Vulnerability Prediction Research Articles

Using hierarchical clustering analysis to evaluate COVID-19 pandemic preparedness and performance in 180 countries in 2020

ObjectiveTo rank and score 180 countries according to COVID-19 cases and fatality in 2020 and compare the results to existing pandemic vulnerability prediction models and results generated by standard epidemiological...

Improving Vulnerability Inspection Efficiency Using Active Learning

Software engineers can find vulnerabilities with less effort if they are directed towards code that might contain more vulnerabilities. HARMLESS is an incremental support vector machine tool that builds a vulnerability prediction model from the source code inspected to date, then suggests what source code files should be inspected next. In this way, HARMLESS can reduce the time and effort required to achieve some desired level of recall for finding vulnerabilities. The tool also provides feedback on when to stop (at that desired level of recall) while at the same time, correcting human errors by double-checking suspicious files. This paper evaluates HARMLESS on Mozilla Firefox vulnerability data. HARMLESS found 80, 90, 95, 99 percent of the vulnerabilities by inspecting 10, 16, 20, 34 percent of the source code files. When targeting 90, 95, 99 percent recall, HARMLESS could stop after inspecting 23, 30, 47 percent of the source code files. Even when human reviewers fail to identify half of the vulnerabilities (50 percent false negative rate), HARMLESS could detect 96 percent of the missing vulnerabilities by double-checking half of the inspected files. Our results serve to highlight the very steep cost of protecting software from vulnerabilities (in our case study that cost is, for example, the human effort of inspecting 28,750 × 20% = 5,750 source code files to identify 95 percent of the vulnerabilities). While this result could benefit the mission-critical projects where human resources are available for inspecting thousands of source code files, the research challenge for future work is how to further reduce that cost. The conclusion of this paper discusses various ways that goal might be achieved.

Evaluation of the vulnerability in water distribution systems through targeted attacks

Abstract This paper presents the results of a vulnerability analysis in different water distribution system (WDS) benchmarks, performed under a framework based on a graph model that integrates topological features and hydraulic characteristics, allowing the comparison between different attack strategies and centrality measures in terms of their ability to predict the shortage of water supply. This vulnerability framework has been previously applied to electric power systems and it employs a vulnerability prediction measure to quantify the amount of damage caused in terms of the physical damage measure. Different attack strategies and centrality measures were applied to four WDS benchmarks: the New York Tunnel, the Hanoi, the Modena, and the Balerma networks. It was determined that removing the most central element and recalculating the centrality for each stage are the most damaging attack strategies. Degree, eigenvector, and Katz centrality measures presented the best performance to predict the elements that are more relevant to the network and can have a larger impact on the water supply. It was demonstrated that the vulnerability framework can be applied to the WDS in the same way it was previously applied to electric power systems. Future work will be oriented to the design of the WDS using optimization techniques to minimize the vulnerability of the network under faults that can be generated by droughts and other extreme weather conditions.

Dampak Perubahan Curah Hujan Terhadap Tingkat Kerentanan Erosi Tanah Di Sub DAS Merawu, Jawa Tengah

This research was held to estimate rainfall and change in soil erosion vulnerability from 2020 to 2050 in Merawu Sub-Watershed, Banjanegara District with RCP 2.6, 4.5 and 8.5. The RCP is an overview of the concentration trends for greenhouse gases, aerosols and land use change created by the climate modeling community. Rainfall prediction was generated from SDSM Software and combined with USLE to predict soil erosion in ArcGIS 10.4. Changes in rainfall intensity are an important factor in changes of soil erosion rates because the kinetic energy of falling rainwater can cause soil erosion.The results showed rainfall in Banjarnegara Station at 2020-2050 with RCP 2.6,4.5 and 8.5 were increasing by +0,26%; +0,60%; +0,52%, while in Kalisapi Station were decreasing by -1,54%; -1,65% dan -2,20%. The change of soil erosion vulnerability prediction showed that soil erosion in Sub-DAS Merawu at 2020-2050 with RCP 2.6,4.5 and 8.5 in very light category were -0,02%;-0,02%;-0,03%, light category were -0,17%;-0,17%;-0,17%, moderate category -0,05%;-0,05%;-0,04%, heavy category -0,26%;-0,35%;-0,37%, and very heavy category were +1,46%;+1,88%;+1,95%. While the average soil erosion prediction at RCP 2.6, 4.5 and 8.5 were +0,86, +1,19% and +1,03%, respectively. Keywords: soil erosion prediction, rainfall prediction, SDSM Software, Sub-DAS Merawu

