Exploratory analysis of high-resolution power interruption data reveals spatial and temporal heterogeneity in electric grid reliability

Modern grid monitoring equipment enables utilities to collect detailed records of power interruptions. These data are aggregated to compute publicly reported metrics describing high-level characteristics of grid performance. The current work explores the depth of insights that can be gained from public data, and the implications of losing visibility into heterogeneity in grid performance through aggregation. We present an exploratory analysis examining three years of high-resolution power interruption data collected by archiving information posted in real-time on the public-facing website of a utility in the Western United States. We report on the size, frequency and duration of individual power interruptions, and on spatio-temporal variability in aggregate reliability metrics. Our results show that metrics of grid performance can vary spatially and temporally by orders of magnitude, revealing heterogeneity that is not evidenced in publicly reported metrics. We show that limited access to granular information presents a substantive barrier to conducting detailed policy analysis, and discuss how more widespread data access could help to answer questions that remain unanswered in the literature to date. Given open questions about whether grid performance is adequate to support societal needs, we recommend establishing pathways to make high-resolution power interruption data available to support policy research.Comment: Journal submission (in review), 22 pages, 8 figures, 1 tabl

A Geographic Modeling Framework for Assessing Critical Infrastructure Vulnerability: Energy Infrastructure Case Study

Vulnerability of critical infrastructure systems is of the utmost importance to a nation\u27s national security interests, especially the electric grid. Despite the importance of these systems, disruptions continue to occur at an alarming rate, thus indicating that there is a fundamental flaw in the way critical infrastructure systems are analyzed for vulnerability. Critical infrastructure systems are typically analyzed using mathematical approaches such as graph theory, which strip systems of their important geographic information, and only look at their connections to each other. While these relationships and metrics provide useful information, they cannot provide the entire picture. As such, this research seeks to develop a new, geographic framework that not only takes into account the information uncovered by graph metrics, but information about the unique geography of the area that can impact these systems. Using Southeast Asia as a study region, this research seeks to answer the following broad questions: 1. What are differences that arise from analyzing energy network vulnerability using the new geographic framework versus graph theory alone? 2. What types of evaluation methods are applicable for determining if the proposed framework is more effective than graph theory? To answer these questions, this research developed a field-based model utilizing service areas as the unit of analysis. The factors of betweenness, degree, closeness, land use, service area population, other critical infrastructure frequency, natural hazard frequency, and temperature extremes. These factors were ranked from one to five, one indicating the least vulnerability and five indicating the highest vulnerability. These factors were then weighted, using the Analytic Hierarchy Process to determine the weights, and summed to determine an overall vulnerability ranking. The higher the score, the more vulnerable a particular substation is. The results indicate that many of these variables provide little insight into the vulnerability of the electric grid, when validated against real-world data from the 2012 Indian Blackout. The most important variables were betweenness, land use, natural hazard frequency, and temperature extremes. Basic metrics of percentage of substations identified versus substations affected in a real-world scenario provided the basis for effective evaluation of each model

Detection and Prevention of Unknown Vulnerabilities on Enterprise IP Networks

Computer networks have long become the backbone of Enterprise Information System. The substantial share of the security problems are still encountered in Enterprise Network. Cyber espionage can effect Ethical, Military, Political and Economic interest anywhere. To provide secure computer networks, it is necessary to measure the relative effectiveness of security solution in the network. A network security metric enable a direct measurement and comparison of the amounts of security provided by different security solutions .In this paper we propose a novel security metric Zero Day Vulnerability Prevention Framework consists of bunches of algorithms. The above framework detects and prevents unknown vulnerabilities in Enterprise IP networks. It also protects the behavior of the sessions performed by the user from the huge range of attacks. It helps in monitoring database requests and prevents the attacks. The proposed framework also implements worm and virus detection to evaluate malware from the data. The system also presents scoring to the vulnerabilities and finally it performs security analysis with the help of Topological Vulnerability Analysis (TVA) tool. DOI: 10.17762/ijritcc2321-8169.15028

Dynamical Patterns of Cattle Trade Movements

Despite their importance for the spread of zoonotic diseases, our understanding of the dynamical aspects characterizing the movements of farmed animal populations remains limited as these systems are traditionally studied as static objects and through simplified approximations. By leveraging on the network science approach, here we are able for the first time to fully analyze the longitudinal dataset of Italian cattle movements that reports the mobility of individual animals among farms on a daily basis. The complexity and inter-relations between topology, function and dynamical nature of the system are characterized at different spatial and time resolutions, in order to uncover patterns and vulnerabilities fundamental for the definition of targeted prevention and control measures for zoonotic diseases. Results show how the stationarity of statistical distributions coexists with a strong and non-trivial evolutionary dynamics at the node and link levels, on all timescales. Traditional static views of the displacement network hide important patterns of structural changes affecting nodes' centrality and farms' spreading potential, thus limiting the efficiency of interventions based on partial longitudinal information. By fully taking into account the longitudinal dimension, we propose a novel definition of dynamical motifs that is able to uncover the presence of a temporal arrow describing the evolution of the system and the causality patterns of its displacements, shedding light on mechanisms that may play a crucial role in the definition of preventive actions

