@article{PrasseKnaebelMachlicaetal.2019,
  author    = {Prasse, Paul and Knaebel, Rene and Machlica, Lukas and Pevny, Tomas and Scheffer, Tobias},
  title     = {Joint detection of malicious domains and infected clients},
  series = {Machine learning},
  volume    = {108},
  journal   = {Machine learning},
  number    = {8-9},
  publisher = {Springer},
  address   = {Dordrecht},
  issn      = {0885-6125},
  doi       = {10.1007/s10994-019-05789-z},
  pages     = {1353 -- 1368},
  year      = {2019},
  abstract  = {Detection of malware-infected computers and detection of malicious web domains based on their encrypted HTTPS traffic are challenging problems, because only addresses, timestamps, and data volumes are observable. The detection problems are coupled, because infected clients tend to interact with malicious domains. Traffic data can be collected at a large scale, and antivirus tools can be used to identify infected clients in retrospect. Domains, by contrast, have to be labeled individually after forensic analysis. We explore transfer learning based on sluice networks; this allows the detection models to bootstrap each other. In a large-scale experimental study, we find that the model outperforms known reference models and detects previously unknown malware, previously unknown malware families, and previously unknown malicious domains.},
  language  = {en}
}
@article{GautamZhangLandwehretal.2021,
  author    = {Gautam, Khem Raj and Zhang, Guoqiang and Landwehr, Niels and Adolphs, Julian},
  title     = {Machine learning for improvement of thermal conditions inside a hybrid ventilated animal building},
  series = {Computers and electronics in agriculture : COMPAG online ; an international journal},
  volume    = {187},
  journal   = {Computers and electronics in agriculture : COMPAG online ; an international journal},
  publisher = {Elsevier Science},
  address   = {Amsterdam [u.a.]},
  issn      = {0168-1699},
  doi       = {10.1016/j.compag.2021.106259},
  pages     = {10},
  year      = {2021},
  abstract  = {In buildings with hybrid ventilation, natural ventilation opening positions (windows), mechanical ventilation rates, heating, and cooling are manipulated to maintain desired thermal conditions. The indoor temperature is regulated solely by ventilation (natural and mechanical) when the external conditions are favorable to save external heating and cooling energy. The ventilation parameters are determined by a rule-based control scheme, which is not optimal. This study proposes a methodology to enable real-time optimum control of ventilation parameters. We developed offline prediction models to estimate future thermal conditions from the data collected from building in operation. The developed offline model is then used to find the optimal controllable ventilation parameters in real-time to minimize the setpoint deviation in the building. With the proposed methodology, the experimental building's setpoint deviation improved for 87\% of time, on average, by 0.53 degrees C compared to the current deviations.},
  language  = {en}
}
@article{ChenLangeAndjelkovicetal.2022,
  author    = {Chen, Junchao and Lange, Thomas and Andjelkovic, Marko and Simevski, Aleksandar and Lu, Li and Krstic, Milos},
  title     = {Solar particle event and single event upset prediction from SRAM-based monitor and supervised machine learning},
  series = {IEEE transactions on emerging topics in computing / IEEE Computer Society, Institute of Electrical and Electronics Engineers},
  volume    = {10},
  journal   = {IEEE transactions on emerging topics in computing / IEEE Computer Society, Institute of Electrical and Electronics Engineers},
  number    = {2},
  publisher = {Institute of Electrical and Electronics Engineers},
  address   = {[New York, NY]},
  issn      = {2168-6750},
  doi       = {10.1109/TETC.2022.3147376},
  pages     = {564 -- 580},
  year      = {2022},
  abstract  = {The intensity of cosmic radiation may differ over five orders of magnitude within a few hours or days during the Solar Particle Events (SPEs), thus increasing for several orders of magnitude the probability of Single Event Upsets (SEUs) in space-borne electronic systems. Therefore, it is vital to enable the early detection of the SEU rate changes in order to ensure timely activation of dynamic radiation hardening measures. In this paper, an embedded approach for the prediction of SPEs and SRAM SEU rate is presented. The proposed solution combines the real-time SRAM-based SEU monitor, the offline-trained machine learning model and online learning algorithm for the prediction. With respect to the state-of-the-art, our solution brings the following benefits: (1) Use of existing on-chip data storage SRAM as a particle detector, thus minimizing the hardware and power overhead, (2) Prediction of SRAM SEU rate one hour in advance, with the fine-grained hourly tracking of SEU variations during SPEs as well as under normal conditions, (3) Online optimization of the prediction model for enhancing the prediction accuracy during run-time, (4) Negligible cost of hardware accelerator design for the implementation of selected machine learning model and online learning algorithm. The proposed design is intended for a highly dependable and self-adaptive multiprocessing system employed in space applications, allowing to trigger the radiation mitigation mechanisms before the onset of high radiation levels.},
  language  = {en}
}