Event Ticket Scalping Prevention System using Blockchain and AI

Event Ticket Scalping Prevention System using Blockchain and AI

Mr. Amol Rajpure

Department of Information Technology VPKBIET, Baramati Pune,India

Ismail Sayyed

Department of Information Technology VPKBIET, Baramati Pune,India

Kalyani Pachangane

Department of Information Technology VPKBIET , Baramati Pune India

Asim Sayyad

Department of Information Technology VPKBIET, Baramati Pune, India

Anagha Bhosale

Department of Information Technology VPKBIET, Baramati Pune, India

Abstract – Currently, tickets scams and counterfeits are the main issue within the ticket purchasing platforms. This creates an unfair pricing strategy and diminishes users condence in them. Traditional platforms have issues with transparency, cannot control unauthorized re-selling and excessive buying. Rexell uses a combination of blockchain technology and arti- cial intelligence for solving these problems. The tickets are generated from smart contracts in the form of non-fungible tokens (NFTs) for providing security and traceability. The AI anti-scalping component monitors users actions and informs about any potential scam activities, including use of bots and fast transactions. The implementation of the controlled resale process with permission from the organizers prevents price manipulation. The system strives to be convenient, safe, transparent and have fraud prevention algorithm. The experiment proves that the proposed approach is effective in preventing the scam attempts and increasing the integrity of the system.

Index TermsBlockchain, NFT, Articial Intelligence, Ticket Scalping, Smart Contracts, Fraud Detection

1. Introduction

   Currently, there is a great problem with ticket scalping and fraudulent activities during the purchasing process. High prices and low trust are consequences of such activities. The current platforms are opaque, do not protect from unauthorized re-selling and bots activity effectively. The current research presents Rexell decentralized platform for tickets distribu- tion, built with the help of blockchain technology and articial intelligence. Tickets are distributed via issuing non-fungible tokens with smart contract implementation. Such approach allows providing reliable and safe possession, uniqueness of each ticket and tracking its further transactions. Anti-scalping AI-module analyses users actions and denes suspicious ac- tivities such as use of bots and rapid ticket purchase. Regulated resale function requires the organizers conrmation in order to prevent price manipulation. Such an approach increases safety, transparency and efciency of the system.

2. Research Contributions

   The primary contributions of this research work are sum- marized as follows:

   • A blockchain-enabled event ticketing system is proposed to improve transparency and security in ticket distribution through NFT-based ownership management.

   • A regulated resale framework is introduced, allowing ticket transfers only after organizer approval, thereby reducing the possibility of unfair resale practices.

   • An intelligent monitoring mechanism is incorporated to identify unusual purchasing patterns and help mitigate ticket scalping activities.

   • Decentralized storage is utilized for managing ticket- related information, ensuring data integrity and accessi- bility.

   • A secure ownership verication process is implemented using blockchain transactions, enabling reliable tracking of ticket ownership throughout its lifecycle.

3. Literature Review

   Fraud and scalping issues are increasingly becoming sig- nicant with the rise of digital ticketing. In this context, many studies focus on possible solutions using blockchain and articial intelligence (AI).

   The blockchain technology was introduced by Nakamoto and provides decentralized transaction handling capabilities. Later on, Ethereum added smart contract capabilities that provide a completely automated and trustless environment for transactions. Blockchain technology is useful for improving transparency and integrity of the distributed systems.

   A wide range of researchers applies blockchain technologies in ticketing solutions. For example, Li et al. suggested a solution that preserves the security of ticket ownership and provides anti-duplicate mechanisms. On the other hand, Zhang proposed a solution based on a decentralized system design, which increases transparency; however, the solution has insuf- cient reselling control features and fraud detection capability. Many scholars also use AI technologies for fraud detection purposes. For instance, Chen et al. demonstrated the efciency of using machine learning to detect fraudulent patterns. More-

   over, Smith focused on behavior analysis in bot detection, while Kumar studied the anomaly-based transaction frauds.

   Nevertheless, most existing solutions rely only on one of two discussed approaches. Thus, blockchain ensures trans- parency but lacks smart fraud detection capability, whereas AI technologies can detect fraud patterns but do not guarantee the ticket ownerships security or traceability. The proposed solution Rexell addresses those limitations using blockchain- based ticketing together with NFT ticket issuance, identity verication, and intelligent fraud detection.

