In 2023 alone, crypto fraudsters managed to get their hands on more than $24 billion worth of crypto, causing the alarm to go off for the entire industry. While the number is well below the $39 billion in 2022, the number is nowhere low enough to be relaxed about the threats of crypto fraud.
Naturally, many in the industry have been looking for better solutions, and Artificial Intelligence (AI) has become the biggest hope to fend off crypto criminals.
But is that hope based on reality?
AI Fortifying The Blockchain
Isn’t blockchain hack-proof? Well, a study by Epiq shows that even blockchain is not immune to crypto attacks. Creation errors, 51% attacks, and security holes are the most common channels for hackers to penetrate the system.
Luckily, we can leverage two AI-based strategies, Clustering and Peeling, to address these vulnerabilities.
Clustering
Clustering is an ML-based technique that uses algorithms to group data into subsets with shared characteristics. This makes it possible to aggregate blockchain addresses that are thought to be controlled by a single party. This advanced method discovers intricate webs of criminal activity, which aids in pattern recognition and sheds light on the hidden funnels for illegal funds, leading to more traceability and transparency.
Peeling
Criminals use smurfing to hide the true source of their illegal cash by making a succession of modest, complicated transactions; by “peeling” away these layers, it’s possible to uncover criminal activity.
To combat this strategy, AI’s analytical powers are vital. ML techniques enable AI systems to excel at spotting smurfing’s complex structure, making it easier to trace and interrupt these frauds. This strategy, which is based on AI’s capacity to learn from past data, is a huge step forward in detecting complex money laundering techniques. It also shows how AI can dynamically strengthen the blockchain ecosystem’s cybersecurity.
How AI Can Cover Crypto Security Holes
Beyond the blockchain landscape, AI is still the biggest hope for preventing crypto fraud. It can do so by:
AI-Based Auditing
Without AI, smart contract auditing hasn’t proved to be much of a success. In fact, $2.8 billion was lost to insecure smart contracts, despite the fact that %91 had gone through auditing.
AI-based monitoring tools can look through the source of smart contracts, checking each line and code for possible security holes. These might be anything from generic programming mistakes to problems unique to blockchain technology.
Programmers and auditors working on smart contracts may benefit from real-time input and ideas provided by AI tools. By receiving feedback quickly, developers may fix vulnerabilities while they’re still in the development process, which lowers the chance of releasing contracts that aren’t safe.
On top of that, AI can help with security by suggesting best practices, doing regression tests, and making sure that new modifications don’t compromise security or destroy current safeguards.
This can be incredibly helpful to companies like Euler Labs that lost $196 million to these attacks, creating one of the “hardest days” for the CEO Michael Bentley.
Real-Time Monitoring
AI also has the added benefit of being able to analyze massive amounts of data in real-time, which is very useful when Bitcoin networks are under assault. The more traditional methods of network security, which rely on operator monitoring and predetermined rules, leave networks vulnerable to more sophisticated attacks. Contrarily, AI is always on the lookout for anomalies and suspicious actions that might indicate a cyber assault.
AI uses ML methods to look at standard and unusual actions in crypto markets, wallets, and networks. When the AI system detects any unusual activity, such as many failed login attempts, incorrect password inputs, or significant withdrawals, it has the opportunity to alert the security personnel. Plus, it has the ability to identify security threats in real-time, so it can promptly notify the owner and diminish the likelihood of additional damage or theft.
AI-Based Predictive Analysis
By analyzing past data and patterns of cyberattacks, AI systems may anticipate possible dangers even before they happen. By taking this strategy, the security teams are able to implement safeguards that will keep such incidents at bay.
To illustrate, AI has the ability to detect patterns in ransomware assaults that target certain exchanges. It can also predict when the next attack will happen based on signals like a high volume of traffic from specific IP addresses or unusual behavior from recognized criminals. When an organization gets this kind of information, it can improve its security steps and take action, like blocking certain people or making the identification process stronger.
Phishing Detection
Phishing is an aged, but effective, trick for crypto hackers, and it has helped them steal +$730 million worth of crypto in the first half of 2024. So, it’s no small issue by any means. How can AI help?
Enhancing client authentication is one way AI may improve the security of cryptocurrency wallets and exchanges. Plus, most accounts currently employ MFA, which requires the user to verify many times that they are the sole authorized user. By incorporating biometrics of behavior into the identification process, AI can potentially take this to the next level.
From the way a person types and moves their mouse to the way they hold a cell phone, AI analyzes these actions. For the second layer of authentication, AI systems could use such external behaviors to build a user profile. Using AI allows platforms to detect unauthorized users and either reject access or request authentication.
Anti-Fraud AI in Real Life
It’s only logical for many to anxiously wait for a new AI frontier against crypto fraud. The good news is that some successful solutions are already in place.
One solution that’s become more popular is CUBE3.AI, a platform that uses a scoring system to help users understand the potential risks of using a particular crypto platform. Its Runtime Application Self-Protection (RASP) tools, available in Lite and Pro variants, reinforce security by protecting smart contracts and user-facing applications.
The company’s president, Jonathan Anastasia, recently stated that the key to preventing many scams is to provide “enough information” to the human user, which is coincidentally the “weakest link” in most crypto attacks. He also believes that the root of these Web3 incidents goes back to 1v1 conversations on social media, where uninformed users fall for the promise of an easy buck.
Another good example is the recent NEAR – Deutsche Telekom partnership, boosting the NEAR’s on-chain security by using validators while minimizing congestion and maximizing scalability.
Oliver Nyderle, Head of Digital Trust & Web3 Infrastructure at Deutsche Telekom MMS, stated that the AI-crypto partnership is a big step towards boosting “data sovereignty and user data control” in the crypto realm.
Can AI Be the Ultimate Solution?
While AI offers transformative tools to combat crypto fraud, from real-time monitoring to predictive analysis, it is not a one-size-fits-all solution. AI’s success depends on its integration with user awareness, regulatory frameworks, and continuous innovation. Criminal tactics evolve, making it crucial for AI to adapt swiftly.
While platforms like CUBE3.AI and partnerships like NEAR-Deutsche Telekom showcase AI’s potential, true security requires a collaborative effort. Ultimately, AI may not eliminate all risks but stands as a vital ally in fortifying the crypto space, fostering trust, and paving the way for a more secure blockchain ecosystem.
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