Biblio

Confidentiality, Integrity, and Availability are principal keys to build any secure software. Considering the security principles during the different software development phases would reduce software vulnerabilities. This paper measures the impact of the different software quality metrics on Confidentiality, Integrity, or Availability for any given object-oriented PHP application, which has a list of reported vulnerabilities. The National Vulnerability Database was used to provide the impact score on confidentiality, integrity, and availability for the reported vulnerabilities on the selected applications. This paper includes a study for these scores and its correlation with 25 code metrics for the given vulnerable source code. The achieved results were able to correlate 23.7% of the variability in `Integrity' to four metrics: Vocabulary Used in Code, Card and Agresti, Intelligent Content, and Efferent Coupling metrics. The Length (Halstead metric) could alone predict about 24.2 % of the observed variability in ` Availability'. The results indicate no significant correlation of `Confidentiality' with the tested code metrics.

Common vulnerability scoring system (CVSS) is an industry standard that can assess the vulnerability of nodes in traditional computer systems. The metrics computed by CVSS would determine critical nodes and attack paths. However, traditional IT security models would not fit IoT embedded networks due to distinct nature and unique characteristics of IoT systems. This paper analyses the application of CVSS for IoT embedded systems and proposes an improved vulnerability scoring system based on CVSS v3 framework. The proposed framework, named CVSSIoT, is applied to a realistic IT supply chain system and the results are compared with the actual vulnerabilities from the national vulnerability database. The comparison result validates the proposed model. CVSSIoT is not only effective, simple and capable of vulnerability evaluation for traditional IT system, but also exploits unique characteristics of IoT devices.

SCADA system is an essential component for automated control and monitoring in many of the Critical Infrastructures (CI). Cyber-attacks like Stuxnet, Aurora, Maroochy on SCADA systems give us clear insight about the damage a determined adversary can cause to any country's security, economy, and health-care systems. An in-depth analysis of these attacks can help in developing techniques to detect and prevent attacks. In this paper, we focus on the assessment of SCADA vulnerabilities from the widely used National Vulnerability Database (NVD) until May 2019. We analyzed the vulnerabilities based on severity, frequency, availability, integrity and confidentiality impact, and Common Weaknesses. The number of reported vulnerabilities are increasing yearly. Approximately 89% of the attacks are the network exploits severely impacting availability of these systems. About 19% of the weaknesses are due to buffer errors due to the use of insecure and legacy operating systems. We focus on finding the answer to four key questions that are required for developing new technologies for securing SCADA systems. We believe this is the first study of its kind which looks at correlating SCADA attacks with publicly available vulnerabilities. Our analysis can provide security researchers with useful insights into SCADA critical vulnerabilities and vulnerable components, which need attention. We also propose a domain-specific vulnerability scoring system for SCADA systems considering the interdependency of the various components.

The risk of cyber-attacks exploiting vulnerable organisations has increased significantly over the past several years. These attacks may combine to exploit a vulnerability breach within a system's protection strategy, which has the potential for loss, damage or destruction of assets. Consequently, every vulnerability has an accompanying risk, which is defined as the "intersection of assets, threats, and vulnerabilities" [1]. This research project aims to experimentally compare the similarity-based ranking of cyber security information utilising a recommendation environment. The Memory-Based Collaborative Filtering technique was employed, specifically the User-Based and Item-Based approaches. These systems utilised information from the National Vulnerability Database, specifically for the identification and similarity-based ranking of cyber-security vulnerability information, relating to hardware and software applications. Experiments were performed using the Item-Based technique, to identify the optimum system parameters, evaluated through the AUC evaluation metric. Once identified, the Item-Based technique was compared with the User-Based technique which utilised the parameters identified from the previous experiments. During these experiments, the Pearson's Correlation Coefficient and the Cosine similarity measure was used. From these experiments, it was identified that utilised the Item-Based technique which employed the Cosine similarity measure, an AUC evaluation metric of 0.80225 was achieved.

The software supply chain is a source of cybersecurity risk for many commercial and government organizations. Public data may be used to inform automated tools for detecting software supply chain risk during continuous integration and deployment. We link data from the National Vulnerability Database (NVD) with open version control data for the open source project OpenSSL, a widely used secure networking library that made the news when a significant vulnerability, Heartbleed, was discovered in 2014. We apply the Alhazmi-Malaiya Logistic (AML) model for software vulnerability discovery to this case. This model predicts a sigmoid cumulative vulnerability discovery function over time. Some versions of OpenSSL do not conform to the predictions of the model because they contain a temporary plateau in the cumulative vulnerability discovery plot. This temporary plateau feature is an empirical signature of a security failure mode that may be useful in future studies of software supply chain risk.