4. Problem Statement

   Online ticketing has introduced new issues concerning fair- ness, security, and ticket distribution. Scanning refers to the practice whereby individuals or software programs acquire multiple tickets and resell them at inated prices, denying real customers the opportunity to access tickets and fostering unfair competition.

   Conventional systems suffer from centralization, making it difcult to determine ownership and prevent fraud, such as ticket counterfeiting and unauthorized sales. Furthermore, these systems cannot authenticate user identities and curb the activities of bots.

   The blockchain technology works quite perfectly since it makes sure that all transactions are done securely. It is common that most people making use of the blockchain technology do not know how they can protect themselves from any kind of fraudulent activity. On the other hand, making use of AI to do certain activities lacks an effective mechanism for ensuring protection in terms of property ownership; hence, its implementation cannot be considered to be working well.

   Therefore, what we need is a system where property owner- ship will be guaranteed and, at the same time, where there is the capability of detecting fraudulent activities. The objective of this study is to design a blockchain-powered and transparent ticketing platform that would avoid reselling of tickets without any permission and identify fraudulent activities.

5. Methodology

   A fresh approach kicks off by mixing blockchain with articial intelligence, aiming to block those who hoard tickets then resell them for more. Safety in how tickets get handed out comes next, built right into the design.The proposed system provides a secure and efcient platform for ticket transactions. Blockchain technology ensures transparency and data integrity, while articial intelligence detects suspicious activities and potential fraud. By combining these technologies, the system enables fair, reliable, and secure ticket management.

   1. System Design Aproach

      The proposed system consists of three main components: a blockchain module, an AI-based detection module, and a user interface. The blockchain module secures ticket transactions, while the detection module identies suspicious activities. The user interface enables interaction between users and the system. These components work together to provide a secure and efcient ticketing platform.

   2. Blockchain-Based Ticket Lifecycle

      Tickets are taken care of using contracts and they have a certain way of working. They go through a steps:

      • Ticket Creation: Event organizers make tickets and they are, like special items that are unique these are made as NFTs by event organizers.

      • Ticket Purchase: when users buy tickets using the blockchain it is a way to do it.

      • Ticket Verication: when we check who really owns the ticket we use the blockchain to make sure.

      • Ticket Resale:when someone wants to sell their ticket this is also controlled by contracts and the event organizer has to say it is okay.

        The whole process of tickets makes sure that everything is safe and we can see what is happening it also stops people from making tickets. Tickets are managed in a way that prevents duplication tickets are secure because of this.

   3. AI-Based Fraud Detection Process

      It examines behavioral aspects such as frequency of pur- chases, quantity of purchases, and inter-purchase duration. It generates risk scores for transactions, which are then classied into approved, suspicious, or denied categories to prevent fraud from happening in real time.

   4. Identity Verication Mechanism

      A unique identity token is generated for each user to verify his/her identity. Users that have been veried are allowed access to perform certain critical operations such as reselling.

   5. System Workow

      The steps involved are creation of events, generation of NFT tickets, buying of tickets using cryptocurrency wallets, transaction evaluation through AI, storing transactions in a blockchain system, and management of resale activities.

   6. Integration of Blockchain and AI

   Combining blockchain technology and articial intelligence allows us to develop a sophisticated yet robust platform where the transactions performed are safe and transparent.

6. Proposed System

   The proposed system,Rexell is a platform for buying and selling event tickets that uses blockchain and articial intel- ligence. This is done to stop people from buying tickets just to sell them at a price and to make sure tickets are given to people safely. Rexell makes things more transparent it reduces cheating. It helps nd suspicious things happening in real time with the Rexell system. The Rexell system is really good, at doing these things because it uses blockchain and articial intelligence to run the Rexell platform.

