Enhancing Vulnerability Reports With Automated and Augmented Description Summarization

Multiple databases and repositories exist for collecting known vulnerabilities for different systems and on different levels. However, it is not unusual that extensive time elapses, in some cases more than a year, in order to collect all information needed to perform/publish vulnerability scoring calculations for security management groups to assess and prioritize vulnerabilities for remediation. Scoring a vulnerability also requires broad knowledge about its characteristics, which is not always provided. As an alternative, this paper targets the quantitative understanding of security vulnerabilities in the context of insufficient vulnerability information. We propose a novel approach for the predictive assessment of security vulnerabilities, taking into consideration the relevant scenarios, e.g., zero day vulnerabilities, for which there is limited or no information to perform a typical vulnerability scoring. We propose a new analytical model, the Vulnerability Assessment Model (VAM), which is inspired by the Linear Discriminant Analysis and uses publicly available vulnerability databases such as the National Vulnerability Database (NVD) as a training data set. To demonstrate the applicability of our approach, we have developed a publicly available web application, the VAM Calculator. The experimental results obtained using real-world vulnerability data from the three most widely used Internet browsers show that by reducing the amount of required vulnerability information by around 50%, we can maintain the misclassification rate at approximately 5%.

Security risk assessment and prediction are critical for organisations deploying Internet of Things (IoT) devices. An absolute minimum requirement for enterprises is to verify the security risk of IoT devices for the reported vulnerabilities in the National Vulnerability Database (NVD). This paper proposes a novel risk prediction for IoT devices based on publicly available information about them. Our solution provides an easy and cost-efficient solution for enterprises of all sizes to predict the security risk of deploying new IoT devices. After an extensive analysis of the NVD records over the past eight years, we have created a unique, systematic, and balanced dataset for vulnerable IoT devices, including key technical features complemented with functional and descriptive features available from public resources. We then use machine learning classification models such as Gradient Boosting Decision Trees (GBDT) over this dataset and achieve 71% prediction accuracy in classifying the severity of device vulnerability score.KeywordsIoT Security Risk PredictionNational Vulnerability Database (NVD)CVEIoT securityMachine learning

Cybercrime affects companies worldwide, costing millions of dollars annually. The constant increase of threats and vulnerabilities raises the need to handle vulnerabilities in a prioritized manner. This prioritization can be achieved through Common Vulnerability Scoring System (CVSS), typically used to assign a score to a vulnerability. However, there is a temporal mismatch between the vulnerability finding and score assignment, which motivates the development of approaches to aid in this aspect. We explore the use of Natural Language Processing (NLP) models in CVSS score prediction given vulnerability descriptions. We start by creating a vulnerability dataset from the National Vulnerability Database (NVD). Then, we combine text pre-processing and vocabulary addition to improve the model accuracy and interpret its prediction reasoning by assessing word importance, via Shapley values. Experiments show that the combination of Lemmatization and 5,000-word addition is optimal for DistilBERT, the outperforming model in our experiments of the NLP methods, achieving state-of-the-art results. Furthermore, specific events (such as an attack on a known software) tend to influence model prediction, which may hinder CVSS prediction. Combining Lemmatization with vocabulary addition mitigates this effect, contributing to increased accuracy. Finally, binary classes benefit the most from pre-processing techniques, particularly when one class is much more prominent than the other. Our work demonstrates that DistilBERT is a state-of-the-art model for CVSS prediction, demonstrating the applicability of deep learning approaches to aid in vulnerability handling. The code and data are available at https://github.com/Joana-Cabral/.

Prior research has shown that public vulnerability systems such as US National Vulnerability Database (NVD) rely on a manual, time-consuming, and error-prone process which has led to inconsistencies and delays in releasing final vulnerability results. This work provides an approach to curate vulnerability reports in real-time and map textual vulnerability reports to machine readable structured vulnerability attribute data. Designed to support the time consuming human analysis done by vulnerability databases, the system leverages the Common Vulnerabilities and Exposures (CVE) list of vulnerabilities and the vulnerability attributes described by the National Institute of Standards and Technology (NIST) Vulnerability Description Ontology (VDO) framework. Our work uses Natural Language Processing (NLP), Machine Learning (ML) and novel Information Theoretical (IT) methods to provide automated techniques for near real-time publishing, and characterization of vulnerabilities using 28 attributes in 5 domains. Experiment results indicate that vulnerabilities can be evaluated up to 95 hours earlier than using manual methods, they can be characterized with F-Measure values over 0.9, and the proposed automated approach could save up to 47% of the time spent for CVE characterization.

