Software development needs continuous quality control for a timely detection and removal of quality problems. This includes frequent quality assessments, which need to be automated as far as possible to be feasible. One way of automation in assessing the security of software are application scanners that test an executing software for vulnerabilities. At present, common quality assessments do not integrate such scanners for giving an overall quality statement. This paper presents an integration of application scanners into a general quality assessment method based on explicit quality models and Bayesian nets. Its applicability and the detection capabilities of common scanners are investigated in a case study with two open-source web shops.
@cite_6 propose an security assessment method that has similarities to the method in this paper. It also defines metrics and aggregates them to quality attributes. This method, however, uses -ilities'' similar to ISO 9126 that have several well-known problems. Moreover, they concentrate on the architecture of the software (white-box view) whereas this paper focuses on testing by application scanners (black-box view).
@cite_6
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A proper security metrics and measurement process is thus a means to automatize security decisions. Unfortunately, so far automatic security evaluation techniques have failed to achieve the performance of security experts. In this paper we argue security metrics are by nature highly unstable in time. Moreover, their effectiveness depends on specific target of evaluation. In this paper we elaborate this finding and we describe our experimental framework with its results. Abstract Estimating the possible impacts of security attacks on physical processes can help to rank the critical assets based on their sensitivity to performed attacks and predict their attractiveness from the attacker’s point of view. To address this challenge, this paper proposes a new method for assessing the direct and indirect impacts of attacks on cyber–physical systems (CPSs). The proposed method studies the dynamic behavior of systems in normal situation and under security attacks and evaluates the consequence propagation of attacks. The inputs to the model are control parameters including sensor readings and controller signals. The output of the model is evaluating the consequence propagation of attacks, ranking the important assets of systems based on their sensitivity to conducted attacks, and prioritizing the attacks based on their impacts on the behavior of system. The validation phase of the proposed method is performed by modeling and evaluating the consequence propagation of attacks against a boiling water power plant (BWPP). System architects need quantitative security metrics to make informed trade-off decisions involving system security. The security metrics need to provide insight on weak points in the system defense, considering characteristics of both the system and its adversaries. To provide such metrics, we formally define the ADversary View Security Evaluation (ADVISE) method. Our approach is to create an executable state-based security model of a system and an adversary that represents how the adversary is likely to attack the system and the results of such an attack. The attack decision function uses information about adversary attack preferences and possible attacks against the system to mimic how the adversary selects the most attractive next attack step. The adversary's decision involves looking ahead some number of attack steps. System architects can use ADVISE to compare the security strength of system architecture variants and analyze the threats posed by different adversaries. We demonstrate the feasibility and benefits of ADVISE using a case study. To produce quantitative model-based security metrics, we have implemented the ADVISE method in a tool that facilitates user input of system and adversary data and automatically generates executable models. There are lots of different software metrics discovered and used for defect prediction in the literature. Instead of dealing with so many metrics, it would be practical and easy if we could determine the set of metrics that are most important and focus on them more to predict defectiveness. We use Bayesian networks to determine the probabilistic influential relationships among software metrics and defect proneness. In addition to the metrics used in Promise data repository, we define two more metrics, i.e. NOD for the number of developers and LOCQ for the source code quality. We extract these metrics by inspecting the source code repositories of the selected Promise data repository data sets. At the end of our modeling, we learn the marginal defect proneness probability of the whole software system, the set of most effective metrics, and the influential relationships among metrics and defectiveness. Our experiments on nine open source Promise data repository data sets show that response for class (RFC), lines of code (LOC), and lack of coding quality (LOCQ) are the most effective metrics whereas coupling between objects (CBO), weighted method per class (WMC), and lack of cohesion of methods (LCOM) are less effective metrics on defect proneness. Furthermore, number of children (NOC) and depth of inheritance tree (DIT) have very limited effect and are untrustworthy. On the other hand, based on the experiments on Poi, Tomcat, and Xalan data sets, we observe that there is a positive correlation between the number of developers (NOD) and the level of defectiveness. However, further investigation involving a greater number of projects is needed to confirm our findings.