   TABLE I

   Comparative Analysis of Existing NFT Ticketing Systems and Proposed System

   No.	Existing System	Key Features	Limitations	Proposed System Improvement
   1	PureNFT (ICCE 2025)	AI-assisted detection for unfair resale, ERC-721 token usage, lightweight architecture	Detection errors may occur af- ter ticket purchase completion	Introduces pre-transaction behav- ioral monitoring using client-side and server-side AI with dynamic risk scoring to reduce incorrect de- cisions
   2	Secure NFT Ticketing (ICUIS 2024)	Blockchain-based ownership validation and secure ticket transfer	High transaction costs and complex onboarding for non- crypto users	Integrates stablecoin-based pay- ments (cUSD) with simplied wal- let connection and seamless fron- tend interaction to enhance acces- sibility
   3	IPL NFT Ticketing (ICEI 2024)	Designed for large-scale ticket distribution in sports environ- ments	Adoption challenges due to technical complexity and lack of automation	Provides intuitive user interface with automated AI decision- making and real-time validation to simplify user interaction
   4	Blockchain Ticket Sales (ICCSEC 2023)	Smart contract-enabled ticket sales and resale control	Limited user experience and insufcient monitoring of re- sale misuse	Implements AI-driven resale track- ing combined with policy enforce- ment mechanisms to regulate sec- ondary market behavior
   5	NFT Ticketing System (MMSP 2022)	NFT minting and ownership verication using blockchain	Absence of mechanisms to de- tect bots or abnormal resale patterns	Incorporates dedicated bot detec- tion and scalping detection mod- ules using behavioral analytics and machine learning models
   6	Smart Contract Ticket Mgmt (JEEIT 2023)	Transparent ticket management with decentralized execution	Integration difculties with ex- isting systems and limited ex- ibility	Uses modular microservices archi- tecture (FastAPI-based) enabling scalable integration with external services and APIs
   7	akaTick Hybrid System (MetaCom 2023)	Combines centralized database with blockchain for improved performance	Centralized database may cre- ate performance bottlenecks and scalability issues	Utilizes distributed caching (Redis) and asynchronous processing with message queues to minimize bot- tlenecks and improve throughput
   8	Decentralized Ticketing (2022)	Secure and distributed ticketing framework using blockchain	Scalability limitations and high transaction costs under heavy load	Ofoads computation-intensive tasks to off-chain AI services and optimizes blockchain interaction to improve efciency and scalability
   Proposed System Summary:The system will use an approach that incorporates blockchain technology alongside articial intelligence algorithms. Real-time behavioral data collection will be done using a client-side software development kit (SDK). Bots will be identied using bot detection microservices that run in the background of the system, and machine learning algorithms will be applied to make inferences from the collected data. The system will assess risk and implement policies aimed at preventing fraudulent activity prior to transactions. Soulbound identities will also be used to conrm users authenticity.

   1. System Overview

      The proposed system combines blockchain, articial intel- ligence, and user authentication to improve ticket security. Blockchain provides transparent and tamper-resistant trans- action records, while AI detects suspicious user activities. Authentication mechanisms verify user identities and support secure ticket transfers. Together, these components help pre- vent counterfeit tickets and enable regulated ticket resale.

   2. Blockchain-Based Ticketing

      Tickets are issued as unique NFTs through smart contracts. The blockchain securely records ticket ownership and all related transactions, ensuring transparency, traceability, and protection against duplication.

   3. AI-Based Anti-Scalping Mechanism

      The system monitors purchasing behavior, such as transac- tion frequency and bulk purchases, to detect suspiciou activ- ities. Based on risk assessment, transactions can be approved, agged, or blocked to reduce ticket scalping and fraud.

   4. Identity Verication System

      Each user receives a unique identity token linked to their verication status. Veried users gain access to additional platform features, including ticket resale. This mechanism improves security and prevents unauthorized participation.

   5. Controlled Resale Mechanism

      Ticket resale is governed by organizer-dened policies. Resale requests are subject to approval and may include restrictions on ticket quantity and resale price. These controls help prevent unfair pricing and promote equitable ticket dis- tribution.

   6. Workow of the System

   The workow starts with event creation and NFT ticket is- suance on the blockchain. Users purchase tickets through smart contracts, which automatically validate transactions. Veried users can transfer or resell tickets through authorized channels. Blockchain records and verication mechanisms ensure secure and transparent ticket management throughout the process.

7. System Architecture

   The proposed framework integrates blockchain and articial intelligence to create a secure and scalable ticketing platform. The architecture consists of four interconnected layers that collectively ensure transparency, security, and efcient ticket management.

