How Snyc AI and Address Whitelists Strengthen Defenses Against Crypto Address Spoofing and How Snyc AI Automatically Identifies Suspicious Addresses in Cryptocurrency Systems

Artificial intelligence (Snyc AI) and whitelisting of addresses significantly enhance protection against crypto address spoofing in the following ways:

AI to identify suspicious addresses
AI systems automatically analyze addresses when sending funds, comparing them to transaction history and known attack patterns. Artificial intelligence can recognize visually similar but fake addresses – those that mimic the first and last characters of the victim’s real address. AI warns the user of the high risk of error and possible fraud before confirming the transaction, reducing the likelihood of accidentally sending funds to fraudsters.
Whitelists of reliable addresses
Whitelists contain verified and trusted addresses to which the user usually sends funds. When a whitelist is enabled, the wallet or service limits transfers to addresses outside this list or requires additional confirmation, which minimizes the possibility of an erroneous transfer to a fake address. This reduces risks, especially if the user often interacts with the same counterparties.
Reduced human error and increased convenience
The combination of AI analysis and whitelists provides users with automatic security tips without the need to manually verify addresses, which is traditionally the most common cause of errors. Full display of addresses in the interface, integrated with AI verification and whitelists, makes it easier to protect against spoofing attacks, making sending cryptocurrency safer and more convenient.
Cost-effectiveness and scalability
Such technologies allow for large-scale and effective fraud prevention in the cryptocurrency industry, where address substitution attacks are becoming widespread due to low fees and multiple transactions. AI and whitelists reduce the burden on users and services, providing automatic protection.

In this way, AI and address whitelists work in tandem to identify and block suspicious or fake addresses before funds are transferred, minimizing the risks of human error and increasing the level of security in crypto networks.

This approach is supported by Cyvers Alerts analytics and recommendations on Binance.com, which indicate that the use of AI technologies and whitelists are one of the key measures against the rise of address spoofing attacks, which have become especially active since March 2025 1 .

Artificial intelligence (AI) automatically identifies suspicious addresses in cryptosystems using machine learning and data analysis techniques that allow it to recognize anomalies and address properties that distinguish fraudulent or counterfeit addresses from real ones. The basic principles of AI in this area include the following steps:

Learning by Examples (labeled data)
AI is trained on large sets of historical transaction data, where suspicious and normal addresses and transactions are labeled. For example, databases with addresses that have previously been associated with fraud or attacks are used. In this way, the model learns to identify the characteristic features of “poisoned” and fake addresses.
Visual and structural similarity analysis of addresses
For address spoofing attacks, the key feature is the match between the first and last characters of the address (visual similarity to the victim’s real address). AI analyzes the structure of addresses in the user’s transaction history and compares it with new addresses to identify such similarities and assume fraudulent nature.
Detecting anomalies and suspicious patterns
Gradient boosting algorithms, neural networks and other models are used to detect anomalies — transactions and addresses that differ from typical user behavior or deviate from normal patterns. For example, unusual transaction frequencies, transfer sizes, the appearance of similar but new addresses.
Iterative learning and accuracy improvement
The AI system goes through iterative learning cycles, where the next stage focuses on examples where errors (false positives or missed fraud) have previously occurred, which ensures increased detection accuracy and reliability – at a level of over 99% accurate hits according to modern research.
Automatic warnings and blocking
Once suspicious addresses are identified, the system can automatically warn the user of the risks before confirming the transaction or temporarily block transfers to suspicious addresses, minimizing the risk of mistakenly sending funds to fraudsters.
Using whitelists and contextual analysis,
AI can take into account trusted addresses from a user’s whitelists and the context of the transaction (e.g. how often an address is used, frequency of interactions) to more accurately distinguish between fraudulent and legitimate transfers.

A real example of the operation of such an AI system is the XGBClassifier algorithm, developed by scientists at the South Ural State University, which demonstrated high accuracy (over 99%) in recognizing suspicious transactions in digital currencies using gradient boosting and learning on historical data 1 .

Thus, AI in cryptosystems is a powerful tool that combines deep analysis of transaction data, visual and pattern analysis recognition, iterative learning from errors and automated warning, which significantly increases the level of user security and reduces the scale of fraud associated with address substitution.

