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- ItemMethods increasing inherent resistance of ECC designs against horizontal attacks(Amsterdam [u.a.] : Elsevier Science, 2020) Kabin, Ievgen; Dyka, Zoya; Klann, Dan; Langendoerfer, PeterDue to the nature of applications such as critical infrastructure and the Internet of Things etc. side channel analysis attacks are becoming a serious threat. Side channel analysis attacks take advantage from the fact that the behaviour of crypto implementations can be observed and provides hints that simplify revealing keys. A new type of SCA is the so called horizontal differential SCA. In this paper we investigate two different approaches to increase the inherent resistance of our hardware accelerator for the kP operation. The first approach aims at reducing the impact of the addressing in our design by realizing a regular schedule of the addressing. In the second approach, we investigated how the formula used to implement the multiplication of GF(2n)-elements influences the results of horizontal DPA attacks against a Montgomery kP-implementation. We implemented 5 designs with different partial multipliers, i.e. based on different multiplication formulae. We used two different technologies, i.e. a 130 and a 250 nm technology, to simulate power traces for our analysis. We show that the implemented multiplication formula influences the success of horizontal attacks significantly. The combination of these two approaches leads to the most resistant design. For the 250 nm technology only 2 key candidates could be revealed with a correctness of about 70% which is a huge improvement given the fact that for the original design 7 key candidates achieved a correctness of more than 90%. For our 130 nm technology no key candidate was revealed with a correctness of more than 60%.
- ItemKafka-ML: Connecting the data stream with ML/AI frameworks(Amsterdam [u.a.] : Elsevier Science, 2022) Martín, Cristian; Langendoerfer, Peter; Zarrin, Pouya Soltani; Díaz, Manuel; Rubio, BartoloméMachine Learning (ML) and Artificial Intelligence (AI) depend on data sources to train, improve, and make predictions through their algorithms. With the digital revolution and current paradigms like the Internet of Things, this information is turning from static data to continuous data streams. However, most of the ML/AI frameworks used nowadays are not fully prepared for this revolution. In this paper, we propose Kafka-ML, a novel and open-source framework that enables the management of ML/AI pipelines through data streams. Kafka-ML provides an accessible and user-friendly Web user interface where users can easily define ML models, to then train, evaluate, and deploy them for inferences. Kafka-ML itself and the components it deploys are fully managed through containerization technologies, which ensure their portability, easy distribution, and other features such as fault-tolerance and high availability. Finally, a novel approach has been introduced to manage and reuse data streams, which may eliminate the need for data storage or file systems.
- ItemPrediction of solar particle events with SRAM-based soft error rate monitor and supervised machine learning(Amsterdam [u.a.] : Elsevier Science, 2020) Chen, J.; Lange, T.; Andjelkovic, M.; Simevski, A.; Krstic, M.This work introduces an embedded approach for the prediction of Solar Particle Events (SPEs) in space applications by combining the real-time Soft Error Rate (SER) measurement with SRAM-based detector and the offline trained machine learning model. The proposed approach is intended for the self-adaptive fault-tolerant multiprocessing systems employed in space applications. With respect to the state-of-the-art, our solution allows for predicting the SER 1 h in advance and fine-grained hourly tracking of SER variations during SPEs as well as under normal conditions. Therefore, the target system can activate the appropriate mechanisms for radiation hardening before the onset of high radiation levels. Based on the comparison of five different machine learning algorithms trained with the public space flux database, the preliminary results indicate that the best prediction accuracy is achieved with the recurrent neural network (RNN) with long short-term memory (LSTM). © 2020 The Authors
- ItemA survey on Bluetooth multi-hop networks(Amsterdam [u.a.] : Elsevier Science, 2019) Todtenberg, Nicole; Kraemer, RolfBluetooth was firstly announced in 1998. Originally designed as cable replacement connecting devices in a point-to-point fashion its high penetration arouses interest in its ad-hoc networking potential. This ad-hoc networking potential of Bluetooth is advertised for years - but until recently no actual products were available and less than a handful of real Bluetooth multi-hop network deployments were reported. The turnaround was triggered by the release of the Bluetooth Low Energy Mesh Profile which is unquestionable a great achievement but not well suited for all use cases of multi-hop networks. This paper surveys the tremendous work done on Bluetooth multi-hop networks during the last 20 years. All aspects are discussed with demands for a real world Bluetooth multi-hop operation in mind. Relationships and side effects of different topics for a real world implementation are explained. This unique focus distinguishes this survey from existing ones. Furthermore, to the best of the authors’ knowledge this is the first survey consolidating the work on Bluetooth multi-hop networks for classic Bluetooth technology as well as for Bluetooth Low Energy. Another individual characteristic of this survey is a synopsis of real world Bluetooth multi-hop network deployment efforts. In fact, there are only four reports of a successful establishment of a Bluetooth multi-hop network with more than 30 nodes and only one of them was integrated in a real world application - namely a photovoltaic power plant. © 2019 The Authors
- ItemHigh-resolution net load forecasting for micro-neighbourhoods with high penetration of renewable energy sources(Amsterdam [u.a.] : Elsevier Science, 2020) Kobylinski, P.; Wierzbowski, M.; Piotrowski, K.Though extensive, the literature on electrical load forecasting lacks reports on studies focused on existing residential micro-neighbourhoods comprising small numbers of single-family houses equipped with solar panels. This paper provides a full description of an ANN-based model designed to predict short-term high-resolution (15-min intervals) micro-scale residential net load profiles. Since it seems especially relevant due to the specificity of local autocorrelations in load signal, in this paper we put stress on the systematic approach to feature selection in the context of lagged signal. We performed a case study of a real micro-neighbourhood comprising only 75 single-family houses. The obtained average prediction error was equivalent to 5.4 per cent of the maximal measured net load. The issues, i.e.: (1) the feasibility of micro-scale residential load forecasting taking into account renewable energy penetration, (2) the feasibility to predict net load with dense temporal resolution of 15 min, (3) the feature selection problem, (4) the proposed prosumption- and comparison-oriented prediction model key performance measure, could be of interest to engineers designing energy balancing systems for local smart grids. © 2019 The Authors