This week in Forta… things are a little different! To celebrate the 50th edition of the Forta Newsletter, this issue will highlight some of the most popular and innovative articles published on the Forta blog.
In December of 2022, Forta published “How Forta’s Predictive ML Models Detect Attacks Before Exploitation”, an article that represents one of the core research tenets of Forta’s research efforts. The work done here, using machine learning to analyze opcode and decide if a contract is malicious, forms the basis of Forta’s Attack and Scam detector’s ability to detect attacks before exploitation.
In March of 2023, the above concept of detecting hacks before exploitation was put to the test with the $197M Euler Finance hack. Forta’s early detection system passed with flying colors, detecting the catastrophic hack before exploitation and at each stage of the attack. Forta researchers published a comprehensive writeup of the attack on the Forta blog and the example remains a gold standard of comprehensive hack detection.
One of Forta’s fan favorite articles was its most controversial, “How AI based Threat Detection Can Save Web3”. Detailing the importance of early detection and the necessity for a distributed monitoring layer, this blog is a must read for anyone who needs a primer on the Forta Network. By leveraging machine learning and a decentralized network with a powerful FORT token based incentive mechanism, web3’s defenders will be able to create a world without on-chain scams and hacks.
In June of 2023, Forta researchers made a discovery: Sleepdrops. This new scam type impersonates legitimate tokens and airdrops them to unsuspecting users from a mimicked ‘legitimate address’, creating an illusion of security. Once users connect their wallets to the scam’s website, they’re tricked into signing a transaction that results in a native ice phishing scam. In collaboration with Blockfence, Forta researchers analyzed the scam and published their findings in a comprehensive writeup that showed over $11.5M in user funds had been stolen.
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