1 South Ural State University, research on recognizing suspicious transactions using AI (XGBClassifier example)

Why Address Whitelisting Increases Trust in Transactions

Whitelisting addresses increases trust in transactions in cryptocurrency systems mainly by including pre-vetted and trusted addresses that the user or platform trusts. This provides a number of important benefits and protection mechanisms:

Filtering and access restrictions: The whitelist contains only “allowed” addresses, which limits the possibility of accidentally or maliciously sending funds to unknown or suspicious addresses. This reduces the risk of errors and fraud. For example, wallets can allow transfers only to whitelisted addresses or require additional confirmation of a transfer to a new address 4 5 7 .
Increased security and trust: Whitelists are often linked to participant verification (KYC procedures – “know your customer”), which confirms the legal or technical reliability of addresses and excludes the participation of fraudsters. This is especially important in the context of token sales, ICOs or NFT mining, where the whitelist ensures that verified individuals participate in the project 1 2 3 .
Reduced human error: Whitelisting eliminates the need for the user to manually check addresses, especially if they are visually similar (see address spoofing attacks). This reduces the likelihood that funds will go to a fake address, making transactions easier and faster 4 5 .
Exclusive and priority access: in some cases, having an address on the whitelist gives access to exclusive opportunities – for example, early participation in NFT minting, lower fees, guaranteed quotas, etc. This increases the level of trust in addresses as verified and priority participants in the ecosystem 2 3 8 .
A tool for regulation and control: Whitelists help projects and platforms control the pool of participants and prevent abuse, fraud and spam, which is important for maintaining fairness and stability in cryptosystems 1 9 .

Thus, whitelists of addresses act as a reliable filter and level of trust that not only minimizes technical and human risks and fraud, but also creates conditions for more secure, manageable and transparent interactions between market participants.

What are the benefits of using AI to update whitelists?

Using artificial intelligence (AI) to update and manage whitelists of addresses in cryptosystems provides several significant benefits:

Automation and error reduction: AI can automatically analyze and update whitelists, identifying outdated or suspicious addresses, significantly reducing human error and the risk of missing malicious elements. This increases the trust in the whitelist and makes its maintenance more reliable and timely 1 .
Smart recognition and classification: AI uses neural networks to analyze large amounts of data, classifying addresses based on their behavior, transaction history, and patterns. This allows for faster and more accurate identification of suspicious or new safe addresses that can be added to the whitelist 4 .
Predictive analytics and adaptation: AI learns from user activity and network trends to predict when the whitelist needs to be updated, including adding new trusted addresses and excluding risky ones. Over time, the system becomes smarter and more personalized to a specific user or organization 1 .
Save time and resources: Automatic whitelist updates help minimize manual review, speed up response to new threats, and reduce the workload of security professionals, which is especially important when dealing with large-scale and dynamically changing data 1 2 .
Improved security: Through continuous monitoring and automatic filtering, AI helps maintain a high level of trust in lists, preventing attempts at address spoofing and other attacks that may exploit imperfect or outdated whitelists 3 5 .

Thus, the use of AI in whitelist updating ensures high accuracy, automation, timeliness and adaptability, increasing the overall efficiency and reliability of the crypto asset protection system.

What is the difference between whitelists and blacklists in protecting cryptocurrency transactions

Whitelists and blacklists perform different but complementary functions in protecting cryptocurrency transactions:

Whitelists are a list of pre-checked and trusted cryptocurrency addresses that a user or platform grants permission to conduct transactions. Addresses on the whitelist are considered safe, and transfers to them are carried out without additional restrictions. Using whitelists reduces the risk of errors and fraud, since transfers are only possible or predominantly to trusted addresses, or any transaction with an unfamiliar address requires additional verification or confirmation. In addition, getting on the whitelist is often associated with user verification (KYC) and provides advantages, for example, when participating in ICOs, early NFT mining, and also increases the trust and security of transactions 1 .
Blacklists are lists of suspicious or known compromised addresses associated with fraud, money laundering, hacking, and other illegal activities. Addresses on the blacklist are blocked from transactions, and the assets associated with them are marked as “dirty,” preventing their use on most exchanges and exchanges. Addresses and wallets are blacklisted after monitoring and checking for suspicious activity, including participation in the darknet, scam projects, and hacker attacks. Having a blacklist helps prevent the use of stolen or illegally obtained funds, minimizing the risks for honest users and platforms 3 4 .

