Fraud detection in crypto transactions: ML-powered approaches for enhanced security

The continuous progression of technology has us—people modernize our ways of living to keep up with it. One of the many revolutionary ideas is cryptocurrency. This digital currency that mostly only exists electronically has offered consumers comfort, investment opportunities, and more just at the tip of our fingers. While it offers promising ideas, the world of cryptocurrency is predominantly unregulated, making it a mecca for cybercriminals.

In this article, we’ll go through examples of cryptocurrency frauds, how to avoid them at all costs and detecting them through Machine Learning (ML) approaches for refined security.

Common Cryptocurrency Scams to Look Out For

Investment Scam

This type of cryptocurrency fraud involves an ‘actor’ who plays the role of an investment manager, celebrity/influencer, and may be a love interest you met online. It can be varied, but the common ground is that they will entice you to invest in a certain cryptocurrency and offer promising gains and returns. While it is true that cryptocurrency can be a good investment opportunity, especially in this digital age, it is still a risky decision that requires research, plenty of time, and thorough thinking.

Fraudulent Initial Coin Offerings (ICOs)

As the famous idiom says, “the early bird catches the worm,” is usually the promise with Initial Coin Offerings or ICOs scams. ICOs, in a technical sense, are a way for startup crypto firms to raise money for future consumers. It can be a lot more promising since grabbing the opportunity to participate in a massive project early could yield incredible returns. However, as it is a new venture, cyber criminals take the chance and hide behind it. Numerous ICOs already turned out to be scams as criminals go a long way to deceive the interested investors, such as faking the ICO with little effort or money, promoting it through the use of high-end marketing materials, and renting fake offices.


Entirely operating online, it is no surprise that phishing falls into this category. The goal of phishing scams is to have access with regard to your information. In this case particularly, it’s the crypto keys. It’s an old favorite among cyber criminals by luring people with a link to a fake website, asking them to provide private information. Once the scammers have acquired the needed details, they will then steal the cryptocurrency in the victims’ wallets. Aside from the usual, hackers are constantly creating new phishing schemes, such as impersonating famous companies and posting phishing links on their social media or offering token giveaways.

How to avoid and detect such fraudulent transactions?

Crypto transaction scams can happen to anyone, and with the unpredictability and prevalence of such, we can help each other to avoid being caught and detect one. Commonly, crypto users or interested-to-be-one should look out for the following:

  • Guaranteed returns
  • Free money or freebies
  • Excessive marketing
  • Unknown key people
  • Poorly made or nonexistent crypto whitepaper

However, for enhanced security, we can look into and invest in better ways so that we can be one step ahead of cyber criminals. This is where Machine Learning-based approaches come into the picture.

What is Machine Learning (ML)?

ML is the process in which computers learn to make decisions on their own without explicit programming, but with data it gathered. The programs are designed to recognize known and unknown patterns, improve the sets of data over time, and make use of past information in guiding future decisions, if a similar situation arises.

ML-based approach for fraud detection

There are many algorithms to consider in detecting scams in your crypto transactions. These techniques generally train the dataset on the fraudulent transaction patterns, and therefore, predict the incoming ones. Here are the most commonly implemented algorithms used in fraud detection online:

1.     Decision Tree

One of the most straightforward models to use in binary classification or targets. It adopts a tree-like decision-making model, hence the name,  determining whether a transaction is fraudulent.  They do have a tendency to overfit, making it its weakness.

2.     K-Nearest Neighbor

This model is good for multi-class cases, and is simple to implement. Proven quite accurately, but difficult to interpret when making a decision, as you’ll need to determine the number of nearest neighbors, which can be costly.

3.     Logistic Regression

Based on a set of relevant parameters, this particular algorithm calculates the probability of one thing out of the two alternatives, such as ‘fraud’ or ‘non-fraud.’ Although the underlying math might seem complicated, it is highly interpretable and is widely used too.

4.     Neural Networks

Trying to copy the neurophysiology of the human brain, this model is the go-to algorithm for identifying non-linear relations and unprecedented fraud scenarios. By detecting such relations in a dataset, it can also help predict real-world problems.

5.     Support Vector Machines (SVMs)

A geometric method used to perform classification and regression analysis. It offers high accuracy, and great ability at generating non-linear decision boundaries, but can be computationally demanding.

For safer crypto transactions

Undeniably, fraud is an increasingly widespread issue in crypto transactions. No one would want to be a victim to such things. The good thing is, ML-based approaches can help us to detect fraudulent transactions by intelligently analyzing a large amount of data.


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