Adversary modeling is the process of identifying and characterizing potential threats to a digital system or network. It involves analyzing the motivations, capabilities, and likely actions of malicious actors to predict and mitigate potential security breaches. This systematic approach aids in developing robust defenses by anticipating attack vectors and understanding attacker methodologies within the cryptocurrency ecosystem.
Context
The ongoing evolution of cyber threats necessitates a dynamic approach to adversary modeling within the blockchain and digital asset space. Current discussions often center on state-sponsored actors, sophisticated decentralized application exploits, and the potential for novel attack vectors targeting emerging DeFi protocols. Vigilance regarding these evolving threat landscapes is paramount for maintaining network integrity and user confidence.
This research establishes a foundational theory for Maximal Extractable Value, enabling rigorous security proofs and clearer distinction between beneficial and exploitative value extraction in decentralized systems.
This theory formally defines Maximal Extractable Value, offering a robust framework for proving smart contract security and clarifying adversarial extraction in blockchains.
This research establishes a rigorous, abstract theory of MEV, enabling formal security proofs against economic attacks that exploit transaction ordering.
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