AN ARTIFICIAL INTELLIGENCE AND MACHINE LEARNINGBASEDSYSTEMAND METHOD WITH IMPROVED SECURITY OF NODES DATAINABLOCKCHAIN NETWORK FOR ATM WITHDRAWALS

Abstract

The present invention relates to an advanced AI and ML-based system and method for improving the security of nodes in a blockchain network for ATM withdrawal transactions. The system integrates hardware and software components to enhance the security of ATM withdrawals through a combination of blockchain technology, cryptographic key management, biometric authentication, and AI-based threat detection. The blockchain network, implemented using Hyperledger Fabric, ensures a permissioned and decentralized model for secure logging and validation of ATM transactions. The system includes a Hardware Security Module (HSM) for secure key management, a biometric scanner for user identity verification, and a Secure Processing Unit (SPU) to manage transaction authorization. The AI & ML-based security module uses a Recurrent Neural Network (RNN) trained on historical transaction data to detect fraudulent activities in real time. The method includes user authentication, anomaly detection, blockchain validation, multi-factor authorization, and secure transaction processing, thereby providing a robust solution for securing ATM withdrawals.

Specification

Description:[013] The following sections of this article will provide various embodiments of the current invention with references to the accompanying drawings, whereby the reference numbers utilised in the picture correspond to like elements throughout the description. However, this invention is not limited to the embodiment described here and may be embodied in several other ways. Instead, the embodiment is included to ensure that this disclosure is extensive and complete and that individuals of ordinary skill in the art are properly informed of the extent of the invention. Numerical values and ranges are given for many parts of the implementations discussed in the following thorough discussion. These numbers and ranges are merely to be used as examples and are not meant to restrict the claims' applicability. A variety of materials are also recognised as fitting for certain aspects of the implementations. These materials should only be used as examples and are not meant to restrict the application of the innovation.
[014] Referring now to the drawings, these are illustrated in FIG. 1, the system architecture comprises the following components:
Blockchain Network: A decentralized network that records and validates ATM withdrawal transactions. The blockchain is implemented using Hyperledger Fabric, with nodes distributed across multiple locations to ensure redundancy and data integrity. The use of Hyperledger Fabric ensures a permissioned blockchain model, which provides enhanced privacy and access control compared to public blockchain networks. Each node is equipped with a consensus mechanism to validate transactions, ensuring the integrity of the data across the entire network.
[015] In accordance with another embodiment of the present invention, Hardware Security Module (HSM): The HSM is integrated with each ATM to securely manage cryptographic keys. It ensures that all cryptographic operations (such as key generation, encryption, and decryption) are conducted in a secure, tamper-resistant environment. The HSM also facilitates secure communication between the ATM and blockchain nodes, ensuring that sensitive information is never exposed during data transmission. Additionally, the HSM performs periodic integrity checks on stored keys, providing alerts in case of any discrepancies.
[016] In accordance with another embodiment of the present invention, AI & ML-Based Security Module: This module contains a deep learning model trained on historical ATM transaction data to detect abnormal or fraudulent activity. It uses a Recurrent Neural Network (RNN) architecture to recognize unusual withdrawal patterns in real time. The model is continuously updated using federated learning, which allows the system to adapt without compromising data privacy. By aggregating model updates from multiple ATMs, the system ensures that new fraud patterns are quickly recognized across the network.
[017] In accordance with another embodiment of the present invention, Biometric Scanner: A fingerprint scanner is implemented in the ATM to provide an additional layer of security. The scanner captures the user's fingerprint and compares it against a stored template for identity verification. The biometric data is processed locally to ensure that sensitive information is not transmitted over the network. This reduces the risk of data breaches and ensures compliance with privacy regulations. The fingerprint scanner is designed to operate even in adverse environmental conditions, ensuring reliability.
Secure Processing Unit: The Secure Processing Unit (SPU) is a specialized microprocessor embedded in the ATM to handle secure data processing. The SPU communicates with the blockchain, biometric scanner, and HSM to verify user identity, validate withdrawal requests, and authorize transactions. The SPU is equipped with a secure boot mechanism, ensuring that only trusted software is executed. It also performs regular firmware integrity checks to detect and prevent any unauthorized modifications. The SPU communicates with the blockchain, biometric scanner, and HSM to verify user identity, validate withdrawal requests, and authorize transactions as shown in figure 2.
[018] In accordance with another embodiment of the present invention, the ATM withdrawal security method consists of the following steps:
Step 1: User Authentication
The user initiates a withdrawal request by inserting their ATM card and providing a fingerprint scan. The fingerprint is verified locally through the biometric scanner, which sends the authentication data to the Secure Processing Unit (SPU).
Step 2: Anomaly Detection
The AI & ML-Based Security Module, integrated within the SPU, analyzes the withdrawal request using the RNN model to detect anomalies. The module evaluates parameters such as the user's withdrawal history, location, and transaction time to determine whether the request is legitimate.
