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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The application of deep recurrent networks to audio transcription has led to impressive gains in automatic speech recognition (ASR) systems. Many have demonstrated that small adversarial perturbations can fool deep neural networks into incorrectly predicting a specified target with high confidence. Current work on fooling ASR systems have focused on white-box attacks, in which the model architecture and parameters are known. In this paper, we adopt a black-box approach to adversarial generation, combining the approaches of both genetic algorithms and gradient estimation to solve the task. We achieve a 89.25 targeted attack similarity after 3000 generations while maintaining 94.6 audio file similarity.