   1. Frontend Layer

      The frontend layer provides functionalities such as event creation, ticket purchasing, and ticket resale. It serves as the interface between users and the underlying blockchain and AI components.

   2. Blockchain Layer

      The blockchain layer records all ticket-related transactions in a secure and immutable manner. Smart contracts automate ticket issuance, ownership transfers, and resale operations, ensuring transparency and preventing ticket duplication.

   3. AI Layer

      The AI layer evaluates user behavior and transaction pat- terns to identify suspicious activities. Based on risk analysis, transactions may be approved, agged, or blocked to reduce fraudulent actions.

   4. Storage Layer

      The storage layer utilizes decentralized solutions such as IPFS to store ticket metadata and event information. This approach improves data availability, enhances security, and reduces reliance on centralized storage systems.

   5. System Workow

      A single moment kicks off a chain – rst comes an event taking shape. Next appears a digital pass, minted as an NFT. Someone buys it, handing over value for access. At the gate, articial intelligence checks authenticity in real time. Finally, ownership locks into place across the distributed ledger.

   6. Operational Workow

   The workow begins with event creation and NFT ticket generation on the blockchain. Users connect their digital wallets to purchase tickets, after which the AI module eval- uates the transaction for potential fraud. Based on the risk assessment, the transaction is either approved or agged for re- view. Once conrmed, ownership details are securely recorded on the blockchain. Ticket resale is permitted only when predened conditions are satised and organizer approval is obtained.

   From the start, security in ownership tracking stands strong thanks to how the system is built. Clear records of every transaction appear without delay, showing each change openly. Instead of guessing, risky patterns get agged by smart checks that learn over time. Because of this setup, reselling tickets unfairly becomes far harder to pull off.

8. Implementation

   Starting fresh, Rexell builds on Web3 alongside smart algorithms to shape a secure space for tickets that grows without breaking trust. While code runs deep, safety stays sharp even when crowds swell fast.

   1. Frontend Implementation

      Next up, combining Next.js with React helps handle event planning smoothly. Tickets can be bought or handled later through the same system. Resale requests? They t right into the workow too. With Web3, logging in becomes safer through wallet-based identity checks while transactions happen directly from personal wallets. A different way to handle access and money moves online shows up when users rely on decentralized tools instead of traditional forms. Security shifts because control stays with individuals rather than centralized systems managing everything behind closed doors.

   2. Blockchain Implementation

      The system is implemented using Solidity smart contracts deployed on the Celo blockchain. Tickets are issued as ERC- 721 NFTs, ensuring unique identication and veriable own- ership. Smart contracts automate ticket issuance, transfer, and resale while enforcing predened rules throughout the ticket lifecycle.

   3. Smart Contract Functions

      Smart contracts manage core platform operations, including event creation, ticket minting, ownership transfer, and resale approval. All transactions are permanently recorded on the blockchain, providing transparency, traceability, and secure ownership management while reducing dependence on inter- mediaries.

   4. AI Module Implementation

      The AI module analyzes transaction frequency, purchase volume, and transaction timing to identify suspicious behavior. Based on the detected patterns, a risk score is assigned to each transaction, allowing it to be approved, agged, or blocked when necessary.

   5. AI Model Training and Evaluation

      A test used fake ticket buying data to check how well the fraud detector worked. Normal actions mixed with odd patterns lled the collection – like many tries to buy, loading up on tickets, or rapid buys close together in time. Each behavior showed different signs, yet only some raised red ags. Quick repeats stood out more than steady activity. What looked routine at rst often hid unusual rhythms underneath. Out of the gathered information, one portion fed the learning stage while another checked how well guesses held up when spotting real versus suspicious actions. Purchase habits played a role, along with ticket volume, when payments happened, how digital wallets behaved over time, also records of past resales. Instead of mixing everything, separation helped clarity. Each detail added context without overwhelming the process.

      Fig. 1. System Architecture of Rexell

      Timing mattered just as much as frequency did. Patterns emerged only after looking closely at repeated behaviors across accounts.

