An automatic algorithm of identifying vulnerable spots of internet data center power systems based on reinforcement learning

Exploring the Optimum Proactive Defense Strategy for the Power Systems from an Attack Perspective

“…e construction process of the network attack and defense model is the same as that of the host, and the cost of both sides is exactly the same as the matrix (1). Considering the openness and accessibility of the network, the extent of injury from an attacker via the network is slightly higher than that of the host, so its specific revenue is shown in the following matrix:…”

“…e energy systems, the only channels for energy transmission, are responsible for the stable transmission of energy. As the primary branch of the energy systems, the power systems have been the focus to be assaulted in recent years [1]; in 2010, a power plant in Iran was attacked by the Stuxnet virus, which made the Iranian nuclear power plant lose its power generation capacity for a short time [2]; in 2014, the malicious software Black Energy invaded into USA power turbines during which USA power systems suffered a total of no less than 79 hacker attacks; in 2015, Ukrainian power systems were attacked by a malicious code, which caused a large-scale blackout; in 2016, a great many computers of the power systems, attacked in Israel by hackers, were in a suspended state; in 2019, many major hydropower stations in Venezuela were under cyber-attack, which occurred in more than half of the regions with a large-scale power outage for more than 6 days. e fundamental reason that energy systems such as the power systems can be frequently attacked successfully is that the protection strategy of each system is passive and static and it does not have an autoimmune function [3].…”

Vulnerability assessment of power system based on Theil entropy of branch potential energy

“…So in order to analyze the vulnerability of the power system in real time, [11] proposed an enhanced potential state algorithm based on the relaxation theory to judge whether the saturated cut sets are generated or not after the system failure. In [12], a parallel fault evolution model (PFEM) was proposed to accelerate the fault evolution process of the power system, and reinforcement learning technology was used to identify the vulnerability of power grid. State vulnerability is mainly used to evaluate the risk level of the power grid by element operation status under a certain operation mode.…”