Biblio

An effective way for building secure software is to embed security into software in the early stages of software development. Thus, we aim to study several evidences of code anomalies introduced during the software development phase, that may be indicators of security issues in software, such as code smells, structural complexity represented by diverse software metrics, the issues detected by static code analysers, and finally missing security best practices. To use such evidences for vulnerability prediction and removal, we first need to understand how they are correlated with security issues. Then, we need to discover how these imperfect raw data can be integrated to achieve a reliable, accurate and valuable decision about a portion of code. Finally, we need to construct a security actuator providing suggestions to the developers to remove or fix the detected issues from the code. All of these will lead to the construction of a framework, including security monitoring, security analyzer, and security actuator platforms, that are necessary for a security-aware integrated development environment (SIDE).

The complexity and ubiquity of modern computing systems is a fertile ground for anomalies, including security and privacy breaches. In this paper, we propose a new methodology that addresses the practical challenges to implement anomaly detection approaches. Specifically, it is challenging to define normal behavior comprehensively and to acquire data on anomalies in diverse cloud environments. To tackle those challenges, we focus on anomaly detection approaches based on system performance signatures. In particular, performance signatures have the potential of detecting zero-day attacks, as those approaches are based on detecting performance deviations and do not require detailed knowledge of attack history. The proposed methodology leverages an analytical performance model and experimentation, and allows to control the rate of false positives in a principled manner. The methodology is evaluated using the TPCx-V workload, which was profiled during a set of executions using resource exhaustion anomalies that emulate the effects of anomalies affecting system performance. The proposed approach was able to successfully detect the anomalies, with a low number of false positives (precision 90%-98%).