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Microservices can be broadly defined as the design of service-oriented software using a set of small services. In a microservice architecture, application complexity is distributed among narrowly focused and independently deployable units of computation. Such complexity can result in security vulnerabilities. Trustworthiness is also an issue when dealing with microservices. Moreover, there may be gaps in existing legal frameworks with regard to this technology. Solutions to these issues must seek balance between security and performance

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Network anomalies, circumstances in which the network behavior deviates from its normal operational baseline, can be due to various factors such as network overload conditions, malicious/hostile activities, denial of service attacks, and network intrusions. New detection schemes based on machine learning principles are therefore desirable as they can learn the nature of normal traffic behavior and autonomously adapt to variations in the structure of 'normality' as well as recognize the significant deviations as suspicious or anomalous events. The main advantages of these techniques are that, in principle, they are not restricted to any specific environment and that they can provide a way of detecting unknown attacks. Detection performance is directly correlated with the traffic model quality, in terms of ability of representing the traffic behavior from its most characterizing internal dynamics. Starting from these ideas, we developed a two-stage anomaly detection strategy based on multiple distributed sensors located throughout the network. By using Independent Component Analysis, the first step, modeled as a Blind Source Separation problem, extracts the fundamental traffic components (the 'source' signals), corresponding to the independent traffic dynamics, from the multidimensional time series incoming from the sensors, corresponding to the perceived 'mixed/aggregate' effect of traffic on their interfaces. These components will be used to build the baseline traffic profiles needed in the second supervised phase, based on a binary classification scheme (detection is casted into an anomalous/normal classification problem) driven by machine learning-inferred decision trees. Copyright © 2013 John Wiley & Sons, Ltd

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