Spatiotemporal Evolution and Trend Prediction of Tourism Economic Vulnerability in China’s Major Tourist Cities

The evaluation and trend prediction of tourism economic vulnerability (TEV) in major tourist cities are necessary for formulating tourism economic strategies scientifically and promoting the sustainable development of regional tourism. In this study, 58 major tourist cities in China were taken as the research object, and an evaluation index system of TEV was constructed from two aspects of sensitivity and adaptive capacity. On the basis of the entropy weight method, TOPSIS model, obstacle diagnosis model, and BP neural network model, this study analyzed the spatiotemporal patterns, obstacle factors, and future trends of TEV in major tourist cities in China from 2004 to 2019. The results show three key findings: (1) In terms of spatiotemporal patterns, the TEV index of most of China’s tourist cities has been on the rise from 2004 to 2019. Cities throughout the coast of China’s Yangtze River Delta and the Pearl River Delta urban agglomeration show high vulnerability, whereas low vulnerability has a scattered distribution in China’s northeast, central, and western regions. (2) The proportion of international tourists out of total tourists, tourism output density, urban industrial sulfur dioxide emissions per unit area, urban industrial smoke and dust emission per unit area, and discharge of urban industrial wastewater per unit area are the five major obstacles affecting the vulnerability degree of the tourism economy. (3) According to the prediction results of TEV from 2021 to 2030, although the TEV of many tourist cities in China is increasing year by year, cities with low TEV levels occupy the dominant position. Research results can provide reference for tourist cities to prevent tourism crises from occurring and to reasonably improve the resilience of the tourism economic system.

Data-driven structure selection for the grey NGMC(1,N) model

PurposeWith the prosperity of grey extension models, the form and structure of grey forecasting models tend to be complicated. How to select the appropriate model structure according to the data characteristics has become an important topic. The purpose of this paper is to design a structure selection method for the grey multivariate model.Design/methodology/approachThe linear correction term is introduced into the grey model, then the nonhomogeneous grey multivariable model with convolution integral [NGMC(1,N)] is proposed. Then, by incorporating the least absolute shrinkage and selection operator (LASSO), the model parameters are compressed and estimated based on the least angle regression (LARS) algorithm.FindingsBy adjusting the values of the parameters, the NGMC(1,N) model can derive various structures of grey models, which shows the structural adaptability of the NGMC(1,N) model. Based on the geometric interpretation of the LASSO method, the structure selection of the grey model can be transformed into sparse parameter estimation, and the structure selection can be realized by LASSO estimation.Practical implicationsThis paper not only provides an effective method to identify the key factors of the agricultural drought vulnerability, but also presents a practical model to predict the agricultural drought vulnerability.Originality/valueBased on the LASSO method, a structure selection algorithm for the NGMC(1,N) model is designed, and the structure selection method is applied to the vulnerability prediction of agricultural drought in Puyang City, Henan Province.

A modified sub-assembly approach for hurricane induced wind-surge-wave vulnerability assessment of low-rise wood buildings in coastal communities

The coastal low-rise buildings account for large amounts of property loss during hurricanes, which makes it essential to perform vulnerability assessments of buildings and communities to estimate the possible structural damage and economical loss. Fragility assessment and loss prediction are usually performed with the sub-assembly (SA) approach in the HAZUS-MH model. However, hydrodynamic forces caused by surge and waves are excluded, and damages from wind and flood are assumed independent of each other. In the present study, a modified sub-assembly (MSA) approach is proposed based on the SA approach for the hurricane-induced wind-surge-wave vulnerability assessments for buildings and communities considering the hurricane-induced wind-surge-wave loadings without the predefined assumption. The physics-based high-fidelity finite element models are established, and the structural performances are analyzed for three typical coastal residential wood buildings under individual wind or surge-wave loadings and combined wind-surge-wave loadings. Building type is found to be a key factor influencing the vulnerabilities of the buildings, and building elevation is an effective method to mitigate building damages from surge-wave loadings as well as hurricane-induced wind-surge-wave loadings. Profound influences of wind profile shifting under combined wind-surge-wave loading are observed on elevated buildings' vulnerabilities at some stillwater levels for different building types. The MSA approach outperforms the SA approach in the building vulnerability prediction at the hurricane-induced wind-surge-wave scenarios, especially for the elevated buildings. A smaller wind speed interval is recommended to help with the building vulnerability prediction accuracy of the MSA approach. The vulnerability assessment of a coastal community further validates the accuracy of the proposed MSA approach.

