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Browsing by Subject "Anomaly detection"

Now showing items 1-12 of 12

    • Anomaly detection in IoT devices via monitoring of supply current 

      Myridakis D., Spathoulas G., Kakarountas A., Schoinianakisy D., Luekeny J. (2018)
      This work presents a methodology that correlates the supply current of a smart device to its functional characteristics in order to detect a manufacturing or a security anomaly in IoT devices. It is proven that awareness ...

    • Anomaly detection via blockchained deep learning smart contracts in industry 4.0 

      Demertzis K., Iliadis L., Tziritas N., Kikiras P. (2020)
      The complexity of threats in the ever-changing environment of modern industry is constantly increasing. At the same time, traditional security systems fail to detect serious threats of increasing depth and duration. ...

    • Anomaly detection via blockchained deep learning smart contracts in industry 4.0. 

      Konstantinos Demertzis; Lazaros Iliadis; Nikos Tziritas; Panagiotis Kikiras (2020)
      The complexity of threats in the ever-changing environment of modern industry is constantly increasing. At the same time, traditional security systems fail to detect serious threats of increasing depth and duration. ...

    • Cyber-Typhon: An Online Multi-task Anomaly Detection Framework 

      Demertzis, K.; Iliadis, L.; Kikiras, P.; Tziritas, N. (2019)
      According to the Greek mythology, Typhon was a gigantic monster with one hundred dragon heads, bigger than all mountains. His open hands were extending from East to West, his head could reach the sky and flames were coming ...

    • Cyber-Typhon: An Online Multi-task Anomaly Detection Framework 

      Demertzis K., Iliadis L., Kikiras P., Tziritas N. (2019)
      According to the Greek mythology, Typhon was a gigantic monster with one hundred dragon heads, bigger than all mountains. His open hands were extending from East to West, his head could reach the sky and flames were coming ...

    • Deep Hybrid Learning for Anomaly Detection in Behavioral Monitoring 

      Georgakopoulos S.V., Tasoulis S.K., Vrahatis A.G., Moustakidis S., Tsaopoulos D.E., Plagianakos V.P. (2022)
      The task of understanding human behavior through intelligent systems is crucial in various domains from medical health and well-being to financial and social platforms. In this work, we propose a complete framework that ...

    • Monitoring supply current thresholds for smart device's security enhancement 

      Myridakis D., Spathoulas G., Kakarountas A., Schinianakis D., Lueken J. (2019)
      The rapid growth of connected devices and the sensitive data they generate poses a significant challenge for manufacturers seeking to comprehensively protect their devices from attack. This paper presents a study and its ...

    • Proactive, Correlation Based Anomaly Detection at the Edge 

      Fountas P., Kolomvatsos K. (2021)
      Data management at the edge of the network is a significant research subject. Devices being active at the Internet of Things (IoT) can collect data and transfer them to a set of edge nodes for further processing. There, ...

    • Smart devices security enhancement via power supply monitoring 

      Myridakis D., Spathoulas G., Kakarountas A., Schinianakis D. (2020)
      The continuous growth of the number of Internet of Things (IoT) devices and their inclusion to public and private infrastructures has introduced new applciations to the market and our day-to-day life. At the same time, ...

    • Supply current monitoring for anomaly detection on IoT devices 

      Myridakis D., Spathoulas G., Kakarountas A. (2017)
      This paper presents results from the correlation of the supply current of a smart device to its functional characteristics in order to detect a manufacturing or an operational anomaly. Awareness of the typical operation ...

    • Switching Gaussian Mixture Variational RNN for Anomaly Detection of Diverse CDN Websites 

      Dai L., Chen W., Liu Y., Argyriou A., Liu C., Lin T., Wang P., Xu Z., Chen B. (2022)
      To conduct service quality management of industry devices or Internet infrastructures, various deep learning approaches have been used for extracting the normal patterns of multivariate Key Performance Indicators (KPIs) ...

    • Variational restricted Boltzmann machines to automated anomaly detection 

      Demertzis K., Iliadis L., Pimenidis E., Kikiras P. (2022)
      Data-driven methods are implemented using particularly complex scenarios that reflect in-depth perennial knowledge and research. Hence, the available intelligent algorithms are completely dependent on the quality of the ...