IDF Curves

Cyber Supply Chain (CSC) system is complex which involves different subsystems performing various tasks. Security in supply chain is challenging due to the inherent vulnerabilities and threats from any part of the system which can be exploited at any point within the supply chain. This can cause a severe disruption on the overall business continuity. Therefore, it is paramount important to understand and predicate the threats so that organization can undertake necessary control measures for the supply chain security. Cyber Threat Intelligence (CTI) provides an intelligence analysis to discover unknown to known threats using various properties including threat actor skill and motivation, Tactics, Techniques, and Procedure (TT and P), and Indicator of Compromise (IoC). This paper aims to analyse and predicate threats to improve cyber supply chain security. We have applied Cyber Threat Intelligence (CTI) with Machine Learning (ML) techniques to analyse and predict the threats based on the CTI properties. That allows to identify the inherent CSC vulnerabilities so that appropriate control actions can be undertaken for the overall cybersecurity improvement. To demonstrate the applicability of our approach, CTI data is gathered and a number of ML algorithms, i.e., Logistic Regression (LG), Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT), are used to develop predictive analytics using the Microsoft Malware Prediction dataset. Parameters and vulnerabilities and Indicators of compromise (IoC) as output parameters. The results relating to the prediction reveal that Spyware/Ransomware and spear phishing are the most predictable threats in CSC. We have also recommended relevant controls to tackle these threats. We advocate using CTI data for the ML predicate model for the overall CSC cyber security improvement.

Ante el supuesto aumento del volumen de agua precipitada por evento de lluvia, se realizó para la Ciudad de Salta un estudio para determinar cómo varían las Curvas de Intensidad – Duración – Frecuencia (Curvas IDF) construidas en dos momentos distintos del tiempo. Se tomó como línea de base para el área de estudio a las obtenidas por Sciortino, 1991 (Citado por Villanueva et al, 2004). El otro momento considerado fue el año 2012, instancia para la cual se construyó nuevas curvas IDF en base a datos de precipitaciones del Aeropuerto El Aybal período 1980–2012. La construcción consistió en llevar los nuevos datos a intensidad horaria, ajustando para cada duración “D” de lluvia la Distribución Probabilística de Gumbel. A los valores obtenidos para las Precipitaciones Diarias Máximas Probables para distintos Períodos de Retorno, se les aplicó el factor de corrección propuesto por U.S. Weather Bureau, actual Servicio Meteorológico Nacional de Estados Unidos de Norteamérica. Se delinearon así nuevas curvas IDF para períodos de retornos de 2, 5, 10, 25, 50 y 100 años, que acusaron mayores intensidades de lluvias que sus pares de 1991. Esta situación estaría indicando la estrecha interrelación funcional de diversos factores, entre los cuales se puede mencionar a: errores de manipulación y lectura de equipos, diferencias insalvables entre los métodos y tecnologías empleadas para tomar datos en una y otra estación meteorológica, diferencias metodológicas en la construcción de las curvas y/o, entre otros, a un incremento efectivo del volumen de agua precipitada por evento de lluvia.
rgb-6/2013

Analysis of quantity of rainfall is needed for planning process and design process of various water resource projects. Intensity-duration-frequency (IDF) curves are used to analyze quantity of rainfall of different duration (t) and return periods (T). The study is carried out in Aghnashini river watershed which is located at 74 o 18 ' 15.95''-74 o 55'22.84 '' E longitude and 14°15'26.21''-14°37'17.65'' N latitude covering an area of 1400.47 sq.km and elevation ranges from zero meters to 784 meters above MSL. The river originates in Sirsi and flow towards West and reaches Arabian sea at Kumta of Uttara Kannada, Karnataka state.

Los eventos extremos, meteorológicos y climáticos, han recibido una atención especial durante los últimos años debido a la pérdida de vidas humanas que provocan y al aumento exponencial de las pérdidas económicas asociadas. Además, la sociedad es más sensible a estos eventos, ya que ha habido un aumento de la vulnerabilidad de bienes y personas. El riesgo natural más importante tanto en España como en Europa son las inundaciones, producidas por fenómenos meteorológicos ocasionales o estacionales, en que la magnitud de la inundación dependerá de las caracterísitcas geomorfológicas del territorio y de las actividades humanas desarrolladas en él.

The evaluation of the possible climate change influence on extreme precipitation is very interesting in the Mediterranean area due to the usual and characteristic high intensities of its rainfall pattern. This analysis is also very important in urban zones, especially those densely populated with complex sewer systems, generally vulnerable to torrential rainfall. In this work, a total of 114 simulated daily rainfall series, 84 for the period 2000–2099 and 30 for the control period 1951–1999, have been analysed. These series were obtained for six thermo-pluviometric stations located in the metropolitan area of Barcelona using the information provided by five general circulation models under four future climate scenarios of greenhouse gas emissions and applying statistical downscaling methods. The potential changes in the intensity–duration–frequency relationships due to climate change have been investigated. For the last third of the 21st century, under A1B, A2 and B2 climate scenari...

El objetivo del presente trabajo es la caracterización de los eventos de precipitación intensa, susceptibles de provocar inundaciones y avenidas, mediante la obtención de las curvas de intensidad-duración-frecuencia (curvas IDF) en tres localidades de la costa catalana. Estas curvas se utilizan en numerosos estudios de hidrología aplicada relacionados con el cálculo de caudales, la construcción de lluvias de diseño utilizadas como entrada de modelos hidrológicos, o para el cálculo y diseño de redes alcantarillado y drenaje urbano. http://virtualpublish.es/clientes/kioscos/BIENAL/BIENAL_RSEF2013.html#848

The infrastructure design is primarily based on rainfall intensity–duration–frequency (IDF) curves, and the existing IDF curves are based on the concept of stationary extreme value theory (EVT) (i.e. the occurrence probability of extreme precipitation is not expected to change significantly over time). But, the extreme precipitation events are increasing due to global climate change and questioning the reliability of our current infrastructure design. Based on recent developments in the EVT, recent studies proposed a method for developing non-stationary rainfall IDF curve by incorporating linear trend in the location parameter of the generalized extreme value (GEV) distribution. Upon detecting a significant trend in the extreme rainfall series, directly applying the linear trend to develop non-stationary IDF curves may increase the bias of the non-stationary model. Hence, it is important to develop non-stationary GEV model which has less bias than the stationary model by modelling nonlinear trend in the series. In this study, we try to develop non-stationary GEV models with less bias by modelling nonlinear trend in the series using multi-objective genetic algorithm (MOGA). In addition, the proposed GEV model is compared with the stationary GEV model and the linear trend-based non-stationary GEV model. Furthermore, the Wilmington city and the Hyderabad city non-stationary IDF curves are developed and compared with stationary IDF curves. From the study results, it is observed that the proposed MOGA-based method is able to build the good quality and less bias non-stationary GEV models by modelling nonlinear trend in the series. In addition, it is also observed that the usage of linear trend for modelling non-stationarity in the time series sometimes increase the bias of non-stationary model.