A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings

A vast number of existing buildings were constructed before the development and enforcement of seismic design codes, which run into the risk of being severely damaged under the action of seismic excitations. This poses not only a threat to the life of people but also affects the socio-economic stability in the affected area. Therefore, it is necessary to assess such buildings’ present vulnerability to make an educated decision regarding risk mitigation by seismic strengthening techniques such as retrofitting. However, it is economically and timely manner not feasible to inspect, repair, and augment every old building on an urban scale. As a result, a reliable rapid screening methods, namely Rapid Visual Screening (RVS), have garnered increasing interest among researchers and decision-makers alike. In this study, the effectiveness of five different Machine Learning (ML) techniques in vulnerability prediction applications have been investigated. The damage data of four different earthquakes from Ecuador, Haiti, Nepal, and South Korea, have been utilized to train and test the developed models. Eight performance modifiers have been implemented as variables with a supervised ML. The investigations on this paper illustrate that the assessed vulnerability classes by ML techniques were very close to the actual damage levels observed in the buildings.

Information security vulnerability prediction based on business process model using machine learning approach

Identifying information security vulnerabilities of a new business process resulting from a business process redesign (BPR) must occur as early as possible. Organisations frequently initiate a BPR, and vulnerability identification that involves information security experts should follow. In developing countries, many organisations have insufficient experts, causing problems related to the experts’ workload. In this study, we propose a new method called Task-based Vulnerability Prediction (TbVP), which uses a machine-learning approach to predict information security vulnerabilities of the business process model. The method utilises the type and label of tasks in the model to predict vulnerabilities in implementing applications. Vulnerabilities data are taken from the Common Weakness Enumeration (CWE) dictionary. Our method consists of two main stages. First, we developed clusters using classification and clustering methods. Second, we built an automatic system to predict vulnerabilities using the clusters obtained from the first stage. Business processes of public universities were used as case studies to evaluate the method. We evaluated the automatic prediction system using the reliability test and comparing vulnerabilities our system predicted with actual vulnerabilities that materialised in the applications. The evaluation result shows that the system is a reliable predictor of application vulnerabilities, which our method can automatically predict based on a business process model, before implementing supporting applications.

Model parameter prediction of lumped plasticity model for nonlinear simulation of circular reinforced concrete columns

This paper presents the model parameter estimation of a lumped plasticity model to realistically simulate the nonlinear load-deformation response of circular reinforced concrete columns under cyclic lateral loading. The model parameter calibration is based on an experimental database consisting of 210 circular columns with various input parameters, such as material strength, reinforcement layout, specimen geometry, and test configuration. The model parameter values for the initial stiffness, plastic rotation capacity, moment strength, and cyclic damage parameters are calibrated to the first-cycle envelope of each set of test data. Empirical predictive equations are developed to estimate the model parameters as functions of the input parameters using four different regression techniques: stepwise, ridge, lasso, and elastic net regression. The equations developed with lasso regression provided a reasonable trade-off between the prediction error and the number of input parameters. Important input parameters that affect the model parameters are the volumetric transverse reinforcement ratio, spacing of confinement steel, and axial load. As an application of the proposed column model, seismic fragilities of bridge columns in two bridge classes with different interior bent type are compared based on an existing distributed plasticity model and the proposed lumped plasticity model. The seismic vulnerability prediction discrepancy between the proposed and existing models tend to increase as the level of ground motion intensity increases. The prediction trend between the two models may differ by bent configuration and deck mass. The proposed column model reduces the seismic vulnerability of one-column bent bridge class, but increases the vulnerability of two-column bent bridge class.

Assessment and Prediction of Water Resources Vulnerability Based on a NRS-RF Model: A Case Study of the Song-Liao River Basin, China.