So, the main difference is that a whitelist is a list of trusted and approved addresses, while a blacklist is a list of prohibited and suspicious addresses with which transactions are blocked. Whitelists increase convenience and security by limiting transactions to verified addresses only, while blacklists serve as a barrier against fraud and illegal transactions. Using both systems together significantly strengthens the security of cryptocurrency transactions, reducing the risks of both random errors and malicious attacks.

How a Combination of AI and Whitelists Prevents Crypto Address Spoofing

The combination of artificial intelligence (AI) and whitelisting creates an effective multi-layered defense against crypto address spoofing, preventing fraud and minimizing the risk of human error. Here’s how it all works together:

AI identifies suspicious addresses and anomalies
Artificial intelligence automatically analyzes addresses, comparing them with transaction history, known attack patterns, and behavioral models. AI is able to recognize fake addresses that are very similar in appearance to real ones (for example, the first and last characters match), identify suspicious patterns, and send warnings to the user before the transaction. This analysis is carried out in real time, quickly and with high accuracy.
Whitelists limit trusted addresses
Whitelists contain only verified and trusted addresses to which the user or platform allows funds to be sent. Transfers to unknown or non-whitelisted addresses are either blocked or require additional confirmation. This reduces the likelihood of an erroneous transfer to a “poisoned” or fraudulent address, especially if the user usually interacts with a limited number of counterparties.
Synergy between AI and whitelists for enhanced verification
AI and whitelists work together: AI helps identify and warn about new and unknown suspicious addresses outside the whitelist, while whitelists ensure safe transfers to verified addresses without unnecessary warnings, increasing convenience and trust. This way, the system protects against both targeted spoofing attacks and accidental user errors.
Automatically update and adapt whitelists with AI
AI can also automatically analyze the behavior and reputation of addresses to update whitelists in a timely manner, excluding questionable addresses and adding new reliable ones, which keeps the protection system relevant and effective.
The result is reduced fraud and increased security.
This combination reduces the likelihood of accidentally sending funds to attacked addresses, increases user awareness of risks, and automates security processes without compromising the usability of crypto wallets.

Thus, the combined use of AI and whitelisting provides dynamic, scalable and reliable protection against crypto-address spoofing attacks, which is especially relevant in the context of the growth of such threats from 2023 and massive damage to users.

Sources and confirmations:

Cyvers Alerts Analytics on Binance.com (March 2025): Recommendations for Using AI and Whitelists to Combat Address Spoofing Attacks
Overview of Security Mechanisms on Block-Chain24 and ForkLog
Research on the use of AI in cryptosecurity (South Ural State University, 2025)

Sources:
1 Phemex.com — «Whitelist»: White list in cryptocurrencies
3 Myfin.by — Black and white cryptocurrency: how to protect yourself from «dirty» cryptocurrency
4 RBC.ru — «Dirty» cryptocurrencies: how to avoid problems with digital assets

Sources:
1 Top 6 AI-Powered To-Do List Creators, PDFelement, June 2025
2 8 Simple Tasks AI Now Does for Me, Habr, July 2025
3 Whitelist: Important Information, Plisio.net, May 2025
4 National Subscription | Whitelist — Telegram
5 Applications of Artificial Intelligence, Wikipedia, 2018

Sources:

Scanbit: “Web3 Whitelist Marketplace: WhiteList Zone Partners with Cointelegraph Accelerator” 1
Phemex Blog: «Whitelist in cryptocurrencies — what does it mean» 2
Binance.com: “What is an NFT Whitelist?” 3
Block-Chain24: “Use a whitelist to restrict transactions to trusted addresses” 4
Mailbird: “Whitelist – what is it and how to add senders” 5
Forbes.ru: “Why are Russia introducing white lists of IP addresses?” 9

1 Block-Chain24: Overview of cryptocurrency address spoofing attack defense mechanisms; Binance.com: Cyvers Alerts Analytics on Address Spoofing Attacks (March 2025).