This article focuses on the application of conditional variational autoencoders as anomaly detectors to identify emerging threats in computer networks. Autoencoders are machine learning techniques that are used to find lower-dimensional representations, i.e. an encoding in latent space, from input space. With variational Autoencoders (VAE) this representation is not a single code word or vector but a probability distribution – greatly improving the robustness of the coding scheme. In contrast to VAE, we present a conditional variational autoencoder (CVAE), which uses the latent representation to encode regular and malicious network traffic into a bimodal distribution. While regular autoencoders are unsupervised, we require some labeled data to tune the bimodal representations, thus turning the learning problem into a semi-supervised classification task. However, unknown threats (i.e. those not contained in labeled training data) can be detected as well. In our presented case study, based on available computer network datasets (KDD99 and CIC-IDS2017), we could improve the detection of unknown threats compared to conventional approaches. Our experiments are publicly available.

Due to the ongoing trend towards a decarbonisation of energy use, the power system is expected to become the backbone of all energy sectors and thus the fundamental critical infrastructure. High penetration with distributed energy resources demands the coordination of a large number of prosumers, partly controlled by home energy management systems (HEMS), to be designed in such a way that the power system’s operational limits are not violated. On the grid level, distribution management systems (DMS) seek to keep the power system in the normal operational state. On the prosumer level, distributed HEMS optimise the internal power flows by setpoint specification of batteries, photovoltaic generators, or flexible loads. The vision of the ODiS (Organic Distribution System) initiative is to develop an architecture to operate a distribution grid reliably, with high resiliency, and fully autonomously by developing “organic” HEMS and DMS which possess multiple self-x capabilities, collectively referred to as self-management. Thus, ODiS seeks answers to the following question: How can we create the most appropriate models, techniques, and algorithms to develop novel kinds of self-configuring, self-organising, self-healing, and self-optimising DMS that are integrally coupled with the distributed HEMS? In this concept paper, the vision of ODiS is presented in detail based on a thorough review of the state of the art.

The CAP theorem from the distributed systems domain essentially states that any networked shared-data system can have only two of three desirable properties consistency, availability, and partition tolerance. The majority of systems considered in the self-improving system integration (SISSY) community falls into this domain. This position paper raises the question if there is another trade-off beyond the CAP theorem that is specific for SISSY systems. We briefly revisit various aspects of these systems such as robustness, fairness, stability, or utility and discuss the impact in example scenarios. Based on a discussion of why such a theoretical foundation is desirable, we provide an outlook on how to answer the question.