The vulnerability of water resources is an important criterion for evaluating the carrying capacity of water resources systems under the influence of climate change and human activities. Moreover, assessment and prediction of river basins’ water resources vulnerability are important means to assess the water resources security state of river basins and identify possible problems in future water resources systems. Based on the constructed indicator system of water resources vulnerability assessment in Song-Liao River Basin, this paper uses the neighborhood rough set (abbreviated as NRS) method to reduce the dimensionality of the original indicator system to remove redundant attributes. Then, assessment indicators’ standard values after dimensionality reduction are taken as the evaluation sample, and the random forest regression (abbreviated as RF) model is used to assess the water resources vulnerability of the river basin. Finally, based on data under three different future climate and socio-economic scenarios, scenario predictions are made on the vulnerability of future water resources. The results show that the overall water resources vulnerability of the Song-Liao River Basin has not improved significantly in the past 18 years, and the overall vulnerability of the Song-Liao River Basin is in the level V of moderate to high vulnerability. In the future scenario 1, the overall water resources vulnerability of the river basin will improve, and it is expected to achieve an improvement to the level III of moderate to low vulnerability. At the same time, the natural vulnerability and vulnerability of carrying capacity will increase significantly in the future, and the man-made vulnerability will increase slowly, which will deteriorate to the level V of moderate to high vulnerability under Scenario 3. Therefore, taking active measures can significantly reduce the vulnerability of nature and carrying capacity, but man-made vulnerability will become a bottleneck restricting the fragility of the overall water resources of the river basin in the future.

Using ML and Data-Mining Techniques in Automatic Vulnerability Software Discovery

Today’s age is Machine Learning (ML) and Data-Mining (DM) Techniques, as both techniques play a significant role in measuring vulnerability prediction accuracy. In the field of computer security, vulnerability is a fault that might be exploited as a risk artist that performs unlawful activities inside computer security. The attackers have several different fitting tools and they are taking advantage to operate software illegally and are using it for getting self-profit. Additionally, that helps to expose and identify the violence external. Weakness management remains a repeating exercise to identify, remediating, and justifying weaknesses. These exercises normally send software faults in computing security. The meaning of using weakness with the same risk might go to misperception. It is possible to have a major effect because of possible stability and the window of weakness presented a risk hole in the software and required to fruitfully finish and smoothly operate. A security room has to be set up (zero-day invaders). Software Security Faults stand serious among unavoidable complications in the realm of computer risk. In this study, we have provided a comprehensive review of three book chapters, more than a hundred research articles papers, and several associated papers of different work that have been studied within the capacity of SVA and discovery applying ML and data-mining techniques. The earlier work has been thoroughly read and an adequately comprehensive summary has been provided in table-1. ML techniques that can professionally handle these attacks and we expect the net result of this survey article to help indesigning the new detection model for identifying the above-mentioned attacks

A hierarchical model for quantifying software security based on static analysis alerts and software metrics

Despite the acknowledged importance of quantitative security assessment in secure software development, current literature still lacks an efficient model for measuring internal software security risk. To this end, in this paper, we introduce a hierarchical security assessment model (SAM), able to assess the internal security level of software products based on low-level indicators, i.e., security-relevant static analysis alerts and software metrics. The model, following the guidelines of ISO/IEC 25010, and based on a set of thresholds and weights, systematically aggregates these low-level indicators in order to produce a high-level security score that reflects the internal security level of the analyzed software. The proposed model is practical, since it is fully automated and operationalized in the form of a standalone tool and as part of a broader Computer-Aided Software Engineering (CASE) platform. In order to enhance its reliability, the thresholds of the model were calibrated based on a repository of 100 popular software applications retrieved from Maven Repository. Furthermore, its weights were elicited in a way to chiefly reflect the knowledge expressed by the Common Weakness Enumeration (CWE), through a novel weights elicitation approach grounded on popular decision-making techniques. The proposed model was evaluated on a large repository of 150 open-source software applications retrieved from GitHub and 1200 classes retrieved from the OWASP Benchmark. The results of the experiments revealed the capacity of the proposed model to reliably assess internal security at both product level and class level of granularity, with sufficient discretion power. They also provide preliminary evidence for the ability of the model to be used as the basis for vulnerability prediction. To the best of our knowledge, this is the first fully automated, operationalized and sufficiently evaluated security assessment model in the modern literature.

When loneliness dimensions drift apart: Emotional, social and physical loneliness during the COVID-19 lockdown and its associations with age, personality, stress and well-being.