      The system assigns a risk score to each transaction based on user behavior and transaction patterns. Depending on the score, transactions are classied as legitimate, suspicious, or fraudulent. This approach helps identify potential ticket scalping activities and supports fair ticket distribution.

   6. Storage Implementation

      The system uses IPFS to store ticket metadata and event- related information. While transaction records remain on the blockchain, metadata is stored off-chain to reduce storage costs and improve scalability and performance.

   7. Integration and Deployment

   The platform integrates blockchain, smart contracts, cloud services, and a user interface into a unied architecture. Smart contracts handle core blockchain operations, while the cloud- hosted interface enables seamless user interaction. Continuous communication among components ensures reliable and ef- cient system performance.

   TABLE II Technology Stack

9. Experimental Results and Discussion

   1. Blockchain Performance Metrics

      Experimental results demostrate that the proposed system provides secure and efcient ticket management. NFT-based ticket issuance ensures unique ownership and prevents ticket duplication, while blockchain technology maintains transpar- ent and immutable ownership records. The platform achieved a high transaction success rate, and ticket issuance, transfer, and verication operations were completed within acceptable conrmation times, indicating stable performance under dif- ferent operating conditions.

      TABLE III

      Blockchain Performance Results

      Metric	Result
      Ticket Minting Success Rate	98%
      Average Conrmation Time	3.2 sec
      Ownership Verication	100%
      Ticket Duplication	0 Cases

   2. AI Performance Evaluation

   he AI module was evaluated based on its ability to identify suspicious transactions while minimizing false classications. Results show that the system effectively detects abnormal ticket purchasing behavior with consistent performance across key evaluation metrics. The risk assessment mechanism suc- cessfully distinguishes legitimate transactions from potentially fraudulent activities. The obtained results are presented in Table IV.

   Component	Technology
   Frontend	Next.js, React
   Blockchain	Celo Sepolia
   Smart Contracts	Solidity
   Wallet	MetaMask
   Storage	IPFS
   Backend API	FastAPI
   Database	PostgreSQL
   Cache	Redis
   AI Module	Python, Scikit-learn

   TABLE IV

   Performance Metrics of the Fraud Detection Module

   Metric	Value
   Accuracy	94.6%
   Precision	92.3%
   Recall	90.8%
   F1-Score	91.5%

   Performance analysis also indicates that the blockchain infrastructure processes transactions efciently under vary- ing workloads. Although the AI module experienced slightly higher processing times during peak loads, the overall system remained stable and suitable for practical deployment.

   Blockchain: Successful 98

   AI: Accuracy: 94.6

   Resale restrictions: 100

   The evaluation conrms that the proposed approach en- hances ticket security, transparency, and fraud prevention when compared with traditional ticketing systems. Key benets include:

   • Prevention of ticket duplication through NFT-based own- ership

   • Detection of fraudulent activities using AI-based analysis

   • Enhanced transparency through blockchain records

   • Secure and regulated ticket resale

10. System Testing

    From tiny pieces to full ow, each part of the new ticket system got checked. Not just alone – how they t together mattered too. Tests ran at every stage, making sure nothing broke when joined. Modules worked on their own before being linked up. Checking one thing led to checking how it behaved with others.

    TABLE V

    Summary of Testing Results

    Module	Test Cases	Passed
    Event Creation	10	10
    Ticket Minting	15	15
    Ticket Purchase	20	19
    Resale Requests	10	10
    Marketplace Purchase	10	10
    Wallet Integration	10	10

    Despite one hiccup, nearly every function held up under scrutiny. From start to nish, managing events owed without issue. Ticket creation followed smoothly, then moved into resale operations – each step conrmed working. Transactions on the marketplace linked properly, just like wallet connections did later. A single test around buying tickets needed extra attention, though everything else passed outright. Close to 98 Security gets stronger when blockchain teams up with articial intelligence in ticketing, one study shows. Reliability climbs too, not by chance but through system design. Fairness follows a similar path – built in, not added later. Each piece connects differently than before, shifting how trust forms

    behind the scenes.

    The gure shows the interface used by organizers to create events by entering details such as event name, location, date, and ticket information.

    This interface displays all available events, allowing users to browse and select events for ticket purchase.

    The gure illustrates the event details page where users can mint NFT-based tickets through blockchain transactions.

