We study cascading failures in smart grids, where an attacker selectively compromises the nodes with probabilities proportional to their degrees, betweenness, or clustering coefficient. This implies that nodes with high degrees, betweenness, or clustering coefficients are attacked with higher probability. We mathematically and experimentally analyze the sizes of the giant components of the networks under different types of targeted attacks, and compare the results with the corresponding sizes under random attacks. We show that networks disintegrate faster for targeted attacks compared to random attacks. A targeted attack on a small fraction of high degree nodes disintegrates one or both of the networks, whereas both the networks contain giant components for random attack on the same fraction of nodes. An important observation is that an attacker has an advantage if it compromises nodes based on their betweenness, rather than based on degree or clustering coefficient. We next study adaptive attacks, where an attacker compromises nodes in rounds. Here, some nodes are compromised in each round based on their degree, betweenness or clustering coefficients, instead of compromising all nodes together. In this case, the degree, betweenness, or clustering coefficient is calculated before the start of each round, instead of at the beginning. We show experimentally that an adversary has an advantage in this adaptive approach, compared to compromising the same number of nodes all at once.
翻译:在智能网格中,一个攻击者有选择地使节点的概率与其程度、程度或组合系数成正比,从而有选择地使节点的概率与它们之间或组合系数成正比。这意味着高度、中间或组合系数的节点受到攻击的概率较高。我们用数学和实验方法分析不同类型目标攻击中网络巨型组成部分的大小,并将结果与随机攻击中相应大小进行比较。我们显示,与随机攻击相比,网络对目标攻击的频率变化速度较快。对某一小部分高节点或两个网络的定向攻击使一个或两个网络解体,而这两个网络都包含随机攻击的巨型组成部分。一个重要的观察是,攻击者如果根据它们之间的间隔而不是程度或组合系数而损害节点,则具有优势。我们接下来研究适应性攻击者攻击者使圆形节点受损的情况。在这里,一些节点根据它们的程度、中间或组合系数,而不是一起损害所有节点。在这种情况下,在本次案例中,攻击者具有优势的程度,先是实验性,然后是计算出每个实验性稳度的优势,然后是计算出每个基数。