Biblio

The evolution of electrical grids, both in terms of enhanced ICT functionalities to improve efficiency, reliability and economics, as well as the increasing penetration of renewable redistributed energy resources, results in a more sophisticated electrical infrastructure which poses new challenges from several perspectives, including resilience and quality of service analysis. In addition, the presence of interdependencies, which more and more characterize critical infrastructures (including the power sector), exacerbates the need for advanced analysis approaches, to be possibly employed since the early phases of the system design, to identify vulnerabilities and appropriate countermeasures. In this paper, we outline an approach to model and analyze smart grids and discuss the major challenges to be addressed in stochastic model-based analysis to account for the peculiarities of the involved system elements. Representation of dynamic and flexible behavior of generators and loads, as well as representation of the complex ICT control functions required to preserve and/or re-establish electrical equilibrium in presence of changes need to be faced to assess suitable indicators of the resilience and quality of service of the smart grid.

The vulnerability analysis is vital for safely running power grids. The simultaneous attack, which applies multiple failures simultaneously, does not consider the time domain in applying failures, and is limited to find unknown vulnerabilities of power grid networks. In this paper, we discover a new attack scenario, called the sequential attack, in which the failures of multiple network components (i.e., links/nodes) occur at different time. The sequence of such failures can be carefully arranged by attackers in order to maximize attack performances. This attack scenario leads to a new angle to analyze and discover vulnerabilities of grid networks. The IEEE 39 bus system is adopted as test benchmark to compare the proposed attack scenario with the existing simultaneous attack scenario. New vulnerabilities are found. For example, the sequential failure of two links, e.g., links 26 and 39 in the test benchmark, can cause 80% power loss, whereas the simultaneous failure of them causes less than 10% power loss. In addition, the sequential attack is demonstrated to be statistically stronger than the simultaneous attack. Finally, several metrics are compared and discussed in terms of whether they can be used to sharply reduce the search space for identifying strong sequential attacks.

As information and communication networks are highly interconnected with the power grid, cyber security of the supervisory control and data acquisition (SCADA) system has become a critical issue in the power system. By intruding into the SCADA system via the remote access points, the attackers are able to eavesdrop critical data and reconfigure devices to trip the system breakers. The cyber attacks are able to impact the reliability of the power system through the SCADA system. In this paper, six cyber attack scenarios in the SCADA system are considered. A Bayesian attack graph model is used to evaluate the probabilities of successful cyber attacks on the SCADA system, which will result in breaker trips. A forced outage rate (FOR) model is proposed considering the frequencies of successful attacks on the generators and transmission lines. With increased FOR values resulted from the cyber attacks, the loss of load probabilities (LOLP) in reliability test system 79 (RTS79) are estimated. The results of the simulations demonstrate that the power system becomes less reliable as the frequency of successful attacks increases.

This paper investigates the vulnerability of power grids based on the complex networks combining the information entropy. The difference of current directions for a link is considered, and it is characterized by the information entropy. By combining the information entropy, the electrical betweenness is improved to evaluate the vulnerability of power grids. Attacking the link based on the largest electrical betweenness with the information can get the larger size of the largest cluster and the lower lost of loads, compared with the electrical betweenness without the information entropy. Finally, IEEE 118 bus system is tested to validate the effectiveness of the novel index to characterize the the vulnerability of power grids.

This paper proposes an analysis method of power grids vulnerability based on complex networks. The method effectively combines the degree and betweenness of nodes or lines into a new index. Through combination of the two indexes, the new index can help to analyze the vulnerability of power grids. Attacking the line of the new index can obtain a smaller size of the largest cluster and global efficiency than that of the pure degree index or betweenness index. Finally, the fault simulation results of IEEE 118 bus system show that the new index can reveal the vulnerability of power grids more effectively.

Outsourcing spatial databases to the cloud provides an economical and flexible way for data owners to deliver spatial data to users of location-based services. However, in the database outsourcing paradigm, the third-party service provider is not always trustworthy, therefore, ensuring spatial query integrity is critical. In this paper, we propose an efficient road network k-nearest-neighbor query verification technique which utilizes the network Voronoi diagram and neighbors to prove the integrity of query results. Unlike previous work that verifies k-nearest-neighbor results in the Euclidean space, our approach needs to verify both the distances and the shortest paths from the query point to its kNN results on the road network. We evaluate our approach on real-world road networks together with both real and synthetic points of interest datasets. Our experiments run on Google Android mobile devices which communicate with the service provider through wireless connections. The experiment results show that our approach leads to compact verification objects (VO) and the verification algorithm on mobile devices is efficient, especially for queries with low selectivity.

The notion of trust is considered to be the cornerstone on patient-psychiatrist relationship. Thus, a trustfully background is fundamental requirement for provision of effective Ubiquitous Healthcare (UH) service. In this paper, the issue of Trust Evaluation of UH Providers when register UH environment is addressed. For that purpose a novel trust evaluation method is proposed, based on cloud theory, exploiting User Profile attributes. This theory mimics human thinking, regarding trust evaluation and captures fuzziness and randomness of this uncertain reasoning. Two case studies are investigated through simulation in MATLAB software, in order to verify the effectiveness of this novel method.

Cyber systems play a critical role in improving the efficiency and reliability of power system operation and ensuring the system remains within safe operating margins. An adversary can inflict severe damage to the underlying physical system by compromising the control and monitoring applications facilitated by the cyber layer. Protection of critical assets from electronic threats has traditionally been done through conventional cyber security measures that involve host-based and network-based security technologies. However, it has been recognized that highly skilled attacks can bypass these security mechanisms to disrupt the smooth operation of control systems. There is a growing need for cyber-attack-resilient control techniques that look beyond traditional cyber defense mechanisms to detect highly skilled attacks. In this paper, we make the following contributions. We first demonstrate the impact of data integrity attacks on Automatic Generation Control (AGC) on power system frequency and electricity market operation. We propose a general framework to the application of attack resilient control to power systems as a composition of smart attack detection and mitigation. Finally, we develop a model-based anomaly detection and attack mitigation algorithm for AGC. We evaluate the detection capability of the proposed anomaly detection algorithm through simulation studies. Our results show that the algorithm is capable of detecting scaling and ramp attacks with low false positive and negative rates. The proposed model-based mitigation algorithm is also efficient in maintaining system frequency within acceptable limits during the attack period.

Control system networks are increasingly being connected to enterprise level networks. These connections leave critical industrial controls systems vulnerable to cyber-attacks. Most of the effort in protecting these cyber-physical systems (CPS) from attacks has been in securing the networks using information security techniques. Effort has also been applied to increasing the protection and reliability of the control system against random hardware and software failures. However, the inability of information security techniques to protect against all intrusions means that the control system must be resilient to various signal attacks for which new analysis methods need to be developed. In this paper, sensor signal attacks are analyzed for observer-based controlled systems. The threat surface for sensor signal attacks is subdivided into denial of service, finite energy, and bounded attacks. In particular, the error signals between states of attack free systems and systems subject to these attacks are quantified. Optimal sensor and actuator signal attacks for the finite and infinite horizon linear quadratic (LQ) control in terms of maximizing the corresponding cost functions are computed. The closed-loop systems under optimal signal attacks are provided. Finally, an illustrative numerical example using a power generation network is provided together with distributed LQ controllers.