Resilience of Smart Power Grids to False Pricing Attacks in the Social Network

Abstract

Communication through social media can help engage end users to improve the efficiency of demand-side management in smart power grids. However, this opens a channel between the social network and the power grid through which malicious attackers can publish false information that can actually cause problems to the power grid. In this paper, we analyze this new problem by modeling a social networkcoupled smart grid and investigating its vulnerability to false pricing attacks in the social network. The energy consumption profile based on social information is modeled as a consumption rescheduling problem, which aims to maximize the benefit of demand-side management. The false price spreading process is described by a multi-level influence propagation model, which takes into account the personalities of the end users. Different attack strategies are considered and the power operator's response is modeled. The residual ampacity of distribution lines and the expected energy not supplied are adopted to quantify the impacts of the attacks on the power system. To account for the stochastic characteristics of the influence propagation process, Monte Carlo simulation is utilized. The proposed modeling and analysis framework is applied on a modified IEEE 13 nodes test feeder and a notional social network. The vulnerability to attack is analyzed at both component and system levels.

A Comprehensive Review on Smart Grids: Challenges and Opportunities

“…Neighborhood Area Networks (NAN) [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37] Software Defined Networks (SDN) [20,[38][39][40][41][42][43] Interdependent networks (IN) [44][45][46][47][48][50][51][52][53] Field Area Networks (FAN) [21,49,[54][55][56][57][58][59][60][61][62][63]69] Wireless Sensor Networks (WSN) [64][65][66][67][68][69]...…”

“…Note that 17% can find both in NANs and WSNs, respectively, pointing out a great opportunity to develop smart grids with softwaredefined topologies. Internet of Things [19,23,24,27,28,30,31,36,37,58,64,66,73,74,79,105,107] Machine learning [20,29,32,51,55,56,60,62,69,78,80,81,85,98,102,106] Data mining [47,107] Machine learning and neural training [21,54,69,81,96] Short term memory network [41,61] Power Line Communication Technology [53,63,90] Power electronics [35,50,99,101,<...>…”

“…Table 3 shows that most of the Encryption issues do not clearly define the algorithms they use, although there are some articles that use Multi-dimensional Data aggregation and even Cognitive Risk Control. Multi-dimensional Data aggregation [42,[55][56][57]60,62,64] Cognitive Risk Control [37,82] Not Defined [19,20,[22][23][24]26,28,29,31,35,36,38,39,43,[45][46][47][48]50,[52][53][54]58,61,63,73,[76][77][78][79]81,[98][99][100][101][102][103][104][105]…”

“…Tang et al [62] address in their article the growth of social media to be used to make the management of the end user of electrical energy more efficient. However, attacks on networks so exposed to public view are constant, at the software or hardware level as well as about the information uploaded and published on social media to be prone to be malicious, imprecise information, no confirmed and that it ends up being a fake news, causing serious problems to the electrical network.…”

Social Collective Attack Model and Procedures for Large-Scale Cyber-Physical Systems

“…With the increasing popularity of social networks, the role of social users in CPSs is becoming more prevalent. Reference [ 24 ] analyzed this new problem by modeling a social network-coupled smart grid and investigating its vulnerability to false pricing attacks in the social network. In this case, the intrusion of the social domain led to the security problem of the physical domain.…”