Sparsity and Coordination Constraints on Stealth Data Injection Attacks

In this thesis, data injection attacks (DIAs) to smart grid within Bayesian framework is studied from two perspectives: centralized and decentralized systems. The fundamental limits of the data injection attacks are characterized by the information measures. Specifically, two metrics, mutual information and the Kullback-Leibler (KL) divergence, quantifies the disruption caused by the attacks and the corresponding stealthiness, respectively. From the perspective of centralized system, a unique attacker constructs the attacks that jointly minimize the mutual information acquired from the measurements about the state variables and the KL divergence between the distribution of measurements with and without attacks. One of the main contributions in the centralized attack construction is the sparsity constraints. Two scenarios where the attacks between different locations are independent and correlated are studied, respectively. In independent attacks, the challenge of the combinatorial character of identifying the support of the sparse attack vector is circumvented by obtaining the closed-form solution to single measurement attack problem followed by a greedy construction that leverages the insight distilled. In correlated attacks, the challenge is tackled by incorporating an additional measurement that yields sequential sensor selection problem. The sequential procedure allows the attacker to identify the additional sensor first and character the corresponding covariances between the additional measurement and the compromised measurements. Following the studies on sparse attacks, a novel metric that describes the vulnerability of the measurements on smart grids to data integrity attacks is proposed. The new metric, coined vulnerability index (VuIx), leverages information theoretic measures to assess the attack effect on the fundamental limits of the disruption and detection tradeoff. The result of computing the VuIx of the measurements in the system yields an ordering of the measurements vulnerability based on the level of the exposure to data integrity attacks. The assessment on the measurements vulnerability of IEEE test systems observes that power injection measurements are overwhelmingly more vulnerable to data integrity attacks than power flow measurements. From the perspective of decentralized system, the attack constructions are determined by a group of attackers in a cooperative manner. The interaction between the attackers is formulated as a game with a normal form. The uniqueness of the Nash Equilibrium (NE) is characterized in different games where the attackers have different objectives. Closed-form expression for the best response of the attackers in different games are obtained and followed by best response dynamics that leads to the NEs. The sparsity constraint is considered in decentralized system where the attackers have limited access to sensors. The attack construction with sparsity constraints in decentralized system is also formulated as a game with a normal form. The uniqueness of the NE and the closed-form expression for the best response are obtained

Effects of bolt slippage on the wind induced responses of transmission tower line system

The wind induced responses of transmission tower line system are studied by finite element method. Firstly, a slip model considering eccentricity and bolt joint slippage in diagonal bracings, tower legs and tower head is built by ANSYS. The slip model has a more accurate result compared with conventional models. Secondly, the finite element models of tower line systems are established and the wind speed time histories are simulated using MATLAB. Finally, the wind induced responses of different tower line systems are studied. The results of a single tower and the tower line systems are compared to study the effects of tower-line coupling effects and bolt slippage on wind induced responses of transmission tower line systems

Information Theoretic Data Injection Attacks with Sparsity Constraints

Information theoretic sparse attacks that minimize simultaneously the information obtained by the operator and the probability of detection are studied in a Bayesian state estimation setting. The attack construction is formulated as an optimization problem that aims to minimize the mutual information between the state variables and the observations while guaranteeing the stealth of the attack. Stealth is described in terms of the Kullback-Leibler (KL) divergence between the distributions of the observations under attack and without attack. To overcome the difficulty posed by the combinatorial nature of a sparse attack construction, the attack case in which only one sensor is compromised is analytically solved first. The insight generated in this case is then used to propose a greedy algorithm that constructs random sparse attacks. The performance of the proposed attack is evaluated in the IEEE 30 Bus Test Case.Comment: Submitted to SGC 202

Power Injection Measurements are more Vulnerable to Data Integrity Attacks than Power Flow Measurements

A novel metric that describes the vulnerability of the measurements in power system to data integrity attacks is proposed. The new metric, coined vulnerability index (VuIx), leverages information theoretic measures to assess the attack effect on the fundamental limits of the disruption and detection tradeoff. The result of computing the VuIx of the measurements in the system yields an ordering of the measurements vulnerability based on the level of exposure to data integrity attacks. This new framework is used to assess the measurements vulnerability of IEEE test systems and it is observed that power injection measurements are overwhelmingly more vulnerable to data integrity attacks than power flow measurements. A detailed numerical evaluation of the VuIx values for IEEE test systems is provided.Comment: 6 pages, 9 figures, Submitted to IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grid

