Optimizing Interconnectivity among Networks under Attacks

Networks may need to be interconnected for various reasons such as inter-organizational communication, redundant connectivity, increasing data-rate and minimizing delay or packet-loss, etc. However, the trustworthiness of an added interconnection link cannot be taken for granted due to the presence of attackers who may compromise the security of an interconnected network by intercepting the interconnections. Namely, an intercepted interconnection link may not be secured due to the data manipulations by attackers. In the first part of this dissertation, the number of interconnections between the two networks is optimized for maximizing the data-rate and minimizing the packet-loss under the threat of security attacks. The optimization of the interconnectivity considering the security attack is formulated using a rate-distortion optimization setting, as originally introduced by Claude E. Shannon in the information theory. In particular, each intercepted interconnection is modeled as a noisy communication channel where the attackers may manipulate the data by flipping and erasing of data bits, and then the total capacity for any given number of interconnections is calculated. By exploiting such formulation, the optimal number of interconnections between two networks is found under network administrators data-rate and packet-loss requirement, and most importantly, without compromising the data security. It is concluded analytically and verified by simulations under certain conditions, increasing interconnections beyond an optimal number would not be beneficial concerning the data-rates and packet-loss. In the second part of this dissertation, the vulnerability of the interconnected network is analyzed by a probabilistic model that maps the intensity of physical attacks to network component failure distributions. Also, assuming the network is susceptible to the attack propagation, the resiliency of the network is modeled by the influence model and epidemic model. Finally, a stochastic model is proposed to track the node failure dynamics in a network considering dependency with power failures. Besides, the cascading failure in the power grid is analyzed with a data-driven model that reproduces the evolution of power-transmission line failure in power grids. To summarize, the optimal interconnectivity among networks is analyzed under security attacks, and the dynamic interactions in an interconnected network are investigated under various physical and logical attacks. The proper application of this work would add the minimum number of inter-network connections between two networks without compromising the data security. The optimal number interconnections would meet network administrator’s requirement and minimize cost (both security and monetary) associated with unnecessary connections. This work can also be used to estimate the reliability of a communication network under different types of physical attacks independently and also by incorporating the dynamics of power failures

Predicting Cascading Failures in Power Grids using Machine Learning Algorithms

Although there has been notable progress in modeling cascading failures in power grids, few works included using machine learning algorithms. In this paper, cascading failures that lead to massive blackouts in power grids are predicted and classified into no, small, and large cascades using machine learning algorithms. Cascading-failure data is generated using a cascading failure simulator framework developed earlier. The data set includes the power grid operating parameters such as loading level, level of load shedding, the capacity of the failed lines, and the topological parameters such as edge betweenness centrality and the average shortest distance for numerous combinations of two transmission line failures as features. Then several machine learning algorithms are used to classify cascading failures. Further, linear regression is used to predict the number of failed transmission lines and the amount of load shedding during a cascade based on initial feature values. This data-driven technique can be used to generate cascading failure data set for any real-world power grids and hence, power-grid engineers can use this approach for cascade data generation and hence predicting vulnerabilities and enhancing robustness of the grid

Efficient Interconnectivity Among Networks Under Security Constraint

Interconnectivity among networks is essential for enhancing communication capabilities of networks such as the expansion of geographical range, higher data rate, etc. However, interconnections may initiate vulnerability (e.g., cyber attacks) to a secure network due to introducing gateways and opportunities for security attacks such as malware, which may propagate from the less secure network. In this paper, the interconnectivity among subnetworks is maximized under the constraint of security risk. The dynamics of propagation of security risk is modeled by the evil-rain influence model and the SIR (Susceptible-Infected-Recovered) epidemic model. Through extensive numerical simulations using different network topologies and interconnection patterns, it is shown that the efficiency of interconnectivity increases nonlinearly and vulnerability increases linearly with the number of interconnections among subnetworks. Finally, parametric models are proposed to find the number of interconnections for any given efficiency of interconnectivity and vulnerability of the secure network

Balancing Smart Grid\u27s Performance Enhancement and Resilience to Cyber Threat

The strong interplay between the power grid and the corresponding communication and control network plays a pivotal role in the resilience of the smart grid. In this paper, the dynamics of the interdependence among smart-grid subsystems such as the power grid, communication network, and response of human operators are captured during the propagation of cascading failures. A previously developed Markov-chain based model is refined into an interdependent Markov chain model to capture the role of cyber threat from the communication network and the human-operator error during cascading failures. The state transitions of the Markov chain are parameterized by the critical operating parameters of the power grid. The calculations assume a generic form of correlation between the level of and damage from cyber-attacks, on the one hand, and the level of interdependence on the other hand. The model finds the optimal level of interdependence, i.e., the trade-off between well-informed control and vulnerability to attacks that minimizes the probability of massive cascading failures in power grids. There is a point of diminishing returns beyond which the harm of exposure to cyber threat outweighs the benefits of information