    Fig. 2. Event Creation Interface

    Fig. 3. All Events Listing

    Fig. 4. Event Details and NFT Ticket Minting

    Fig. 5. Generated NFT Ticket with Unique Identity

    Fig. 6. Setting Resale Price for Ticket

    The generated NFT ticket contains a unique identier and QR code, enabling secure ticket ownership and verication.

    . Users can list tickets for resale by setting a price within platform-dened rules, including royalty and fee policies.

    Fig. 7. Organizer Approval for Resale Requests

    Event organizers can review and approve resale requests to ensure controlled and authorized ticket distribution.

    Fig. 8. Resale Market for Veried Ticket Trading

    The resale marketplace allows users to buy veried tickets securely and transparently from other users.

11. Limitations

    Although the proposed system achieved encouraging results, certain limitations remain. The implementation was tested on the Celo Sepolia network, and its performance may differ in a production-scale environment with higher transaction volumes. Factors such as scalability and transaction latency require further evaluation under real-world conditions. In addition, the fraud detection module was developed using synthetic transaction data. Therefore, its ability to identify emerging and previously unseen fraud patterns should be validated

    using real-world datasets. Future research should focus on large-scale deployment and testing to further improve system reliability, scalability, and adaptability.

12. Challenges, Research Gaps, and Future

    Directions

    1. Current Limitations in Existing Systems

       1. Synthetic Data Limitations: Many fraud detection sys- tems rely on generated datasets because real transaction data is difcult to obtain. As a result, their effectiveness in real-world environments may require further validation.

       2. Transaction Cost and Latency: Blockchain platforms can experience higher transaction costs and slower processing during periods of heavy network activity.

       3. Scalability Issues: Handling a large number of users simultaneously remains challenging, as increased trafc can reduce system performance and transaction throughput.

       4. Usability Challenges: Managing wallets and private keys can be difcult for new users, which may affect the overall user experience.

    2. Research Gaps

       1. Limited Blockchain-AI Integation: Most existing sys- tems use blockchain and AI as separate technologies. Their combined potential is still not fully explored.

       2. Lack of Interoperability: Many ticketing solutions op- erate on a single blockchain, limiting interaction with other networks.

       3. Privacy Concerns: User verication may expose sensi- tive information. More effective privacy-preserving methods are needed.

       4. Limited Real-World Validation: Many proposed solu- tions have been tested only in controlled environments, with limited evaluation in real-world scenarios.

       5. Regulatory Challenges: Variations in legal and regula- tory requirements across regions can hinder the adoption of blockchain-based ticketing systems.

    3. Future Research Directions

       1. Hybrid Architectures: Using a combination of on-chain and off-chain processing may improve scalability and reduce operational costs.

       2. Decentralized Governance: Decentralized governance models can support transparent decision-making and greater user involvement.

       3. Digital Identity Integration: Privacy-focused digital identity solutions can enhance user verication while protect- ing personal information.

       4. Advanced AI Techniques: Advanced machine learning methods may improve the accuracy and effectiveness of fraud detection systems.

       5. Quantum-Resistant Security: Future platforms should consider quantum-resistant cryptographic approaches to strengthen long-term security.

       6. Sustainable Blockchain: Energy-efcient consensus mechanisms can reduce resource consumption and improve the sustainability of blockchain networks.

    4. Future Work

    Future work will focus on improving the scalability, secu- rity, and practicality of the proposed system.

    • KYC Integration: Incorporate real-world identity veri- cation to strengthen user authentication.

    • Cross-Chain Support: Enable ticket transfers across different blockchain networks.

    • Enhanced Fraud Detection: Explore advanced machine learning techniques for more accurate fraud detection.

    • Privacy Protection: Apply zero-knowledge proof mech- anisms to support secure user verication.

    • Performance Evaluation: Conduct large-scale testing to assess system reliability and scalability under real-world conditions.

13. Conclusion

Blockchain and articial intelligence can improve the secu- rity and efciency of event ticketing systems. Blockchain pro- vides transparent and tamper-resistant records, while AI helps identify fraudulent activities and automate ticket management. Together, they support secure ownership verication and fair ticket distribution.