Dynamical Patterns of Cattle Trade Movements

Despite their importance for the spread of zoonotic diseases, our understanding of the dynamical aspects characterizing the movements of farmed animal populations remains limited as these systems are traditionally studied as static objects and through simplified approximations. By leveraging on the network science approach, here we are able for the first time to fully analyze the longitudinal dataset of Italian cattle movements that reports the mobility of individual animals among farms on a daily basis. The complexity and inter-relations between topology, function and dynamical nature of the system are characterized at different spatial and time resolutions, in order to uncover patterns and vulnerabilities fundamental for the definition of targeted prevention and control measures for zoonotic diseases. Results show how the stationarity of statistical distributions coexists with a strong and non-trivial evolutionary dynamics at the node and link levels, on all timescales. Traditional static views of the displacement network hide important patterns of structural changes affecting nodes' centrality and farms' spreading potential, thus limiting the efficiency of interventions based on partial longitudinal information. By fully taking into account the longitudinal dimension, we propose a novel definition of dynamical motifs that is able to uncover the presence of a temporal arrow describing the evolution of the system and the causality patterns of its displacements, shedding light on mechanisms that may play a crucial role in the definition of preventive actions

Resilience in Highways: Proposal of Roadway Redundancy Indicators and Application in Segments of the Brazilian Network

With the growing realization that transport systems must operate satisfactorily not only in typical situations, but also in adverse circumstances, ensuring redundancies in road systems has gained crucial importance. In this context, several methods have been proposed for measuring the vulnerabilities and resilience of transport systems. However, a simple metric to understand and quantify the degree of redundancy of a given road segment is still necessary, mainly to guide the responsible bodies regarding the need for intervention or special care with certain sections of the system. Thus, this paper proposes a redundancy indicator based on network analyses in the vicinity of an element. The proposed indicator was first calculated on nine application examples and then on a substantial sample of the Brazilian road network (~10% of segments). The results demonstrate that the indicator can satisfactorily describe the variety of cases in the Brazilian network, capturing cases where there is significant redundancy in the elements, as in some regions of the Southeast and South; or cases of very low redundancy, such as the sparse grid in the north of the country. It was also verified that the indicator has a particular sensitivity to parameters of the defined function, requiring further research for an acceptable calibration.Comment: 21 pages, 9 figures, 2 tables. Presented at ANPET 2023, Santos, S\~ao Paulo, Brazi

A Macroscopic Study of Network Security Threats at the Organizational Level.

Defenders of today's network are confronted with a large number of malicious activities such as spam, malware, and denial-of-service attacks. Although many studies have been performed on how to mitigate security threats, the interaction between attackers and defenders is like a game of Whac-a-Mole, in which the security community is chasing after attackers rather than helping defenders to build systematic defensive solutions. As a complement to these studies that focus on attackers or end hosts, this thesis studies security threats from the perspective of the organization, the central authority that manages and defends a group of end hosts. This perspective provides a balanced position to understand security problems and to deploy and evaluate defensive solutions. This thesis explores how a macroscopic view of network security from an organization's perspective can be formed to help measure, understand, and mitigate security threats. To realize this goal, we bring together a broad collection of reputation blacklists. We first measure the properties of the malicious sources identified by these blacklists and their impact on an organization. We then aggregate the malicious sources to Internet organizations and characterize the maliciousness of organizations and their evolution over a period of two and half years. Next, we aim to understand the cause of different maliciousness levels in different organizations. By examining the relationship between eight security mismanagement symptoms and the maliciousness of organizations, we find a strong positive correlation between mismanagement and maliciousness. Lastly, motivated by the observation that there are organizations that have a significant fraction of their IP addresses involved in malicious activities, we evaluate the tradeoff of one type of mitigation solution at the organization level --- network takedowns.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116714/1/jingzj_1.pd

Risk analysis beyond vulnerability and resilience - characterizing the defensibility of critical systems

A common problem in risk analysis is to characterize the overall security of a system of valuable assets (e.g., government buildings or communication hubs), and to suggest measures to mitigate any hazards or security threats. Currently, analysts typically rely on a combination of indices, such as resilience, robustness, redundancy, security, and vulnerability. However, these indices are not by themselves sufficient as a guide to action; for example, while it is possible to develop policies to decrease vulnerability, such policies may not always be cost-effective. Motivated by this gap, we propose a new index, defensibility. A system is considered defensible to the extent that a modest investment can significantly reduce the damage from an attack or disruption. To compare systems whose performance is not readily commensurable (e.g., the electrical grid vs. the water-distribution network, both of which are critical, but which provide distinct types of services), we defined defensibility as a dimensionless index. After defining defensibility quantitatively, we illustrate how the defensibility of a system depends on factors such as the defender and attacker asset valuations, the nature of the threat (whether intelligent and adaptive, or random), and the levels of attack and defense strengths and provide analytical results that support the observations arising from the above illustrations. Overall, we argue that the defensibility of a system is an important dimension to consider when evaluating potential defensive investments, and that it can be applied in a variety of different contexts.Comment: 36 pages; Keywords: Risk Analysis, Defensibility, Vulnerability, Resilience, Counter-terroris