Correlating Grid-operators\u27 Performance with Cascading Failures in Smart-Grids

In this paper, the role of human factors associated with the grid operators, e.g., human-error probability (HEP) are modeled as a function of the grid conditions as well as operators\u27 training and experience levels. Moreover, the HEP is embedded in a previously reported Markov-chain model that generates the probability distribution of blackout as a function of time following a trigger. Specifically, through the HEP, the Markov-chains transition matrix includes the dynamics of detailed smart-grid operator attributes. To derive the grid-state dependent HEP, three real-valued performance shaping factors (PSFs), representing key human attributes of the operators, are mapped to the grid-state variables, thereby capturing the correlation between the evolution of the PSF levels and the propagation of transmission-line failures. This mapping is established based on a histogram-equalization principle, which utilizes the experimentally-estimated probability distribution of the PSF levels while assuming a monotone relationship between the HEP values and number of line failures. Further, the distribution of the PSF levels was used to identify the critical combinations of PSF levels that corresponds to an event with high joint probability as well as a high HEP

Mitigating Cascading Failures in Power Grids via Markov Decision-Based Load-Shedding With DC Power Flow Model

Despite the reliability of modern power systems, large blackouts due to cascading failures (CFs) do occur in power grids with enormous economic and societal costs. In this article, CFs in power grids are theoretically modeled proposing a Markov decision process (MDP) framework with the aim of developing optimal load-shedding (LS) policies to mitigate CFs. The embedded Markov chain of the MDP, established earlier to capture the dynamics of CFs, features a reduced state-space and state-dependent transition probabilities. We introduce appropriate actions affecting the dynamics of CFs and associated costs. Optimal LS policies are computed that minimize the expected cumulative cost associated with CFs. Numerical simulations on the IEEE 118 and IEEE 300 bus systems show that the actions derived by the MDP result in minimum total cost of CFs, compared to fixed and random policies. Moreover, the optimality of derived policies is validated by a CF simulation based on dc power flow for the IEEE 118 bus system. Therefore, such actions developed by the proposed theoretical MDP framework can serve as a baseline for devising optimal LS strategies to mitigate CFs in power grids

On the Dynamics of Transmission Capacity and Load Loss during Cascading Failures in Power Grids

In this paper, a novel analytical model is proposed to predict the average transmission-capacity loss and load loss during a cascading failure as a function of time and their steady state values. Cascading failures in the power grid are described using a Markov-chain approach, in which the state transition probabilities depend on the number and capacities of the failed lines. The transition matrix is characterized parametrically using Monte Carlo simulations of cascading failures in the power grid. The severity of cascading failure is estimated using two metrics: the expected number of transmission-line failures and the amount of load shedding/load loss (inferred from the average transmission capacity loss) in the steady state. These two metrics provide critical information regarding the severity of a cascading failure in a power grid (in terms of both the distribution of blackout sizes and the amounts of load shedding). One of the benefits of this model is that it enables the understanding of the effect of initial failures and of the operating parameters of the power grid on cascading failures

Data-Integrity Aware Stochastic Model for Cascading Failures in Power Grids

The reliable operation of power grids during cascading failures is heavily dependent on the interdependencies between the power grid components and the supporting communications and control networks. Moreover, the system operators\u27 expertise in dealing with cascading failures can play a pivotal role during contingencies. In this paper, a dynamical probabilistic model is developed based on Markov-chains, which captures the dynamics of cascading failures in the power grid. Specifically, a previously developed Markov-chain based model is extended to capture the trade-off between the benefits of having a robust communication infrastructure and its vulnerability from data integrity (e.g., cyber-attacks). State-space reduction of the complex interactions between power grids, communication networks and system operators is achieved by judiciously specifying the state variables of the Markov chain. The impact of system operators\u27 probability of error during a cascade-mitigation action is incorporated into the model as a function of the state variables of the Markov chain. A point of diminishing returns is observed beyond which the effect of information infidelity outweighs the benefits of having more information. For a given level of cyber threat, an optimal size of a communication network is observed that minimizes the expected number of transmission-line failures before the cascade stops

Impacts of Operators’ Behavior on Reliability of Power Grids During Cascading Failures

Human operators play a key role in the reliable operation of critical infrastructures. However, human operators may take actions that are far from optimum. This can be due to various factors affecting the operators\u27 performance in time-sensitive and critical situations such as reacting to contingencies with significant monetary and social impacts. In this paper, an analytic framework is proposed based on Markov chains for modeling the dynamics of cascading failures in power grids. The model captures the effects of operators\u27 behavior quantified by the probability of human error under various circumstances. In particular, the observations from historical data and information obtained from interviews with power-system operators are utilized to develop the model as well as identify its parameters. In light of the proposed model, the noncritical regions of power-system\u27s operating characteristics with human-factor considerations are characterized under which the probability of large cascading failures is minimized