Scanning Accuracy

Improving the bug-finding accuracy of Otacon involves a robust strategy of fine-tuning its Large Language Models on a diverse and extensive dataset.

By utilizing a dataset of 9,000 vulnerable smart contracts, Otacon can deeply understand the patterns and commonalities associated with various types of vulnerabilities.

Otacon will also rely on 1,000 security audits from the top 50 smart contract security firms and independent Web 3.0 auditors, ensuring that the model learns from the highest standards of current security practices.

Additionally, integrating models trained on Graph Neural Networks (GNNs) could exploit the relational data between contract components, effectively detecting vulnerabilities arising from complex interactions within contracts. These models analyze the nodes (representing contract elements) and edges (representing interactions) in a graph, providing a sophisticated method for uncovering deep-seated and non-obvious vulnerabilities.

This approach ensures that Otacon remains at the forefront of cybersecurity technology, providing a robust, proactive defense mechanism against the increasingly sophisticated landscape of smart contract vulnerabilities, before tackling other classes of networks and software involving legacy technologies.