A SYSTEM FOR EVALUATING ENHANCED CYBER SECURITY USING ARTIFICIAL INTELLIGENCE AND BLOCKCHAIN FOR ONLINE PAYMENTS

Abstract

The present invention discloses a system for evaluating and enhancing cybersecurity in online payments by integrating Artificial Intelligence (AI) and blockchain technology. The system comprises an AI-based evaluation module for real-time monitoring, predictive analytics, and fraud detection, a blockchain integration module for secure and immutable transaction recording, a multi-factor authentication (MFA) mechanism for verifying user identity, and a real-time threat response module utilizing reinforcement learning to adapt to emerging threats. Additionally, blockchain smart contracts automate critical aspects of payment validation to ensure secure transaction processing. The proposed system provides enhanced security, real-time threat detection, transparency, scalability, and adaptive security measures, making it a robust solution for safeguarding online payment transactions.

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 comprises: AI-Based Evaluation Module: This module consists of advanced machine learning algorithms, including supervised, unsupervised, and reinforcement learning models, trained on vast datasets of legitimate and fraudulent payment transactions. The AI-based evaluation module is capable of recognizing complex patterns and detecting anomalies in real-time, allowing for immediate threat identification and mitigation before executing the transaction. This module includes:
Predictive Analytics Engine: Using deep neural networks and natural language processing (NLP), this engine predicts potential vulnerabilities, suspicious behaviors, and fraud-prone activities. The predictive analytics engine continuously learns from new transaction data to improve its ability to foresee security threats.
Fraud Detection Subsystem: This subsystem utilizes deep learning models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to detect unusual transaction activities. It relies on predefined fraud models, historical data, and adaptive algorithms to continuously refine its accuracy in identifying fraudulent patterns.
[015] In accordance with another embodiment of the present invention, The blockchain layer provides a secure and distributed ledger that records each transaction in a decentralized manner. The blockchain integration module ensures data integrity and enhances trust through the following features:
Immutability: Once a transaction is validated by the AI-based evaluation module, it is permanently recorded on the blockchain. The use of cryptographic hashing ensures that no unauthorized changes can be made to the transaction data.
Consensus Mechanism: A distributed consensus mechanism, such as Proof of Stake (PoS) or Practical Byzantine Fault Tolerance (PBFT), ensures that each payment activity is validated by multiple nodes. This reduces the possibility of a single point of failure and provides resilience against tampering or unauthorized modifications.
[016] In accordance with another embodiment of the present invention, The system integrates a multi-layered MFA mechanism that employs a combination of biometric authentication (such as fingerprint or facial recognition), SMS-based codes, and device-specific tokens. The AI module analyzes MFA events for any unusual or suspicious patterns, which are then flagged and subjected to further scrutiny. This approach ensures that only authorized users can initiate and complete transactions, enhancing overall system security.
[017] In accordance with another embodiment of the present invention, Real-Time Threat Response Module utilizes reinforcement learning to dynamically adapt the security mechanisms based on detected threats. The reinforcement learning model is designed to autonomously adjust the response protocols, such as increasing verification steps or temporarily blocking transactions, to mitigate emerging threats. Once a suspicious transaction is detected, the system will either block it or require additional verification from the user, thereby reducing the risk of fraudulent activities as shown in figure 2.
[018] In accordance with another embodiment of the present invention, the blockchain smart contracts are implemented to automate crucial aspects of the payment validation process. These smart contracts are programmed to execute predefined actions, such as releasing the payment, only when AI algorithms confirm that the transaction meets the security criteria. This ensures the integrity of the payment process, minimizes human intervention, and provides an additional layer of security.
[019] By leveraging AI for threat detection and blockchain for secure recording, the system provides a multi-layer security approach that significantly reduces the risk of online fraud. The combination of machine learning and blockchain ensures a proactive defense against evolving cyber threats. AI instructions provide real-time fraud detection and analysis, enabling instant response to suspicious activities and protecting the user's assets. The reinforcement learning model allows the system to learn from each incident and improve its threat response capabilities. Blockchain technology provides a transparent, decentralized ledger of all payment activities, which can be independently verified by users, financial institutions, and regulators. This enhances the trustworthiness of the online payment ecosystem and reduces the likelihood of disputes. The integration of AI-driven reinforcement learning ensures that the system continuously learns from new threats and adapts its response mechanisms accordingly. This adaptive security framework is essential for addressing the ever-evolving landscape of cybersecurity risks. The system is designed to handle a high volume of online transactions while maintaining accuracy and efficiency. The distributed nature of the blockchain module ensures scalability, while the AI modules optimize threat detection and resource allocation.
[020] This invention provides a robust, secure, and adaptive solution to evaluate and enhance the cybersecurity of online payments, leveraging the combined strengths of Artificial Intelligence and blockchain technology. The AI provides intelligent, real-time analysis for fraud detection, while blockchain ensures secure, immutable, and transparent records of every transaction.
[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. A system for evaluating and enhancing cybersecurity in online payments, comprising:
an AI-based evaluation module configured to monitor, evaluate, and detect suspicious payment activity in real-time;
a blockchain integration module configured to store validated payment records in an immutable and distributed ledger;
a multi-factor authentication mechanism that includes biometric and token-based verification, wherein said mechanism is integrated with AI to ensure enhanced security;
a real-time threat response module configured to adapt the system's security measures using reinforcement learning to mitigate new and evolving threats; and
blockchain smart contracts that facilitate secure and automated execution of payment processes upon AI-based verification of transaction validity.
2. The system as claimed in claim 1, wherein the AI-based evaluation module utilizes machine learning models trained on historical payment data for detecting anomalies and fraud.
3. The system as claimed in claim 1, wherein the blockchain integration module employs a consensus mechanism to validate each payment activity, thereby preventing unauthorized modifications.
4. The system as claimed in claim 1, wherein the real-time threat response module can autonomously initiate additional verification steps or block transactions in response to detected threats.
5. The system as claimed in claim 1, wherein the multi-factor authentication mechanism includes device fingerprinting to evaluate the authenticity of the user device used for payment.
6. The system as claimed in claim 1, wherein the AI-based evaluation module incorporates an ensemble of machine learning models to improve the accuracy of anomaly detection.
7. The system as claimed in claim 1, wherein the blockchain integration module uses a hybrid blockchain approach to optimize scalability and transaction throughput.
8. The system as claimed in claim 1, wherein the real-time threat response module integrates a feedback loop to continuously update AI models based on detected threats.
9. The system as claimed in claim 1, wherein the blockchain smart contracts are configured to trigger alerts to users and administrators in the event of a suspicious transaction.
10. The system as claimed in claim 1, wherein the multi-factor authentication mechanism includes geolocation-based analysis to verify the legitimacy of user transactions.

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