Bet and Attack: Incentive Compatible Collaborative Attacks Using Smart Contracts

TL;DR

This paper introduces a novel smart contract-based framework enabling collaborative attacks through betting, incentivizing attackers without needing to quantify individual contributions, demonstrated via DDoS attack case studies.

Contribution

It presents a new trustless collaboration scheme using betting, along with a formal game-theoretic model and incentive mechanism for orchestrating and rewarding collaborative attacks.

Findings

Attackers collaborate proportionally to their bets at equilibrium.

The incentive mechanism is strategy-proof and budget-balanced.

Numerical simulations confirm the theoretical equilibrium behavior.

Abstract

Smart contract-enabled blockchains allow building decentralized applications in which mutually-distrusted parties can work together. Recently, oracle services emerged to provide these applications with real-world data feeds. Unfortunately, these capabilities have been used for malicious purposes under what is called criminal smart contracts. A few works explored this dark side and showed a variety of such attacks. However, none of them considered collaborative attacks against targets that reside outside the blockchain ecosystem. In this paper, we bridge this gap and introduce a smart contract-based framework that allows a sponsor to orchestrate a collaborative attack among (pseudo)anonymous attackers and reward them for that. While all previous works required a technique to quantify an attacker's individual contribution, which could be infeasible with respect to real-world targets, our framework avoids that. This is done by developing a novel scheme for trustless collaboration through betting. That is, attackers bet on an event (i.e., the attack takes place) and then work on making that event happen (i.e., perform the attack). By taking DDoS as a usecase, we formulate attackers' interaction as a game, and formally prove that these attackers will collaborate in proportion to the amount of their bets in the game's unique equilibrium. We also model our framework and its reward function as an incentive mechanism and prove that it is a strategy proof and budget-balanced one. Finally, we conduct numerical simulations to demonstrate the equilibrium behavior of our framework.