arXiv subjects

Yash Mishra

Publications and source records attributed to Yash Mishra.

3 recordsLinked to original sources

arXiv2026

An Adaptive Neuro-Fuzzy Blockchain-AI Framework for Secure and Intelligent FinTech Transactions

Financial systems have a growing reliance on computer-based and distributed systems, making FinTech systems vulnerable to advanced and quickly emerging cyber-criminal threats. Traditional security systems and fixed machine learning systems cannot identify more intricate fraud schemes whilst also addressing real-time performance and trust demands. This paper presented an Adaptive Neuro-Fuzzy Blockchain-AI Framework (ANFB-AI) to achieve security in FinTech transactions by detecting threats using intelligent and decentralized algorithms. The framework combines both an immutable, transparent and tamper resistant layer of a permissioned blockchain to maintain the immutability, transparency and resistance to tampering of transactions, and an adaptive neuro-fuzzy learning model to learn the presence of uncertainty and behavioural drift in fraud activities. An explicit mathematical model is created to explain the transaction integrity, adaptive threat classification, and unified risk based decision-making. The proposed framework uses Proof-of-Authority consensus to overcome low-latency validation of transactions and scalable real-time financial services. Massive simulations are performed in normal, moderate, and high-fraud conditions with the use of realistic financial and cryptocurrency transactions. The experimental evidence proves that ANFB-AI is always more accurate and precise than recent state-of-the-art algorithms and costs much less in terms of transaction confirmation time, propagation delay of blocks and end-to end latency. ANFB-AI performance supports the appropriateness of adaptive neuro-fuzzy intelligence to blockchain-based FinTech security.

cs.CR↗

arXiv2026

Are Language Models Sensitive to Morally Irrelevant Distractors?

With the rapid uptake of large language models (LLMs) across high-stakes settings, it is becoming increasingly important to ensure that LLMs behave in ways that align with human values. Existing moral benchmarks for this purpose often prompt LLMs with value statements, moral scenarios, or psychological questionnaires, with the implicit underlying assumption that LLMs report somewhat stable moral preferences. However, moral psychology research has shown that even human moral judgements are sensitive to morally irrelevant situational factors such as the smell of cinnamon rolls or the level of ambient noise, thereby challenging moral theories which assume that human moral judgements are stable. Here we draw inspiration from this "situationist" view of moral psychology to evaluate whether LLMs exhibit similar cognitive moral biases. We curate a novel multimodal dataset of 60 "moral distractors" from existing psychological datasets of emotionally-valenced images and narratives, which have no moral relevance to the situation presented. After injecting these distractors into existing moral benchmarks, we find that moral distractors can shift the moral judgements of LLMs by over 30% even in unambiguous scenarios, highlighting the instability of LLMs' moral judgements and the need for more contextual approaches to AI alignment.

cs.CL↗

arXiv2024

K-Nearest Neighbor Classification over Semantically Secure Encrypted Relational Data

Data mining has various real-time applications in fields such as finance telecommunications, biology, and government. Classification is a primary task in data mining. With the rise of cloud computing, users can outsource and access their data from anywhere, offloading data and it is processing to the cloud. However, in public cloud environments while data is often encrypted, the cloud service provider typically controls the encryption keys, meaning they can potentially access the data at any time. This situation makes traditional privacy-preserving classification systems inadequate. The recommended protocol ensures data privacy, protects user queries, and conceals access patterns. Given that encrypted data on the cloud cannot be directly mined, we focus on a secure k nearest neighbor classification algorithm for encrypted, outsourced data. This approach maintains the privacy of user queries and data access patterns while allowing effective data mining operations to be conducted securely in the cloud. With cloud computing, particularly in public cloud environments, the encryption of data necessitates advanced methods like secure k nearest neighbor algorithms to ensure privacy and functionality in data mining. This innovation protects sensitive information and user privacy, addressing the challenges posed by traditional systems where cloud providers control encryption keys.

cs.CR↗