Vulnerability Forecasting: Theory and Practice

It is possible to forecast the volume of CVEs released within a time frame with a given prediction interval. For example, the number of CVEs published between now and 365 days from now can be predicted a year in advance within 8% of the actual value. Different predictive algorithms perform well at different lookahead values other than 365 days, such as monthly, quarterly, and half year. It is also possible to estimate the proportions of that total volume belonging to specific vendors, software, CVSS scores, or vulnerability types. Some vendors and products can be predicted with accuracy, others with too much uncertainty to be practically useful. This paper documents which ones are amenable to being forecasted. Strategic patch management should become much easier with these tools, and further uncertainty reductions can be built from the methodologies in this paper.

Application of Modified Grey Forecasting Model to Predict the Municipal Solid Waste Generation using MLP and MLE

Grey forecasting theory is an approach to build a prediction model with limited data to produce better forecasting results. This forecasting theory has an elementary model, represented as the GM(1,1) model , characterized by the first-order differential equation of one variable. It has the potential for accurate and reliable forecasting without any statistical assumption. The research proposes a methodology to derive the modified GM(1,1) model with improved forecasting precision. The residual series is forecasted by the GM(1,1) model to modify the actual forecasted values. The study primarily addresses two fundamental issues: sign prediction of forecasted residual and the procedure for formulating the grey model. Accurate sign prediction is very complex, especially when the model lacks in data. The signs of forecasted residuals are determined using a multilayer perceptron to overcome this drawback. Generally, the elementary model is formulated conventionally, containing the parameters that cannot be calculated straightforward. Therefore, maximum likelihood estimation is incorporated in the modified model to resolve this drawback. Three statistical indicators, relative residual, posterior variance test, and absolute degree of grey indices, are evaluated to determine the model fitness and validation. Finally, an empirical study is performed using actual municipal solid waste generation data in Saudi Arabia, and forecasting accuracies are compared with the linear regression and original GM(1,1). The MAPEs of all models are rigorously examined and compared, and then it is obtained that the forecasting precision of GM(1,1) model , modified GM(1,1) model, and linear regression is 15.97%, 8.90%, and 27.90%, respectively. The experimental outcomes substantiate that the modified grey model is a more suitable forecasting approach than the other compared models.

Grain Consumption Forecasting: One Modified MLR Model Combined with Time Series Forecasting Theory

In the classical multivariate prediction model, most research studies focused on the selection of relevant behaviour factors and the stability of historical data for improving the predicting accuracy of the main behaviour factor, and the historical data of the main behaviour factor have never been considered as one relevant behaviour factor, which in fact can be the first key impact factor; besides, the historical data can directly predict the main behaviour in the time series forecasting model, such as the ARIMA model. In this paper, one modified MLR model combined with time series forecasting theory is presented and applied in grain consumption forecasting. In the proposed model, to improve the current grain consumption forecasting, how to select impact factors is also discussed by combining the grey relational degree and Pearson correlation coefficient with given weights, and the optimal preprocessing parameter by the moving average filtering is computed for eliminating the abnormal points and stabilizing the data. Finally, the selected main impact factors are inputted to the proposed modified MLR model for forecasting grain consumption. Simulation results have shown that the five-year mean absolute percentage error of ration and feed grain is 2.34% and 3.27%, respectively, and the prediction accuracy has improved up to 2 times compared with the BP model and LSTM model. Moreover, the robustness of the model is verified by prediction analysis at different time intervals of historical data.

The Bayesian Forecasting of the Bridge Deflection Based on Constant Mean Discount Model

Subject to various factors under loading, bridges appear to be discrete. Thus, it is unavoidable to take the practical bridge into consideration with regard to the bridge deflection forecasting. Given this, the Bayesian dynamic forecasting theory is introduced to forecast the bridge deflection. Since the bridge deflection can stay stable in a long term, create constant mean discount Bayesian conditional equation and observational equation and deduce the Bayesian posterior probability of the bridge deflection conditional parameters on the basis of the prior information of the parameters. With recursive deduction, the conditional parameters keep updating as observational data are imported. The results of Bayesian forecasting comprise values and confidence interval, which makes it more instructive. Finally, practical examples are adopted to examine the superior performance of Bayesian dynamic forecasting theory.

The Ordered Network Structure and Prediction Summary for M≥7 Earthquakes in Xinjiang Region of China

M ≥7 earthquakes have showed an obvious commensurability and orderliness in Xinjiang of China and its adjacent region since 1800. The main orderly values are 30 a × k (k = 1,2,3), 11 ~ 12 a, 41 ~ 43 a, 18 ~ 19 a, and 5 ~ 6 a. In the guidance of the information forecasting theory of Wen-Bo Weng, based on previous research results, combining ordered network structure analysis with complex network technology, we focus on the prediction summary of M ≥ 7 earthquakes by using the ordered network structure, and add new information to further optimize network, hence construct the 2D- and 3D-ordered network structure of M ≥ 7 earthquakes. In this paper, the network structure revealed fully the regularity of seismic activity of M ≥ 7 earthquakes in the study region during the past 210 years. Based on this, the Karakorum M7.1 earthquake in 1996, the M7.9 earthquake on the frontier of Russia, Mongol, and China in 2003, and two Yutian M7.3 earthquakes in 2008 and 2014 were predicted successfully. At the same time, a new prediction opinion is presented that the future two M ≥ 7 earthquakes will probably occur around 2019 - 2020 and 2025 - 2026 in this region. The results show that large earthquake occurred in defined region can be predicted. The method of ordered network structure analysis produces satisfactory results for the mid-and-long term prediction of M ≥ 7 earthquakes.