通过用户侧数据篡改对耦合电-交通网络的网络攻击

The incorporation of advanced information and communication technologies has exposed the power and transportation sectors to cyberattacks. Despite the great efforts in analyzing cyber vulnerabilities of network-level devices, little attention has been paid to the threats from user-side cyber vulnerability. This paper uncovers the strategic behavior of an attacker who aims to devastate the operation of the interdependent power and transportation networks through user-side data falsifications. Specifically, as most travelers rely on navigation Apps that guide their daily trips, an attacker could intrude into end-user devices and falsify the route and charging station recommendations, so as to manipulate the distribution of traffic and charging flows towards insecure network operations. We model the attack decisions as a set of binary variables, which are then relaxed into continuous variables by introducing complementarity constraints. In doing so, the original mixed-integer formulation is recast into a mathematical program with complementarity conditions (MPCC). We then design an enhanced iterative relaxation algorithm that solves the MPCC as a sequence of parameterized nonlinear programming. The enhanced version is equipped with a feedback module that identifies and corrects non-smooth relaxations, which contributes to a smooth convergence towards high-quality solutions. Numerical results corroborate the threat of user-side cyber vulnerability on network operations, and validate the superiority of proposed solution methods.