Browsing ODA Open Digital Archive by Author "Hagos, Desta Haileselassie"

Now showing items 1-8 of 8

    • Advanced passive operating system fingerprinting using machine learning and deep learning 

      Hagos, Desta Haileselassie; Løland, Martin V.; Yazidi, Anis; Kure, Øivind; Engelstad, Paal E. (International Conference on Computer Communications and Networks (ICCCN); 2020 29th International Conference on Computer Communications and Networks (ICCCN), Journal article; Peer reviewed, 2020-09-30)
      Securing and managing large, complex enterprise network infrastructure requires capturing and analyzing network traffic traces in real-time. An accurate passive Operating System (OS) fingerprinting plays a critical role ...

    • Classification of Delay-based TCP Algorithms From Passive Traffic Measurements 

      Hagos, Desta Haileselassie; Engelstad, Paal E.; Yazidi, Anis (IEEE International Symposium on Network Computing and Applications;, Conference object, 2019-12-19)
      Identifying the underlying TCP variant from passive measurements is important for several reasons, e.g., exploring security ramifications, traffic engineering in the Internet, etc. In this paper, we are interested in ...

    • A Crowd-Based Intelligence Approach for Measurable Security, Privacy, and Dependability in Internet of Automated Vehicles with Vehicular Fog 

      Rauniyar, Ashish; Hagos, Desta Haileselassie; Shrestha, Manish (International Journal of Mobile Information Systems;Volume 2018, Journal article; Peer reviewed, 2018-03-01)
      With the advent of Internet of things (IoT) and cloud computing technologies, we are in the era of automation, device-to-device (D2D) and machine-to-machine (M2M) communications. Automated vehicles have recently gained a ...

    • A Deep Learning Approach to Dynamic Passive RTT 

      Hagos, Desta Haileselassie; Engelstad, Paal E.; Yazidi, Anis; Griwodz, Carsten (2019 IEEE 38th International Performance Computing and Communications Conference (IPCCC);, Conference object, 2019)
      The Round-Trip Time (RTT) is a property of the path between a sender and a receiver communicating with Transmission Control Protocol (TCP) over an IP network and over the public Internet. The end-to-end RTT value influences ...

    • A Deep Learning Approach to Dynamic Passive RTT Prediction Model for TCP 

      Hagos, Desta Haileselassie; Engelstad, Paal E.; Yazidi, Anis; Griwodz, Carsten (IEEE International Conference Performance, Computing and Communications (IPCCC);, Conference object, 2020-01-16)
      The Round-Trip Time (RTT) is a property of the path between a sender and a receiver communicating with Transmission Control Protocol (TCP) over an IP network and over the public Internet. The end-to-end RTT value influences ...

    • General TCP state inference model from passive measurements using machine learning techniques 

      Hagos, Desta Haileselassie; Engelstad, Paal E.; Yazidi, Anis; Kure, Øivind (IEEE Access;VOLUME 6, 2018, Journal article; Peer reviewed, 2018-05-04)
      Many applications in the Internet use the reliable end-to-end Transmission Control Protocol (TCP) as a transport protocol due to practical considerations. There are many different TCP variants widely in use, and each ...

    • A Machine Learning-based Tool for Passive OS Fingerprinting with TCP Variant as a Novel Feature 

      Hagos, Desta Haileselassie; Yazidi, Anis; Kure, Øivind; Engelstad, Paal (IEEE Internet of Things Journal;Volume: 8, Issue: 5, Peer reviewed; Journal article, 2020-09-15)
      With the emergence of Internet of Things (IoT), securing and managing large, complex enterprise network infrastructure requires capturing and analyzing network traffic traces in real-time. An accurate passive Operating ...

    • Recurrent Neural Network-Based Prediction of TCP Transmission States from Passive Measurements 

      Hagos, Desta Haileselassie; Engelstad, Paal E.; Yazidi, Anis; Kure, Øivind (2018 IEEE 17th International Symposium on Network Computing and Applications (NCA);, Chapter; Chapter; Peer reviewed, 2018-11-29)
      Long Short-Term Memory (LSTM) neural networks are a state-of-the-art techniques when it comes to sequence learning and time series prediction models. In this paper, we have used LSTM-based Recurrent Neural Networks (RNN) ...