Predicting the Discovery Pattern of Publically Known Exploited Vulnerabilities

Vulnerabilities with publically known exploits typically form 2-7% of all vulnerabilities reported for a given software version. With a smaller number of known exploited vulnerabilities compared with the total number of vulnerabilities, it is more difficult to model and predict when a vulnerability with a known exploit will be reported. In this paper, we introduce an approach for predicting the discovery pattern of publically known exploited vulnerabilities using all publically known vulnerabilities reported for a given software. Eight commonly used vulnerability discovery models (VDMs) and one neural network model (NNM) were utilized to evaluate the prediction capability of our approach. We compared their predictions results with the scenario when only exploited vulnerabilities were used for prediction. Our results show that, in terms of prediction accuracy, out of eight software we analyzed, our approach led to more accurate results in seven cases. Only in one case, the accuracy of our approach was worse by 1.6%

Cluster-based Vulnerability Assessment Applied to Operating Systems

Organizations face the issue of how to best allocate their security resources. Thus, they need an accurate method for assessing how many new vulnerabilities will be reported for the operating systems (OSs) they use in a given time period. Our approach consists of clustering vulnerabilities by leveraging the text information within vulnerability records, and then simulating the mean value function of vulnerabilities by relaxing the monotonic intensity function assumption, which is prevalent among the studies that use software reliability models (SRMs) and nonhomogeneous Poisson process (NHPP) in modeling. We applied our approach to the vulnerabilities of four OSs: Windows, Mac, IOS, and Linux. For the OSs analyzed in terms of curve fitting and prediction capability, our results, compared to a power-law model without clustering issued from a family of SRMs, are more accurate in all cases we analyzed

Cluster-based Vulnerability Assessment Applied to Operating Systems

Organizations face the issue of how to best allocate their security resources. Thus, they need an accurate method for assessing how many new vulnerabilities will be reported for the operating systems (OSs) they use in a given time period. Our approach consists of clustering vulnerabilities by leveraging the text information within vulnerability records, and then simulating the mean value function of vulnerabilities by relaxing the monotonic intensity function assumption, which is prevalent among the studies that use software reliability models (SRMs) and nonhomogeneous Poisson process (NHPP) in modeling. We applied our approach to the vulnerabilities of four OSs: Windows, Mac, IOS, and Linux. For the OSs analyzed in terms of curve fitting and prediction capability, our results, compared to a power-law model without clustering issued from a family of SRMs, are more accurate in all cases we analyzed

Cluster-based Vulnerability Assessment of Operating Systems and Web Browsers

Organizations face the issue of how to best allocate their security resources. Thus, they need an accurate method for assessing how many new vulnerabilities will be reported for the operating systems (OSs) and web browsers they use in a given time period. Our approach consists of clustering vulnerabilities by leveraging the text information within vulnerability records, and then simulating the mean value function of vulnerabilities by relaxing the monotonic intensity function assumption, which is prevalent among the studies that use software reliability models (SRMs) and nonhomogeneous Poisson process (NHPP) in modeling. We applied our approach to the vulnerabilities of four OSs (Windows, Mac, IOS, and Linux) and four web browsers (Internet Explorer, Safari, Firefox, and Chrome). Out of the total eight OSs and web browsers we analyzed using a power-law model issued from a family of SRMs, the model was statistically adequate for modeling in six cases. For these cases, in terms of estimation and forecasting capability, our results, compared to a power-law model without clustering, are more accurate in all cases but one

Vulnerability Prediction Capability: A Comparison between Vulnerability Discovery Models and Neural Network Models

In this paper, we introduce an approach for predicting the cumulative number of software vulnerabilities that is in most cases more accurate than vulnerability discovery models (VDMs). Our approach uses a neural network model (NNM) to model the nonlinearities associated with vulnerability disclosure. Nine common VDMs were used to compare their prediction capability with our approach. The different models were applied to vulnerabilities associated with eight well-known software (four operating systems and four web browsers). The models were assessed in terms of prediction accuracy and prediction bias. Out of eight software we analyzed, the NNM outperformed the VDMs in all the cases in terms of prediction accuracy, and provided smaller values of absolute average bias in seven cases. This study shows that NNMs are promising for accurate predictions of software vulnerabilities disclosures

Forecasting number of vulnerabilities using long short-term neural memory network

Cyber-attacks are launched through the exploitation of some existing vulnerabilities in the software, hardware, system and/or network. Machine learning algorithms can be used to forecast the number of post release vulnerabilities. Traditional neural networks work like a black box approach; hence it is unclear how reasoning is used in utilizing past data points in inferring the subsequent data points. However, the long short-term memory network (LSTM), a variant of the recurrent neural network, is able to address this limitation by introducing a lot of loops in its network to retain and utilize past data points for future calculations. Moving on from the previous finding, we further enhance the results to predict the number of vulnerabilities by developing a time series-based sequential model using a long short-term memory neural network. Specifically, this study developed a supervised machine learning based on the non-linear sequential time series forecasting model with a long short-term memory neural network to predict the number of vulnerabilities for three vendors having the highest number of vulnerabilities published in the national vulnerability database (NVD), namely microsoft, IBM and oracle. Our proposed model outperforms the existing models with a prediction result root mean squared error (RMSE) of as low as 0.072