Previous research differentiated between emotional loneliness (perceived lack of emotional connection with others) and social loneliness (perceived lack of a broader social network). We argue that physical loneliness (perceived lack of physical contact) constitutes a third dimension of loneliness that is particularly relevant in times of physical distancing. We conducted a longitudinal experience sampling study (N = 578) during the first 8 weeks of the COVID‐19 lockdown in Germany to test this claim. The results indicate that loneliness has a three‐dimensional structure encompassing emotional, social and physical loneliness. Each loneliness dimension explained a unique variance in perceived stress and psychological well‐being. However, the three loneliness dimensions differed in their prevalence during the contact restrictions and their associations with age and personality. Physical loneliness was higher during the contact restrictions whereas emotional and social loneliness remained on a normal level. Age was positively associated with social loneliness but negatively associated with physical loneliness. Extraversion was negatively associated with emotional and social loneliness but positively associated with physical loneliness. These findings expand loneliness models, enhance loneliness assessment and improve the prediction of vulnerability to loneliness.

Evaluation of the Most Harmful Malicious Attacks in Power Systems Based on a New Set of Centralities

This paper aims to present an evaluation of targeted attack sequences based on a new set of power traffic centrality measures according to the vulnerability prediction measure (VPM) approach. A framework for evaluation of attack proposed in previous work is applied using three fault strategies: remove most central element first (RMCEF), iterated most central element first, and iterated electrical most damaging element first (IMDEF). For attacks on nodes, the reliability of the IMDEF strategy is confirmed, as it was the most predictive in terms of the VPM. Nevertheless, in attacks performed on links, the IMDEF does not always represents the most harmful attack. Regarding the new centralities, the Katz centrality consistently presented high values of VPM for attacks on nodes and links, with results that are comparable to degree and eigenvector centralities. In terms of execution times, the percolation centrality is not recommended, as it presented the highest execution times. The RMCEF strategy with degree, eigenvector and Katz centralities is a good estimation of the most harmful attack sequences on nodes and links with a shorter execution time than the IMDEF.

‘Hidden Hot Springs’ as a Source of Groundwater Fluoride and Severe Dental Fluorosis in Malawi

Hidden hot springs likely impact rural water supplies in Malawi’s Rift Valley with excess dissolved fluoride leading to localised endemic severe dental fluorosis. Predicting their occurrence is a challenge; Malawi’s groundwater data archive is sporadic and incomplete which prevents the application of standard modelling techniques. A creative alternative method to predict hidden hot spring locations was developed using a synthesis of proxy indicators (geological, geochemical, dental) and is shown to be at least 75% effective. An exciting collaboration between geoscientists and dentists allowed corroboration of severe dental fluorosis with hydrogeological vulnerability. Thirteen hidden hot springs were identified based on synthesised proxy indicators. A vulnerability prediction map for the region was developed and is the first of its kind in Malawi. It allows improved groundwater fluoride prediction in Malawi’s rift basin which hosts the majority of hot springs. Moreover, it allows dentists to recognise geological control over community oral health. Collaborative efforts have proven mutually beneficial, allowing both disciplines to conduct targeted research to improve community wellbeing and health and inform policy development in their respective areas. This work contributes globally in developing nations where incomplete groundwater data and vulnerability to groundwater contamination from hydrothermal fluoride exist in tandem.

Predicting plaque vulnerability change using intravascular ultrasound\u2009+\u2009optical coherence tomography image-based fluid\u2013structure interaction models and machine learning methods with patient follow-up data: a feasibility study

BackgroundCoronary plaque vulnerability prediction is difficult because plaque vulnerability is non-trivial to quantify, clinically available medical image modality is not enough to quantify thin cap thickness, prediction methods with high accuracies still need to be developed, and gold-standard data to validate vulnerability prediction are often not available. Patient follow-up intravascular ultrasound (IVUS), optical coherence tomography (OCT) and angiography data were acquired to construct 3D fluid–structure interaction (FSI) coronary models and four machine-learning methods were compared to identify optimal method to predict future plaque vulnerability.MethodsBaseline and 10-month follow-up in vivo IVUS and OCT coronary plaque data were acquired from two arteries of one patient using IRB approved protocols with informed consent obtained. IVUS and OCT-based FSI models were constructed to obtain plaque wall stress/strain and wall shear stress. Forty-five slices were selected as machine learning sample database for vulnerability prediction study. Thirteen key morphological factors from IVUS and OCT images and biomechanical factors from FSI model were extracted from 45 slices at baseline for analysis. Lipid percentage index (LPI), cap thickness index (CTI) and morphological plaque vulnerability index (MPVI) were quantified to measure plaque vulnerability. Four machine learning methods (least square support vector machine, discriminant analysis, random forest and ensemble learning) were employed to predict the changes of three indices using all combinations of 13 factors. A standard fivefold cross-validation procedure was used to evaluate prediction results.ResultsFor LPI change prediction using support vector machine, wall thickness was the optimal single-factor predictor with area under curve (AUC) 0.883 and the AUC of optimal combinational-factor predictor achieved 0.963. For CTI change prediction using discriminant analysis, minimum cap thickness was the optimal single-factor predictor with AUC 0.818 while optimal combinational-factor predictor achieved an AUC 0.836. Using random forest for predicting MPVI change, minimum cap thickness was the optimal single-factor predictor with AUC 0.785 and the AUC of optimal combinational-factor predictor achieved 0.847.ConclusionThis feasibility study demonstrated that machine learning methods could be used to accurately predict plaque vulnerability change based on morphological and biomechanical factors from multi-modality image-based FSI models. Large-scale studies are needed to verify our findings.