An information theoretic vulnerability metric for data integrity attacks on smart grids

A novel metric that describes the vulnerability of the measurements in power systems to data integrity attacks is proposed. The new metric, coined vulnerability index (VuIx), leverages information theoretic measures to assess the attack effect on the fundamental limits of the disruption and detection tradeoff. The result of computing the VuIx of the measurements in the system yields an ordering of their vulnerability based on the level of exposure to data integrity attacks. This new framework is used to assess the measurement vulnerability of IEEE 9-bus and 30-bus test systems and it is observed that power injection measurements are overwhelmingly more vulnerable to data integrity attacks than power flow measurements. A detailed numerical evaluation of the VuIx values for IEEE test systems is provided.Comment: 7 pages, 10 figures, submitted to IET Smart Grid. arXiv admin note: substantial text overlap with arXiv:2207.0697

Information Theoretic Data Injection Attacks with Sparsity Constraints

International audienceInformation theoretic sparse attacks that minimize simultaneously the information obtained by the operator and the probability of detection are studied in a Bayesian state estimation setting. The attack construction is formulated as an optimization problem that aims to minimize the mutual information between the state variables and the observations while guaranteeing the stealth of the attack. Stealth is described in terms of the Kullback-Leibler (KL) divergence between the distributions of the observations under attack and without attack. To overcome the difficulty posed by the combinatorial nature of a sparse attack construction, the attack case in which only one sensor is compromised is analytically solved first. The insight generated in this case is then used to propose a greedy algorithm that constructs random sparse attacks. The performance of the proposed attack is evaluated in the IEEE 30 Bus Test Case

Stealth Data Injection Attacks with Sparsity Constraints

In this report, sparse stealth attack constructions that minimize the mutual information between the state variables and the observations are proposed. The attack construction is formulated as the design of a multivariate Gaussian distribution aiming to minimize the mutual information while limiting the Kullback-Leibler divergence between the distribution of the observations under attack and the distribution of the observations without attack. The sparsity constraint is incorporated as a support constraint of the attack distribution. Two heuristic greedy algorithms for the attack construction are proposed. The first algorithm assumes that the attack vector consists of independent entries, and therefore, requires no communication between different attacked locations. The second algorithm considers correlations between the attack vector entries, which results in larger disruption and smaller probability of detection. A performance analysis of the proposed attack constructions on IEEE test systems is presented. Using a numerical example, it is shown that it is feasible to construct stealth attacks that generate significant disruption with a low number of compromised sensors.Dans ce rapport, des constructions d’attaques furtives ciblant un sous-ensemble des capteurs qui minimisent l’information mutuelle entre les variables d’état et les observations sont proposées. La construction d’attaque est formulée comme la conception d’une distribution gaussienne multivariée visant à minimiser l’information mutuelle tout en limitant la divergence de Kullback-Leibler entre la distribution des observations sous attaque et la distribution des observations sans attaque. La contrainte pour modeliser le fait que l’attaque cible uniquement un sous-ensemble des capteurs est incorporée en tant que contrainte sur le support de la distribution de probabilité de l’attaque. Deux algorithmes heuristiques gloutons pour la construction d’attaques sont proposés. Le premier algorithme suppose que le vecteur d’attaque se compose d’entrées indépendantes et, par conséquent, ne nécessite aucune communication entre les différents emplacements attaqués. Le deuxième algorithme prend en compte les corrélations entre les entrées du vecteur d’attaque, ce qui entraîne une perturbation plus importante et une probabilité de détection plus faible. Une analyse des performances des constructions d’attaque proposées sur les systèmes de test IEEE est présentée. À l’aide d’un exemple numérique, il est démontré qu’il est possible de construire des attaques furtives qui génèrent des perturbations importantes avec un faible nombre de capteurs compromis

