Myths and Facts About Static Application Security Testing Tools: An Action Research at Telenor Digital

It is claimed that integrating agile and security in practice is challenging. There is the notion that security is a heavy process, requires expertise, and consumes developers’ time. These contrast with the agile vision. Regardless of these challenges, it is important for organizations to address security within their agile processes since critical assets must be protected against attacks. One way is to integrate tools that could help to identify security weaknesses during implementation and suggest methods to refactor them. We used quantitative and qualitative approaches to investigate the efficiency of the tools and what they mean to the actual users (i.e. developers) at Telenor Digital. Our findings, although not surprising, show that several barriers exist both in terms of tool’s performance and developers’ perceptions. We suggest practical ways for improvement.publishedVersio

Feature Set Selection for Improved Classification of Static Analysis Alerts

With the extreme growth in third party cloud applications, increased exposure of applications to the internet, and the impact of successful breaches, improving the security of software being produced is imperative. Static analysis tools can alert to quality and security vulnerabilities of an application; however, they present developers and analysts with a high rate of false positives and unactionable alerts. This problem may lead to the loss of confidence in the scanning tools, possibly resulting in the tools not being used. The discontinued use of these tools may increase the likelihood of insecure software being released into production. Insecure software can be successfully attacked resulting in the compromise of one or several information security principles such as confidentiality, availability, and integrity. Feature selection methods have the potential to improve the classification of static analysis alerts and thereby reduce the false positive rates. Thus, the goal of this research effort was to improve the classification of static analysis alerts by proposing and testing a novel method leveraging feature selection. The proposed model was developed and subsequently tested on three open source PHP applications spanning several years. The results were compared to a classification model utilizing all features to gauge the classification improvement of the feature selection model. The model presented did result in the improved classification accuracy and reduction of the false positive rate on a reduced feature set. This work contributes a real-world static analysis dataset based upon three open source PHP applications. It also enhanced an existing data set generation framework to include additional predictive software features. However, the main contribution is a feature selection methodology that may be used to discover optimal feature sets that increase the classification accuracy of static analysis alerts

Static source code analysis in agile development methodologies

This study investigates static code analysis for security audit in an industrial and agile settings. The case study is Telenor Digital, located in Norway. The study aims to understand the challenges for implementing a static code analysis tool from agile developers perspective. The study investigated static code analysis tools on a benchmark security test suite (NIST Juliet Test Suite) in order to make an informed decision by comparing the tools on the basis of their true positive rate and discrimination rate. Lastly, a post-evaluation of the implemented static analysis tool at Telenor was performed. The results of this work shed more light on what are the challenges for implementing a static code analysis tool for security audit in an agile settings. The findings also identify the most important factors for adopting a particular tool, the trade-offs the teams are willing to make to adopt this kind of tool and the relevant metrics for tools evaluation in order to support adoption of such tools

Bridging the Gap: A Survey and Classification of Research-Informed Ethical Hacking Tools

The majority of Ethical Hacking (EH) tools utilised in penetration testing are developed by practitioners within the industry or underground communities. Similarly, academic researchers have also contributed to developing security tools. However, there appears to be limited awareness among practitioners of academic contributions in this domain, creating a significant gap between industry and academia’s contributions to EH tools. This research paper aims to survey the current state of EH academic research, primarily focusing on research-informed security tools. We categorise these tools into process-based frameworks (such as PTES and Mitre ATT&CK) and knowledge-based frameworks (such as CyBOK and ACM CCS). This classification provides a comprehensive overview of novel, research-informed tools, considering their functionality and application areas. The analysis covers licensing, release dates, source code availability, development activity, and peer review status, providing valuable insights into the current state of research in this field