AI Cyber ​​Solutions Positioning Towards Better Secure Web3

The past year has been difficult for crypto markets with the Terra Luna crash, the FTX bankruptcy, a series of high-profile insolvencies and a plethora of hack-related incidents. A recent report from Immunefi, the bug-free and security platform, revealed that the crypto industry has suffered losses of $3.9 billion in 2022 due to various hacking, fraud and scam-related incidents.

The decentralized finance (DeFi) market was the most targeted by cybercriminals, suffering more than $3 billion in losses from 155 incidents, a 56 percent increase over 2021. A CyberEdge report found that a record 63 percent of ransomware victims who paid ransoms (2021), encouraging cybercriminals to increase their attacks, and that ransomware attacks increased by 80 percent year-on-year.

With the Web3 market forecast to scale to $6 trillion, cybersecurity experts expect cybercrime to scale in step with Web3 growth. Cyber ​​experts predict that artificial intelligence (AI), and especially the machine learning (ML) branch of AI, will significantly improve the fabric of digital security to make Web3 more secure.

The AI ​​cybersecurity market will grow to $46 billion by 2028, more than 23 percent per year. With the increasing popularity of AI platforms such as OpenAI’s ChatGPT, Google’s imprecise Bard, and the newly acclaimed Microsoft AI build from Bing, one can speculate that this basic AI technology could accelerate the development of the heuristic side of cybersecurity .

How ML can better secure Web3

Web3, the decentralized web, is built on the foundation of blockchain technology. While public blockchains can offer greater transparency and autonomy for users, they are (more) vulnerable to third-party attacks. Data and transactions are recorded on a public decentralized ledger, rather than a central (government) database, and present a new set of digital security concerns. ML is quickly becoming an integral part of today’s Web3 defenses, providing new ways to identify and mitigate potential vulnerabilities.

One of the ways ML is being used to defend the Web3 ecosystem is through the optimization of smart contracts. Smart contracts are self-executing contracts that contain the terms of the agreement in computer code and eliminate the need for human intervention. Smart contracts are the engine room of many of today’s most popular DeFi platforms. They can be vulnerable to external attacks due to lack of proper testing, poorly secured interactions with other smart contracts, re-entry attacks, and leading intrusions, to name a few.

Christian Seifert, researcher at the Web3 security platform Forta says: “Machine learning will continue to be used by crypto projects to identify and mitigate vulnerabilities present in their smart contract infrastructure. The technology can also provide insights and intelligence, which can improve decision-making and driving innovation within this space Simply put, ML is [fast] becoming an essential tool within the realm of Web3 security.”

ML models analyze vast amounts of big data, billions and trillions of data items, and can identify patterns and anomalies that help indicate fraudulent activity. ML is used in Web3 security in a wide range of areas, including predictive capabilities by training ML algorithms using historical data to identify the characteristics of ransomware attacks, phishing, malware, money laundering and terrorist financing, identity provenance, oracle data provenance and identify potential. node failure.

Dr. Neha Narula, Director of the Digital Currency Initiative at the MIT Media Lab, says: “Machine learning can be used to predict and prevent future exploits, by analyzing patterns and trends in data, it can identify potential vulnerabilities before they are exploited. This allows developers to take proactive measures to mitigate these vulnerabilities, making Web3 projects safer for users.”

ML Only as good as the training

ML models are only as good as the information datasets they are trained on, so it is important to ensure that the models are trained on a diverse and (statistically) representative set of data in order to improve their ability to detect and prevent exploits , to improve.

“The use of AI/ML in cybersecurity can be a double-edged sword. On the one hand, it can significantly reduce the time in detecting threats and allow cybersecurity professionals to focus on the activity that is more likely than not malicious. But too heavy reliance on these systems can lead to an increase in advanced and sophisticated attacks that can evade AI/ML systems,” said David Schwed, COO of blockchain security firm Halborn.

Forta, Halborn, and a number of other cybersecurity firms like Cyware Labs are making the case for ML to protect the burgeoning Web3 ecosystem from third-party threats. Booz Allen Hamilton, the US government and military contractor specializing in intelligence, has used ML technologies to effectively replace human security resources, enabling researchers to maximize their work efficiency.

Darktrace, a British-American company specializing in cyber defense, uses ML-based immunity solutions to protect its customers. The firm thwarted a WannaCry ransomware attack that has so far affected more than 200,000 people (in more than 100 countries) using this technology.

Although machine learning can be used to improve the security of Web3, it is not a foolproof solution. Blockchain security is a constantly evolving field, and new types of cyber attacks are coming to the front line every day. ML tools can be exploited by cybercriminals, and while the technology can be used to detect and prevent known types of cyberattacks, it is often not as effective in protecting against unknown or previously unseen types of attacks.

As cyberattacks continue to become more sophisticated in both their designs and intended outcomes, ML is positioned to play an important role in helping to better secure the Web3 metaverse. ChatGPT’s impressive results have already seen record investments in AI-based technology and the competition has turned up the heat on traditional search firms.

It won’t be long before cybersecurity platforms deploy greater use of heuristics to thwart cybercriminals equally matched with the same technology. Only time will tell how much more effective this “AI layer” of smart security will be in reducing cybercrime. Let’s hope we can change the outcome of this zero sum game in favor of citizens and businesses and not cybercriminals.