Stealth Data Injection Attacks with Sparsity Constraints

Submitted to IEEE Transactions on Smart GridsSparse stealth attack constructions that minimize the mutual information between the state variables and the observations are proposed. The attack construction is formulated as the design of a multivariate Gaussian distribution that aims to minimize the mutual information while limiting the Kullback-Leibler divergence between the distribution of the observations under attack and the distribution of the observations without attack. The sparsity constraint is incorporated as a support constraint of the attack distribution. Two heuristic greedy algorithms for the attack construction are proposed. The first algorithm assumes that the attack vector consists of independent entries, and therefore, requires no communication between different attacked locations. The second algorithm considers correlation between the attack vector entries and achieves a better disruption to stealth tradeoff at the cost of requiring communication between different locations. We numerically evaluate the performance of the proposed attack constructions on IEEE test systems and show that it is feasible to construct stealth attacks that generate significant disruption with a low number of compromised sensors

Stealth Data Injection Attacks with Sparsity Constraints

In this report, sparse stealth attack constructions that minimize the mutual information between the state variables and the observations are proposed. The attack construction is formulated as the design of a multivariate Gaussian distribution aiming to minimize the mutual information while limiting the Kullback-Leibler divergence between the distribution of the observations under attack and the distribution of the observations without attack. The sparsity constraint is incorporated as a support constraint of the attack distribution. Two heuristic greedy algorithms for the attack construction are proposed. The first algorithm assumes that the attack vector consists of independent entries, and therefore, requires no communication between different attacked locations. The second algorithm considers correlations between the attack vector entries, which results in larger disruption and smaller probability of detection. A performance analysis of the proposed attack constructions on IEEE test systems is presented. Using a numerical example, it is shown that it is feasible to construct stealth attacks that generate significant disruption with a low number of compromised sensors.Dans ce rapport, des constructions d’attaques furtives ciblant un sous-ensemble des capteurs qui minimisent l’information mutuelle entre les variables d’état et les observations sont proposées. La construction d’attaque est formulée comme la conception d’une distribution gaussienne multivariée visant à minimiser l’information mutuelle tout en limitant la divergence de Kullback-Leibler entre la distribution des observations sous attaque et la distribution des observations sans attaque. La contrainte pour modeliser le fait que l’attaque cible uniquement un sous-ensemble des capteurs est incorporée en tant que contrainte sur le support de la distribution de probabilité de l’attaque. Deux algorithmes heuristiques gloutons pour la construction d’attaques sont proposés. Le premier algorithme suppose que le vecteur d’attaque se compose d’entrées indépendantes et, par conséquent, ne nécessite aucune communication entre les différents emplacements attaqués. Le deuxième algorithme prend en compte les corrélations entre les entrées du vecteur d’attaque, ce qui entraîne une perturbation plus importante et une probabilité de détection plus faible. Une analyse des performances des constructions d’attaque proposées sur les systèmes de test IEEE est présentée. À l’aide d’un exemple numérique, il est démontré qu’il est possible de construire des attaques furtives qui génèrent des perturbations importantes avec un faible nombre de capteurs compromis

TiO2 Nanoparticles Functionalized Sn/3.0Ag/0.5Cu Lead-free Solder

As the development of micro-systems moves towards higher speed and miniaturization, the requirement for interconnection density increases significantly. However, the use of conventional solders will be limited as the increasing I/O density lowers the pitches to very small size. Recently, there have been great developments in nano-composite solders. This paper investigated the influence of nano-titanium dioxide (TiO 2 ) of 99.9% purity on the wettability, micro-structural, melting and mechanical properties of Sn/3.0Ag/0.5Cu. The composite solder was prepared by mechanically mixing solder paste with TiO 2 nanoparticles for 30 minutes. The TiO 2 nanoparticles, with average diameter of 10 nm, were manufactured by precipitation. The solder paste was SAC305 Type 4. After reflow soldering the wetting angle was measured. The microstructure of the composite solders and TiO 2 was studied by means of scanning electron microscope (SEM). An optical microscope (OM) was employed to observe the fracture mode after shear test. A pull test was performed to investigate the shear strength of all samples with composite solders between two PCBs with Sn coating, both before and after thermal cycling (TC) with range between -40\ub0C and 85\ub0C. Differential scanning caborimetry (DSC) was adopted to analyze the melting point of composite solders. The results indicate that when the TiO 2 content increased from 0.5% to 2%, the wettability of the solder improved, which resulted in higher shear strength and better mechanical behavior. \ua9 2012 IEEE