brent Method, and Secant Method to estimate the market implied volatility for short-dated Ethereum options with 21-day maturity, obtained from Deribit Crypto Options and Futures Exchange. The numerical approximation techniques are compared based on their convergence and time taken for execution. It is found that Newton-Raphson Method converges faster and performs computation in the least time in comparison to the other methods under consideration. This study further focuses on the determination of implied volatility structure for short maturity Ethereum options. The results show that the implied volatility assumes a deep smile far from the day of expiry and as we approach the expiry date, the volatility smile broadens. To the best of our knowledge, this is the first work to use approximation techniques to estimate the implied volatility for Ethereum options. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Transaction fee forecasting in post EIP-1559 Ethereum using 1-D Convolutional Neural Network(Institute of Electrical and Electronics Engineers Inc., 2023) Kallurkar, H.S.; Chandavarkar, B.R.Cryptocurrencies have established their identity as a healthy alternative to the maintenance of digital assets. Their applications include low-cost money transfers and yield farming. Ethereum is a blockchain that provides the functionality of doing more than a transaction regarding cryptocurrency. Ether is the default cryptocurrency of Ethereum, which is issued to the miners after the successful completion of the consensus mechanism to avoid fraudulent miners gaining profits. Transactions in Ether require that the user should include what is called a 'fee' besides the amount that is sent by the user. The EIP-1559 (Ethereum Improvement Proposals) upgrade to the Ethereum protocol has substantially changed how the transaction fee is calculated. Since this transaction data can be considered time-series data, many prior approaches have been proposed to forecast such a transaction fee using suitable methods effectively. One-dimensional Convolutional Neural Networks have recently been successfully applied to time-series forecasting problems, showing promising results. This paper proposes a univariate 1-D CNN for an effective forecast of transaction fees in the new Ethereum protocol. Furthermore, this paper also compares the proposed method with existing standard approaches, and the results show the superior performance of simple 1-dimensional convolutional neural networks over existing hybrid models. © 2023 IEEE.Item Mitigation of Trust-Related Issues in Cryptocurrency Payments Using Machine Learning: A Review(Springer Science and Business Media Deutschland GmbH, 2023) Shridhar Kallurkar, H.; Chandavarkar, B.R.Cryptocurrency is a type of fiat currency in digital form, unlike the physical money that is commonly used for daily purposes. A blockchain is a base on which a cryptocurrency operates, i.e., it is a growing list of records of transactions happening in a particular cryptocurrency. Trust in a cryptocurrency comes into the picture when two stakeholders, virtually unknown to each other, are confident or not about each other’s reliability in the context of whether each one is getting the service they intended to get. Trust in cryptocurrency can exist between any two stakeholders, such as users, merchants, government agencies, and blockchain technology, who are a part of cryptocurrency transactions. Furthermore, direct or indirect involvement of different stakeholders in cryptocurrency transactions results in issues such as lack of transparency, ease of use, regulations of the government, privacy, security of users, etc. Traditional approaches to anomaly detection in blockchain primarily use machine learning methods because they can infer patterns from historical data to give decent accuracy on test data. This survey presents trust in a cryptocurrency payment and its issues. Furthermore, it also shows the mitigation approaches which use machine learning techniques to address these issues. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.