Prediction of software vulnerability based deep symbiotic genetic algorithms: Phenotyping of dominant-features

The detection of software vulnerabilities is considered a vital problem in the software security area for a long time. Nowadays, it is challenging to manage software security due to its increased complexity and diversity. So, vulnerability detection applications play a significant part in software development and maintenance. The ability of the forecasting techniques in vulnerability detection is still weak. Thus, one of the efficient defining features methods that have been used to determine the software vulnerabilities is the metaheuristic optimization methods. This paper proposes a novel software vulnerability prediction model based on using a deep learning method and SYMbiotic Genetic algorithm. We are first to apply Diploid Genetic algorithms with deep learning networks on software vulnerability prediction to the best of our knowledge. In this proposed method, a deep SYMbiotic-based genetic algorithm model (DNN-SYMbiotic GAs) is used by learning the phenotyping of dominant-features for software vulnerability prediction problems. The proposed method aimed at increasing the detection abilities of vulnerability patterns with vulnerable components in the software. Comprehensive experiments are conducted on several benchmark datasets; these datasets are taken from Drupal, Moodle, and PHPMyAdmin projects. The obtained results revealed that the proposed method (DNN-SYMbiotic GAs) enhanced vulnerability prediction, which reflects improving software quality prediction.

Predicting the Soft Error Vulnerability of Parallel Applications Using Machine Learning

With the widespread use of the multicore systems having smaller transistor sizes, soft errors become an important issue for parallel program execution. Fault injection is a prevalent method to quantify the soft error rates of the applications. However, it is very time consuming to perform detailed fault injection experiments. Therefore, prediction-based techniques have been proposed to evaluate the soft error vulnerability in a faster way. In this work, we present a soft error vulnerability prediction approach for parallel applications using machine learning algorithms. We define a set of features including thread communication, data sharing, parallel programming, and performance characteristics; and train our models based on three ML algorithms. This study uses the parallel programming features, as well as the combination of all features for the first time in vulnerability prediction of parallel programs. We propose two models for the soft error vulnerability prediction: (1) A regression model with rigorous feature selection analysis that estimates correct execution rates, (2) A novel classification model that predicts the vulnerability level of the target programs. We get maximum prediction accuracy rate of 73.2% for the regression-based model, and achieve 89% F-score for our classification model.

Prediksi Kerentanan Personal Terhadap Covid 19 dengan Menggunakan Pendekatan Graf

AbstractThe Covid 19 pandemic happened in arround the world include in Indonesia. It has impacts in many fileds. This research developed to solve the Covid 19 problem. This research requires complex variables based on the varying data in the field. Based on surveys and data, it found that there are 65% of personals know the status of their area in danger zone or safe zone for Covid 19. However, there are still many personals ignore the zone status that has been informed previously by the relevant goverment. The purpose of this study is to determine personal vulnerability to Covid-19 based on zones or regions. Moreover, prediction of vulnerability based on personal distance, the number of personal confirm Covid-19 arround the areas, and other variables such as immunity, and the accuracy of GPS applications. The methods is carried out by creating a vulnerabelity prediction model through GPS tracking based on the position or residence, then create to graph model in shortest path. Initial predictions are given a minimum distance between the personal and individuals confirms is one meter. The result of this research is percentage of personal vulnerability on the number of confirmed Covid 19 detections based on zones or regions. The prediction includes three models such as susceptible, quite susceptible, and safe. Personal susceptible in the percentage arround 90%-100%, quite susceptible in the percentage 75%-90%, and consideres safe in less that 75%.Keywords: prediction, vulnerability, graph