The lack of appropriate cyber security measures deployed on Internet of Things (IoT) makes these devices prone to security issues. Consequently, the timely identification and detection of these compromised devices become crucial. Machine learning (ML) models which are used to monitor devices in a network have made tremendous strides. However, most of the research in profiling and identification uses the same data for training and testing. Hence, a slight change in the data renders most learning algorithms to work poorly. In this article, we study a transferability approach based on the concept of transductive transfer learning for IoT device profiling and identification. Notably, this type of transfer learning works by explicitly assigning labels to the test data in the target domain by using the test feature space in the target domain, with training data from the source domain. Specifically, we propose a three-component system comprising: 1) the device type identification; 2) the vulnerability assessment; and 3) the visualization module. The device type identification component uses the underlying concept of transductive transfer learning where the trained model is transferred to a remote lab for testing. A variety of ML models are evaluated with respect to accuracy, precision, recall, and F1-score in order to determine which are the most suitable for the proposed transferability profiling. Furthermore, the vulnerability of the predicted device type is also assessed by using three vulnerability databases: 1) Vulners; 2) National Vulnerability Database (NVD); and 3) IBM X-Force. Finally, the results from the vulnerability assessment are visualized and displayed on a dashboard.

A multi-target approach to estimate software vulnerability characteristics and severity scores

Cyber attacks cause great damage to our national security, ranging from individual internet user to biggest governmental/industrial organizations, such as Equifax (Data Breach 145.5 Million Accounts, reported in July 2017) or Uber (Data Breach 57 Million Records, reported in November 2017). The cyber assault has significantly increased in breadth and depth. This paper introduces CVExplorer, a novel interactive system for visualizing cybersecurity threats reported in the National Vulnerability Database. The proposed system aims to work as a reporting and alerting tool that can help enhance the security against cyber attacks can potentially reduce network vulnerabilities. The CVExplorer system containing multiple linked views allows users to visualize the relationships of various dimensions in the large number of vulnerability reports, such as types and levels of vulnerability, vendors, and products. The CVExplorer provides an intuitive interface and supports a range of interactive features, such as filtering and ordering by vulnerability severity ratings, allowing users to narrow down topics of interest quickly. To demonstrate the effectiveness of the proposed system, we demonstrate the CVExplorer on two case studies of Common Vulnerabilities and Exposures retrieved from the National Vulnerability Database.

Extracting knowledge from cyber threat intelligence is essential for understanding cyber threats and implementing proactive defense measures. However, there is a lack of open datasets in the Chinese cybersecurity field that support both entity and relation extraction tasks. This paper addresses this gap by analyzing vulnerability description texts, which are standardized and knowledge-dense, to create a vulnerability knowledge ontology comprising 13 entities and 15 relations. We annotated 27,311 unique vulnerability description sentences from the China National Vulnerability Database, resulting in a dataset named BVTED for cybersecurity knowledge triple extraction tasks. BVTED contains 97,391 entities and 69,614 relations, with entities expressed in a mix of Chinese and English. To evaluate the dataset’s value, we trained five deep learning-based named entity recognition models, two relation extraction models, and two joint entity–relation extraction models on BVTED. Experimental results demonstrate that models trained on this dataset achieve excellent performance in vulnerability knowledge extraction tasks. This work enhances the extraction of cybersecurity knowledge triples from mixed Chinese and English threat intelligence corpora by providing a comprehensive ontology and a new dataset, significantly aiding in the mining, analysis and utilization of the knowledge embedded in cyber threat intelligence.

