Energy big data: A survey <s> B. INTEGRITY <s> A power grid is a complex system connecting electric power generators to consumers through power transmission and distribution networks across a large geographical area. System monitoring is necessary to ensure the reliable operation of power grids, and state estimation is used in system monitoring to best estimate the power grid state through analysis of meter measurements and power system models. Various techniques have been developed to detect and identify bad measurements, including interacting bad measurements introduced by arbitrary, nonrandom causes. At first glance, it seems that these techniques can also defeat malicious measurements injected by attackers. In this article, we expose an unknown vulnerability of existing bad measurement detection algorithms by presenting and analyzing a new class of attacks, called false data injection attacks, against state estimation in electric power grids. Under the assumption that the attacker can access the current power system configuration information and manipulate the measurements of meters at physically protected locations such as substations, such attacks can introduce arbitrary errors into certain state variables without being detected by existing algorithms. Moreover, we look at two scenarios, where the attacker is either constrained to specific meters or limited in the resources required to compromise meters. We show that the attacker can systematically and efficiently construct attack vectors in both scenarios to change the results of state estimation in arbitrary ways. We also extend these attacks to generalized false data injection attacks, which can further increase the impact by exploiting measurement errors typically tolerated in state estimation. We demonstrate the success of these attacks through simulation using IEEE test systems, and also discuss the practicality of these attacks and the real-world constraints that limit their effectiveness. <s> BIB001 </s> Energy big data: A survey <s> B. INTEGRITY <s> We model smart grids as complex interdependent networks, and study targeted attacks on smart grids for the first time. A smart grid consists of two networks: the power network and the communication network, interconnected by edges. Occurrence of failures (attacks) in one network triggers failures in the other network, and propagates in cascades across the networks. Such cascading failures can result in disintegration of either (or both) of the networks. Earlier works considered only random failures. In practical situations, an attacker is more likely to compromise nodes selectively. We study cascading failures in smart grids, where an attacker selectively compromises the nodes with probabilities proportional to their degrees, high degree nodes are compromised with higher probability. We mathematically analyze the sizes of the giant components of the networks under 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. <s> BIB002
Preventing unauthorized persons or systems modifying information is all about integrity. In the smart grid domain the integrity focuses on information such as sensor values, product recipes and control commands. Avoiding modification through message delay, message reply and message injection are also its relevant work. Security issues may be caused by the violation of integrity. Liu et al. BIB001 pointed out that the risk of integrity-targeting attacks in smart grid is indeed real. Compared to availabilitytargeting attacks, integrity-targeting attacks are less bruteforce but more sophisticated. Either customers' information or network operation information can be the objective of the integrity attacks. That is to say, modifying the original information deliberately and then corrupting critical data exchange in smart grid are the attempt of such attacks. Ruj and Pal BIB002 proposed a targeted attack model in power network where attackers attack nodes by removing them with probability proportional to the nodes' degree. Therefore, a node with higher degree will be removed with higher probability. In the power network, once a node with higher degree has been removed, the integrity of energy data collected by nodes will be affected more. Note ø k as the probability that a node i of k degree hasn't been removed: where V A is the nodes set in power network N A , α A is the power-law coefficient, m A represents the number of edges in N A , deg(i) = Ak −α A represents the degree of node i and α A = 0 means the random removal of nodes.