Blockchain technology could very well be here to stay, as evident by investments and adoption by Fortune 500 companies. However, with its rise, there's also an increase in fraudulent activities.
To combat these threats, various types of machine learning models are being employed for detecting and preventing fraud on blockchain platforms — that also points to the next big shift, that being the normalisation of AI in blockchain.
Understanding Machine Learning in Fraud Detection
Machine learning (ML) entails algorithms that allow systems to learn from data patterns. In the context of blockchain, ML models analyse transactional data to identify and mitigate fraudulent activities efficiently and in real-time.
Fradulent activities have happened, and will continue to happen. Let's take a look at one incident, which underscores why we need to pair AI with blockchain for dynamic security solutions, in the Web3 and crypto space.
The Rise of Fraudulent Activities in Blockchain
One well-documented instance of fraud in the blockchain space involved the infamous Bitfinex hack in 2016. Bitfinex, a major cryptocurrency exchange, fell victim to a loss of approximately 120,000 BTC, equivalent to over $60 million at the time. Hackers exploited a vulnerability in Bitfinex's multi-signature wallets, orchestrating an elaborate scheme that went undetected until it was too late.
This incident exposed several weaknesses in blockchain platforms, such as poorly written smart contracts and consensus mechanism vulnerabilities, and underscored the necessity for robust fraud detection mechanisms.
The Bitfinex hack served as a wake-up call for the blockchain community, emphasising the need for advanced machine learning models capable of identifying and intercepting fraudulent activities before they result in significant financial losses.
Types of AI Machine Learning Models
Different types of machine learning models serve various functions in fraud detection. Here’s a look at the most commonly used models:
1. Supervised Learning Models
Supervised learning involves training a model on labeled datasets. These AI models can predict and detect fraud based on historical data. Key models include:
- Logistic Regression: A statistical model that predicts the probability of fraud by identifying patterns in transaction data
- Decision Trees: These models make decisions based on a series of rules derived from the data, effectively classifying transactions as fraudulent or not
- Support Vector Machines (SVM): This model finds the optimal boundary between classes to classify data points accurately
2. Unsupervised Learning Models
Unsupervised learning models are not trained on labeled data. Instead, they identify anomalous patterns that deviate from the norm. Popular unsupervised models include:
- K-means Clustering: This technique groups similar transactions together, helping to identify unusual clusters that may indicate fraud
- Isolation Forest: This model isolates anomalies by randomly selecting splits, effectively identifying fraudulent activities
3. Semi-Supervised Learning Models
Semi-supervised learning combines labeled and unlabeled data to create more robust models. This approach is particularly useful when labeled fraud data is limited.
4. Reinforcement Learning Models
Reinforcement learning models learn by interacting with their environment, making them suited for continuously adapting to new types of fraudulent activities. An example is:
- Q-Learning: A popular reinforcement learning algorithm that learns the value of actions in various states, optimising the model's decision-making process over time.
As a layer 1 blockchain platform, aelf is leading by example by embracing a decentralised AI infrastructure. It facilitates the development and deployment of AI-powered dApps. Besides empowering users and developers with greater automation of tasks and smart contract creation, AI is expected to help bolster security protocols to keep fraud and malicious activities at bay, so all parties can engage in blockchain-related activities with peace of mind.
Combining Models, Combatting Threats
Often, a single model may not suffice. Combining multiple machine learning models can offer a comprehensive solution. Techniques like ensemble learning leverage multiple models to improve fraud detection accuracy.
Role of Anomaly Detection in Preventing Fraud
When it comes to preventing fraud in blockchain platforms, anomaly detection is a crucial player. Unlike traditional fraud detection methods that rely on known patterns, anomaly detection focuses on recognising deviations from normal behavior within the dataset. These deviations, or anomalies, often signal fraudulent activities on the blockchain.
Anomaly Detection Techniques
Several anomaly detection techniques are leveraged in this domain. One common method is Real-Time Anomaly Detection, which continuously monitors transactions and flags suspicious activities as they occur.
This can be instrumental in stopping fraud before any damage is done. Another technique, Retrospective Anomaly Detection, involves analysing historical data to spot unusual patterns that may have been overlooked. Both methods make use of machine learning algorithms that evolve and improve over time, based on new data inputs to improve the blockchain with AI.
Advantages of Anomaly Detection
The primary advantage of anomaly detection is its ability to identify novel fraud patterns that might otherwise go undetected. In a constantly evolving landscape like blockchain, where new forms of fraud can emerge regularly, this adaptability is much needed.
Moreover, anomaly detection can significantly reduce false positives – the bane of any fraud detection system – by focusing on genuinely unusual patterns rather than predefined ones.
Implementing Anomaly Detection
This involves a blend of supervised and unsupervised learning models. For example, clustering algorithms like K-means group data points based on similarities, helping to identify outliers.
Similarly, autoencoders, a type of neural network, can learn data patterns and spotlight deviations with high precision. Incorporating these models enables a more robust and dynamic approach to fraud detection in blockchain with AI.
Conclusion: Human Learning vs Machine Learning in Blockchain Fraud Detection
Although hackers and fraudsters are becoming more sophisticated, the good news is that AI and blockchain technologies are, too, sophisticated in their own rights to combat them. The future looks promising as machine learning models become more intelligent and data-rich. Blockchain platforms can expect even more accurate, real-time fraud detection capabilities, ensuring enhanced security and trust.
If you know that Web3 platforms you're involved in have such machine learning models in place, you can have some measure of assurance that they are making a difference in safeguarding your digital assets.
*Disclaimer: The information provided on this blog does not constitute investment advice, financial advice, trading advice, or any other form of professional advice. Aelf makes no guarantees or warranties about the accuracy, completeness, or timeliness of the information on this blog. You should not make any investment decisions based solely on the information provided on this blog. You should always consult with a qualified financial or legal advisor before making any investment decisions.
About aelf
aelf, an AI-enhanced Layer 1 blockchain network, leverages the robust C# programming language for efficiency and scalability across its sophisticated multi-layered architecture. Founded in 2017 with its global hub in Singapore, aelf is a pioneer in the industry, leading Asia in evolving blockchain with state-of-the-art AI integration to ensure an efficient, low-cost, and highly secure platform that is both developer and end-user friendly. Aligned with its progressive vision, aelf is committed to fostering innovation within its ecosystem and advancing Web3 and AI technology adoption.
For more information about aelf, please refer to our Whitepaper V2.0.
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