Step 3: Blockchain Validation
Upon successful anomaly detection, the withdrawal transaction is recorded on the blockchain network. The transaction data is hashed and stored in a blockchain node, providing a secure and immutable log of the request.
Step 4: Multi-Factor Authorization
The system employs multi-factor authorization by cross-checking the fingerprint data, ATM PIN, and withdrawal request with the blockchain. The Hardware Security Module (HSM) ensures secure management of cryptographic keys, verifying that all transaction details are accurate and authorized.
Step 5: Secure Processing and Approval
The Secure Processing Unit (SPU) communicates with the blockchain to validate the user's request. Once verified, the SPU authorizes the withdrawal, and the ATM dispenses cash to the user.
[019] The fingerprint scanner is integrated with the ATM system to ensure accurate and secure user identification. The scanner is directly linked to the Secure Processing Unit for rapid verification. The HSM is responsible for generating, storing, and managing encryption keys used for transaction processing. It also facilitates the signing of blockchain transactions to ensure data integrity and authenticity. Secure Processing Unit (SPU) serves as the core of the hardware implementation, managing secure user interactions, executing AI-based threat detection, and interacting with the blockchain nodes to complete ATM withdrawal transactions.
[020] The machine learning module consists of an RNN model trained on historical transaction data to identify suspicious activities. This model is updated periodically with new transaction data to enhance its accuracy in detecting fraudulent transactions. The SPU leverages this ML model to perform real-time threat analysis before authorizing any ATM transaction. The invention successfully integrates AI, ML, blockchain, and hardware modules to enhance the security of ATM transactions, ensuring secure, fast, and reliable withdrawal processes while protecting sensitive user data from potential threats and unauthorized access.
[021] The benefits and advantages that the present invention may offer have been discussed above with reference to particular embodiments. These benefits and advantages are not to be interpreted as critical, necessary, or essential features of any or all of the embodiments, nor are they to be read as any elements or constraints that might contribute to their occurring or becoming more evident.
[022] Although specific embodiments have been used to describe the current invention, it should be recognized that these embodiments are merely illustrative and that the invention is not limited to them. The aforementioned embodiments are open to numerous alterations, additions, and improvements. These adaptations, changes, additions, and enhancements are considered to be within the purview of the invention.
, Claims:1. An advanced AI and ML-based system for improving the security of nodes in a blockchain network for ATM withdrawals, comprising:
a blockchain network implemented using Hyperledger Fabric to record and validate ATM withdrawal transactions with a permissioned model;
a Hardware Security Module (HSM) integrated with each ATM to manage cryptographic keys, perform secure operations, and enable secure communication with blockchain nodes;
a biometric scanner for capturing and verifying user fingerprints for identity verification;
a Secure Processing Unit (SPU) embedded in each ATM to handle secure data processing and authorize transactions;
an AI & ML-based security module using a Recurrent Neural Network (RNN) to detect anomalies and fraudulent transactions in real time.
2. The system of claim 1, wherein the blockchain network comprises multiple distributed nodes utilizing a consensus mechanism to validate transactions and ensure data integrity.
3. The system of claim 1, wherein the Hardware Security Module (HSM) performs periodic integrity checks on stored cryptographic keys and facilitates secure communication between ATMs and blockchain nodes.
4. The system of claim 1, wherein the biometric scanner captures the user's fingerprint and processes it locally to ensure privacy and reduce the risk of data breaches.
5. The system of claim 1, wherein the Secure Processing Unit (SPU) communicates with the blockchain, biometric scanner, and HSM to validate withdrawal requests and is equipped with a secure boot mechanism and firmware integrity checks.
6. The system of claim 1, wherein the AI & ML-based security module is trained on historical transaction data using federated learning to adapt to new fraud patterns while maintaining user privacy.
7. A method for securing ATM withdrawal transactions, comprising:
a) Authenticating the user by verifying the fingerprint through a biometric scanner and providing authentication data to a Secure Processing Unit (SPU);
b) Detecting anomalies in the withdrawal request using an AI & ML-based security module integrated within the SPU;
c) Recording and validating the withdrawal transaction on a blockchain network using a consensus mechanism;
d) Employing multi-factor authorization by cross-checking the fingerprint data, ATM PIN, and transaction request with the blockchain;
e) Authorizing the withdrawal through the SPU and dispensing cash to the user upon successful validation.
8. The method of claim 7, wherein the AI & ML-based security module utilizes a Recurrent Neural Network (RNN) to analyze the withdrawal request based on the user's withdrawal history, location, and transaction time.
9. The method of claim 7, wherein the blockchain network ensures immutability and secure logging of each withdrawal request by hashing transaction data and storing it on a blockchain node.
10. The method of claim 7, wherein the Hardware Security Module (HSM) generates, stores, and manages encryption keys used for processing ATM withdrawal transactions and facilitates the signing of blockchain transactions.

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