Classification of logical vulnerability based on group attacking method

New advancement in the field of e-commerce software technology has also brought many benefits, at the same time developing process always face different sort of problems from design phase to implement phase. Software faults and defects increases the issues of reliability and security, that’s reason why a solution of this problem is required to fortify these issues. The paper addresses the problem associated with lack of clear component-based web application related classification of logical vulnerabilities through identifying Attack Group Method by categorizing two different types of vulnerabilities in component-based web applications. A new classification scheme of logical group attack method is proposed and developed by using a Posteriori Empirically methodology

Vulnerability assessment using remote sensing: The earthquake prone megacity Istanbul, Turkey

Hazards like earthquakes are natural, disasters are not. Disasters result from the impact of a hazard on a vulnerable system or society at a specific location. The framework of vulnerability aims at a holistic concept taking physical, environmental, socio-economic and political components into account. This paper focuses on the capabilities of remote sensing to contribute up-to-date spatial information to the physical dimension of vulnerability for the complex urban system of the megacity Istanbul, Turkey. An urban land cover classification based on high resolution satellite data establishes the basis to analyse the spatial distribution of different types of buildings, the carrying capacity of the street network or the identification of open spaces. In addition, a DEM (Digital Elevation Model) enables a localization of potential landslide areas. A methodology to combine these attributes related to the physical dimension of vulnerability is presented. In this process an n-dimensional coordinate system plots the variables describing vulnerability against each other. This enables identification of the degree of vulnerability and the vulnerability-determining factors for a specific location. This assessment of vulnerability provides a broad spatial information basis for decision-makers to develop mitigation strategies

Synthetic rating system for railway bridge management

Railway bridges deteriorate with age. Factors such as environmental effects on different materials of a bridge, variation of loads, fatigue, etc will reduce the remaining life of bridges. Bridges are currently rated individually for maintenance and repair actions according to the structural conditions of their elements. Dealing with thousands of bridges and several factors that cause deterioration, makes the rating process extremely complicated. Current simplified but practical rating methods are not based on an accurate structural condition assessment system. On the other hand, the sophisticated but more accurate methods are only used for a single bridge or particular types of bridges. It is therefore necessary to develop a practical and accurate system which will be capable of rating a network of railway bridges. This paper introduces a new method for rating a network of bridges based on their current and future structural conditions. The method identifies typical bridges representing a group of railway bridges. The most crucial agents will be determined and categorized to criticality and vulnerability factors. Classification based on structural configuration, loading, and critical deterioration factors will be conducted. Finally a rating method for a network of railway bridges that takes into account the effects of damaged structural components due to variations in loading and environmental conditions on the integrity of the whole structure will be proposed. The outcome of this research is expected to significantly improve the rating methods for railway bridges by considering the unique characteristics of different factors and incorporating the correlation between them

Earthquake Hazard Safety Assessment of Existing Buildings Using Optimized Multi-Layer Perceptron Neural Network

The latest earthquakes have proven that several existing buildings, particularly in developing countries, are not secured from damages of earthquake. A variety of statistical and machine-learning approaches have been proposed to identify vulnerable buildings for the prioritization of retrofitting. The present work aims to investigate earthquake susceptibility through the combination of six building performance variables that can be used to obtain an optimal prediction of the damage state of reinforced concrete buildings using artificial neural network (ANN). In this regard, a multi-layer perceptron network is trained and optimized using a database of 484 damaged buildings from the Düzce earthquake in Turkey. The results demonstrate the feasibility and effectiveness of the selected ANN approach to classify concrete structural damage that can be used as a preliminary assessment technique to identify vulnerable buildings in disaster risk-management programs

Learning-based Analysis on the Exploitability of Security Vulnerabilities

The purpose of this thesis is to develop a tool that uses machine learning techniques to make predictions about whether or not a given vulnerability will be exploited. Such a tool could help organizations such as electric utilities to prioritize their security patching operations. Three different models, based on a deep neural network, a random forest, and a support vector machine respectively, are designed and implemented. Training data for these models is compiled from a variety of sources, including the National Vulnerability Database published by NIST and the Exploit Database published by Offensive Security. Extensive experiments are conducted, including testing the accuracy of each model, dynamically training the models on a rolling window of training data, and filtering the training data by various features. Of the chosen models, the deep neural network and the support vector machine show the highest accuracy (approximately 94% and 93%, respectively), and could be developed by future researchers into an effective tool for vulnerability analysis

Prediction of seismic-induced structural damage using artificial neural networks

Peer reviewedPostprin