Following the coordinated vulnerability disclosure model, a vulnerability in open source software (OSS) is sug-gested to be fixed "silently", without disclosing the fix until the vulnerability is disclosed. Yet, it is crucial for OSS users to be aware of vulnerability fixes as early as possible, as once a vulnerability fix is pushed to the source code repository, a malicious party could probe for the corresponding vulnerability to exploit it. In practice, OSS users often rely on the vulnerability disclosure information from security advisories (e.g., National Vulnerability Database) to sense vulnerability fixes. However, the time between the availability of a vulnerability fix and its disclosure can vary from days to months, and in some cases, even years. Due to manpower constraints and the lack of expert knowledge, it is infeasible for OSS users to manually analyze all code changes for vulnerability fix detection. Therefore, it is essential to identify vulnerability fixes automatically and promptly. In a first-of-its-kind study, we propose VulFixMiner, a Transformer-based approach, capable of automatically extracting semantic meaning from commit-level code changes to identify silent vulnerability fixes. We construct our model using sampled commits from 204 projects, and evaluate using the full set of commits from 52 additional projects. The evaluation results show that VulFixMiner outperforms various state-of-the-art baselines in terms of AUC (i.e., 0.81 and 0.73 on Java and Python dataset, respectively) and two effort-aware performance metrics (i.e., EffortCost, P <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opt</inf> ). Especially, with an effort of inspecting 5% of total LOC, VulFixMiner can identify 49% of total vulnerability fixes. Additionally, with manual verification of sampled commits that were identified as vulnerability fixes, but not marked as such in our dataset, we observe that 35% (29 out of 82) of the commits are for fixing vulnerabilities, indicating VulFixMiner is also capable of identifying unreported vulnerability fixes.

Threat intelligence named entity recognition techniques based on few-shot learning

Based on the Failure Mode and Effects Analysis (FMEA) method, this paper identifies the potential causes that lead to the failure of a Web application built on the WordPress platform. Both software vulnerabilities identified in the U.S. National Vulnerability Database (NVD) and other platform administration and configuration processes that can be exploited in cyber-attacks against the Web application are considered. Finally, measures to eliminate potential security breaches are proposed in the form of a best practice guide for managing sensitive data and increasing the level of security for this type of application.

Increasing usage of Information Technology (IT) applications in distributed environment is leading to an increase in security exploits. Vulnerabilities related information is also available on open web in an unstructured format that developers may leverage to fix security weaknesses in their IT applications. SQL Injection (SQLi) is one of the topmost vulnerabilities impacting the security of IT applications. We propose an approach to identify information about SQLi in text using text summarization to process any length of text, and a supervised machine learning model to automate the classification of SQLi. To validate the proposed approach, we created a dataset of 100,019 entries that includes 50,010 entries of SQLi from the National Vulnerability Database, 25,010 near negatives related to other cyber security vulnerabilities, and 24,999 data entries that are unrelated to cyber security. The selected Random Forest model was also tested identify SQLi from Web and Twitter text.

Chapter 9 - Security Metrics for Risk-aware Automated Policy Management

Cloud computing's rapid development has favored the emergence of many other technologies like OpenStack, which is the most popular open-source cloud management software. OpenStack has received a lot of praise lately thanks to its ease of use and its vibrant community, but it has also started garnering attention in the national vulnerability database. Furthermore, OpenStack has a logical architecture in which, the degree of interconnectedness within and between the components is a source of many security concerns. To prevent the damages that can be caused by the combination of these security issues, we proposed a vulnerability tree security analysis of OpenStack's logical architecture that allowed us to generate ready-to-use vulnerability trees of the major services or components of the architecture. We also suggested an amendment of OpenStack's vulnerability naming, because the current naming does not cope well with our proposal.

A common software product line strategy involves plugin-based web systems that support simple and quick incorporation of custom behaviors. As a result, they have been widely adopted to create web-based applications. Indeed, the popularity of ecosystems that support plugin-based development (e.g., WordPress) is largely due to the number of customization options available as community-contributed plugins. However, plugin-related vulnerabilities tend to be recurrent, exploitable and hard to be detected and may lead to severe consequences for the customized product. Hence, there is a need to further understand such vulnerabilities to enable preventing relevant security threats. Therefore, we conducted an exploratory study to characterize vulnerabilities caused by plugins in web-based systems. To this end, we went over WordPress vulnerability bulletins cataloged by the National Vulnerability Database as well as associated patches maintained by the WordPress plugins repository. We identified the main types of vulnerabilities caused by plugins as well as their impact and the size of the patch to fix the vulnerability. Moreover, we identified the most common security-related topics discussed among WordPress developers. We observed that, while plugin-related vulnerabilities may have severe consequences and might remain unnoticed for years before being fixed, they can commonly be mitigated with small and localized changes to the source code. The characterization helps to provide an understanding on how typical plugin-based vulnerabilities manifest themselves in practice. Such information can be helpful to steer future research on plugin-based vulnerability detection and prevention.