Modern techniques in intrusion and DoS (Denial of Service) detection tend to be either supervised or semi-supervised, i.e., they require training and labelled data. In this work, we study the problem of correlating security attacks with anomalies reported at runtime by a fully unsupervised outlier detection module, i.e., a component that does not require any training at all. Through a concrete proof-of-concept case study, we demonstrate that unsupervised anomaly detection is both efficient and effective, but still, it needs to be combined with additional mechanisms to yield a complete intrusion detection and prevention solution.

We present the Elton tool, a publicly available cloud resource elasticity management system tailored to NoSQL databases. Elton is integrated in the Ganetimgr web platform, and offers an easy to use web interface, through which monitoring and horizontal scaling of NoSQL databases can be performed and what-if analysis queries are enabled. Elton uses Markov Decision Processes (MDPs) as the underlying modeling framework, and encapsulates state-of-the-art horizontal scaling policies that offer different trade-offs between performance and monetary deployment cost. Its main novelty is that it employs probabilistic model checking to allow for both efficient elasticity decisions and analysis of scaling actions and serves as a case study about the benefits of model checking in online decision making and analysis.

The focus of this work is the on-demand resource provisioning in cloud computing, which is commonly referredto as cloud elasticity. Although a lot of effort has been invested in developing systems and mechanisms that enable elasticity, the elasticity decision policies tend to be designed without quantifying or guaranteeing the quality of their operation. We present an approach towards the development of more formalized and dependable elasticity policies. We make two distinct contributions. First, we propose an extensible approach to enforcing elasticity through the dynamic instantiation and online quantitative verification of Markov Decision Processes(MDP) using probabilistic model checking. Second, various concrete elasticity models and elasticity policies are studied. We evaluate the decision policies using traces from a realNoSQL database cluster under constantly evolving externalload. We reason about the behaviour of different modelling and elasticity policy options and we show that our proposal can improve upon the state-of-the-art in significantly decreasing under-provisioning while avoiding over-provisioning.