This study deals with model-based dependability transient analysis of phased mission systems. A review of the studies in the literature showed that several aspects of multiphased systems pose challenging problems to the dependability evaluation methods and tools. To attack the weak points of the state-of-the-art we propose a modeling methodology that exploits the power of the class of Markov regenerative stochastic Petri net models. By exploiting the techniques available in the literature for the analysis of the Markov Regenerative Processes, we obtain an analytical solution technique with a low computational complexity, basically dominated by the cost of the separate analysis of the system inside each phase. Last, the existence of analytical solutions allows us to derive the sensitivity functions of the dependability measures, thus providing the dependability engineer with additional means for the study of phased mission systems.

In the last decade, researchers, practitioners and companies struggled for devising mechanisms to detect cyber-security threats. Among others, those efforts originated rule-based, signature-based or supervised Machine Learning (ML) algorithms that were proven effective for detecting those intrusions that have already been encountered and characterized. Instead, new unknown threats, often referred to as zero-day attacks or zero-days, likely go undetected as they are often misclassified by those techniques. In recent years, unsupervised anomaly detection algorithms showed potential to detect zero-days. However, dedicated support for quantitative analyses of unsupervised anomaly detection algorithms is still scarce and often does not promote meta-learning, which has potential to improve classification performance. To such extent, this paper introduces the problem of zero-days and reviews unsupervised algorithms for their detection. Then, the paper applies a question-answer approach to identify typical issues in conducting quantitative analyses for zero-days detection, and shows how to setup and exercise unsupervised algorithms with appropriate tooling. Using a very recent attack dataset, we debate on i) the impact of features on the detection performance of unsupervised algorithms, ii) the relevant metrics to evaluate intrusion detectors, iii) means to compare multiple unsupervised algorithms, iv) the application of meta-learning to reduce misclassifications. Ultimately, v) we measure detection performance of unsupervised anomaly detection algorithms with respect to zero-days. Overall, the paper exemplifies how to practically orchestrate and apply an appropriate methodology, process and tool, providing even non-experts with means to select appropriate strategies to deal with zero-days.

This paper presents a class of count-and-threshold mechanisms, collectively named $\alpha$-count, which are able to discriminate between transient faults and intermittent faults in computing systems. For many years, commercial systems have been using transient fault discrimination via threshold-based techniques. We aim to contribute to the utility of count-and-threshold schemes, by exploring their effects on the system. We adopt a mathematically defined structure, which is simple enough to analyze by standard tools. $\alpha$-count is equipped with internal parameters that can be tuned to suit environmental variables (such as transient fault rate, intermittent fault occurrence patterns). We carried out an extensive behavior analysis for two versions of the count-and-threshold scheme, assuming, first, exponentially distributed fault occurrencies and, then, more realistic fault patterns.