Utilizing attack enumerations to study SDN/NFV vulnerabilities

International audienceSeveral cybersecurity attack enumerations area available today. These enumerations present lists of known attack patterns (CAPEC), security weaknesses (CWE) or cybersecurity vulnerabilities (CVE). These enumerations are being developed separately and manually. In this paper, we present our efforts in determine the relations between enumerations automatically. We rely on text-based, graph-based and recommendation-based approaches. Then we present of using the prediction in recommending related attacks to SDN/NFV security issues. Experimental results showed that we can predict the relations at high AU C and F − 1 scores. Furthermore, the results gave us some insights about how the enumerations are created and linked, and some suggestions to improve the process in the future

A Look at the Time Delays in CVSS Vulnerability Scoring

This empirical paper examines the time delays that occur between the publication of Common Vulnerabilities and Exposures (CVEs) in the National Vulnerability Database (NVD) and the Common Vulnerability Scoring System (CVSS) information attached to published CVEs. According to the empirical results based on regularized regression analysis of over eighty thousand archived vulnerabilities, (i) the CVSS content does not statistically influence the time delays, which, however, (ii) are strongly affected by a decreasing annual trend. In addition to these results, the paper contributes to the empirical research tradition of software vulnerabilities by a couple of insights on misuses of statistical methodology.</p

An Investigation About the Absence of Validation on Security Quantification Methods

To understand the actions that lead to successful attacks and also how they can be mitigated, researchers should identify and measure the factors that influence both attackers and victims. Quantifying security is particularly important to construct relevant metrics that support the decisions that need to be made to protect systems and networks. In this work, we aimed at investigating the lack of validation in security quantification methods. Different approaches to security quantification were examined and 57 papers are classified. The results show that most of papers seek to measure generic and complex targets like measuring network security or the security of an entire organization, however, the incidence of validation attempts is higher in works that propose the quantification of specific targets

Efficiency and Automation in Threat Analysis of Software Systems

Context: Security is a growing concern in many organizations. Industries developing software systems plan for security early-on to minimize expensive code refactorings after deployment. In the design phase, teams of experts routinely analyze the system architecture and design to find potential security threats and flaws. After the system is implemented, the source code is often inspected to determine its compliance with the intended functionalities. Objective: The goal of this thesis is to improve on the performance of security design analysis techniques (in the design and implementation phases) and support practitioners with automation and tool support.Method: We conducted empirical studies for building an in-depth understanding of existing threat analysis techniques (Systematic Literature Review, controlled experiments). We also conducted empirical case studies with industrial participants to validate our attempt at improving the performance of one technique. Further, we validated our proposal for automating the inspection of security design flaws by organizing workshops with participants (under controlled conditions) and subsequent performance analysis. Finally, we relied on a series of experimental evaluations for assessing the quality of the proposed approach for automating security compliance checks. Findings: We found that the eSTRIDE approach can help focus the analysis and produce twice as many high-priority threats in the same time frame. We also found that reasoning about security in an automated fashion requires extending the existing notations with more precise security information. In a formal setting, minimal model extensions for doing so include security contracts for system nodes handling sensitive information. The formally-based analysis can to some extent provide completeness guarantees. For a graph-based detection of flaws, minimal required model extensions include data types and security solutions. In such a setting, the automated analysis can help in reducing the number of overlooked security flaws. Finally, we suggested to define a correspondence mapping between the design model elements and implemented constructs. We found that such a mapping is a key enabler for automatically checking the security compliance of the implemented system with the intended design. The key for achieving this is two-fold. First, a heuristics-based search is paramount to limit the manual effort that is required to define the mapping. Second, it is important to analyze implemented data flows and compare them to the data flows stipulated by the design

Enhancing Trust –A Unified Meta-Model for Software Security Vulnerability Analysis

Over the last decade, a globalization of the software industry has taken place which has facilitated the sharing and reuse of code across existing project boundaries. At the same time, such global reuse also introduces new challenges to the Software Engineering community, with not only code implementation being shared across systems but also any vulnerabilities it is exposed to as well. Hence, vulnerabilities found in APIs no longer affect only individual projects but instead might spread across projects and even global software ecosystem borders. Tracing such vulnerabilities on a global scale becomes an inherently difficult task, with many of the resources required for the analysis not only growing at unprecedented rates but also being spread across heterogeneous resources. Software developers are struggling to identify and locate the required data to take full advantage of these resources. The Semantic Web and its supporting technology stack have been widely promoted to model, integrate, and support interoperability among heterogeneous data sources. This dissertation introduces four major contributions to address these challenges: (1) It provides a literature review of the use of software vulnerabilities databases (SVDBs) in the Software Engineering community. (2) Based on findings from this literature review, we present SEVONT, a Semantic Web based modeling approach to support a formal and semi-automated approach for unifying vulnerability information resources. SEVONT introduces a multi-layer knowledge model which not only provides a unified knowledge representation, but also captures software vulnerability information at different abstract levels to allow for seamless integration, analysis, and reuse of the modeled knowledge. The modeling approach takes advantage of Formal Concept Analysis (FCA) to guide knowledge engineers in identifying reusable knowledge concepts and modeling them. (3) A Security Vulnerability Analysis Framework (SV-AF) is introduced, which is an instantiation of the SEVONT knowledge model to support evidence-based vulnerability detection. The framework integrates vulnerability ontologies (and data) with existing Software Engineering ontologies allowing for the use of Semantic Web reasoning services to trace and assess the impact of security vulnerabilities across project boundaries. Several case studies are presented to illustrate the applicability and flexibility of our modelling approach, demonstrating that the presented knowledge modeling approach cannot only unify heterogeneous vulnerability data sources but also enables new types of vulnerability analysis