Despite these benets, challenges such as scalability, regula- tory requirements, and real-world deployment remain. Future work may focus on cross-chain support, improved smart con- tracts, and privacy-preserving identity verication to enhance system reliability and usability.

References

1. G. A. Haryadi, A. Zainudin, J.-M. Lee and D.-S. Kim, PureNFT: A blockchain-based ticketing system with lightweight AI for scalping pre- vention, in Proc. IEEE Int. Conf. Consumer Electronics (ICCE), Las Ve- gas, NV, USA, 2025, pp. 16, doi: 10.1109/ICCE63647.2025.10930216.

2. P. Cholke, Y. Munde, M. Sayyed, A. Vidhale and N. Zanwar, Secure event ticketing system using NFT ERC721 tokens, in Proc. Int. Conf. Ubiquitous Computing and Intelligent Information Systems (ICUIS), India, 2024, pp. 14011407, doi: 10.1109/ICUIS64676.2024.10866537.

3. A. Zainudin et al., Blockchain-inspired collaborative cyber-attacks detection for securing metaverse, IEEE Internet Things J., vol. 11, no. 10, pp. 1822118236, 2024, doi: 10.1109/JIOT.2024.3364247.

4. G. A. Haryadi et al., Pure loyalty: Efcient and secure framework for NFT loyalty programs, J. Korean Inst. Commun. Inf. Sci., vol. 49, no. 7, pp. 983992, 2024.

5. I. S. Igboanusi et al., Smart auto mining (SAM) for industrial IoT blockchain network, IET Commun., vol. 16, no. 18, pp. 21232132, 2022.

6. A. Vashistha et al., A robust framework for fraud detection in banking using ML and NN, Proc. Natl. Acad. Sci. India A, vol. 94, no. 2, pp. 201212, 2024.

7. O. Ndama, I. Bensassi and E. M. En-Naimi, Optimizing credit card fraud detection using deep learning, Int. J. Electr. Comput. Eng., vol. 14, no. 4, pp. 48024814, 2024.

8. A. R. Khalid et al., Enhancing credit card fraud detection: An ensemble machine learning approach, Big Data Cogn. Comput., vol. 8, no. 1, 2024.

9. TimeChain Labs, How blockchain is revolutionizing ticketing, Oct. 2024. [Online]. Available: https://timechainlabs.io

10. SeatLab NFT, How blockchain can control the secondary market, Jun. 2022. [Online]. Available: https://www.seatlabnft.com

11. SimpliLearn, Top deep learning algorithms, Aug. 2025. [Online].

    Available: https://www.simplilearn.com

12. Nadcab Labs, ERC-721 standard in smart contracts, Sep. 2025. [Online]. Available: https://www.nadcab.com

13. QuantInsti, Convolutional neural networks, Nov. 2024. [Online]. Avail- able: https://blog.quantinsti.com

14. Swedish University of Agricultural Sciences, A ticket to blockchains. [Online]. Available: http://www.diva-portal.org

15. Risk Ident, Battling the bots, Feb. 2022. [Online]. Available: https://riskident.com

16. Rapid Innovation, Gas optimization guide, Sep. 2024. [Online]. Avail- able: https://www.rapidinnovation.io

17. Secutix, Blockchain for ticketing industry. [Online]. Available: https://appexchange.salesforce.com

18. Dev Team Space, Blockchain KYC verication, Jun. 2025. [Online].

    Available: https://www.devteam.space

19. Leeway Hertz, Decentralized NFT storage using IPFS, Sep. 2022. [Online]. Available: https://www.leewayhertz.com

20. Keylabs, Handling imbalanced data, Nov. 2024. [Online]. Available: https://keylabs.ai

21. IdeaUsher, Blockchain KYC systems, May 2024. [Online]. Available: https://ideausher.com

22. Investopedia, Distributed ledger technology, Jun. 2024. [Online].

    Available: https://www.investopedia.com

23. Info-Tech, Next-gen ticketing systems, Dec. 2024. [Online]. Available: https://www.infotech.com

24. Pyth Network, Smart contract security audit, Oct. 2025. [Online].

    Available: https://www.pyth.network

25. Hedera, Smart contract audit, Jul. 2022. [Online]. Available: https://hedera.com