• 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 ...
    • Automatic Detection of Hateful Comments in Online Discussion 

      Hammer, Hugo Lewi (Peer reviewed; Journal article, 2016)
      Making violent threats towards minorities like immigrants or homosexuals is increasingly common on the Internet. We present a method to automatically detect threats of violence using machine learn- ing. A material of ...
    • Automatic detection of hateful comments in online discussion 

      Hammer, Hugo Lewi (Journal article; Peer reviewed, 2017)
      Making violent threats towards minorities like immigrants or homosexuals is increasingly common on the Internet. We present a method to automatically detect threats of violence using machine learning. A material of 24,840 ...
    • Automatic security classification by machine learning for cross-domain information exchange 

      Hammer, Hugo Lewi; Kongsgård, Kyrre Wahl; Yazidi, Anis; Bai, Aleksander; Nordbotten, Nils Agne; Engelstad, Paal E. (MILCOM IEEE Military Communications Conference;, Journal article; Peer reviewed, 2015)
      Cross-domain information exchange is necessary to obtain information superiority in the military domain, and should be based on assigning appropriate security labels to the information objects. Most of the data found ...
    • Autonomous configuration of network parameters in operating systems using evolutionary algorithms 

      Gembala, Bartosz Gembala; Yazidi, Anis; Haugerud, Hårek; Nichele, Stefano (Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems;, Chapter; Peer reviewed, 2018)
      By default, the Linux network stack is not configured for highspeed large file transfer. The reason behind this is to save memory resources. It is possible to tune the Linux network stack by increasing the network buffers ...
    • Brain connectome mapping of complex human traits and their polygenic architecture using machine learning 

      Maglanoc, Luigi Angelo; Kaufmann, Tobias; van der Meer, Dennis; Marquand, André F.; Wolfers, Thomas; Jonassen, Rune; Hilland, Eva; Andreassen, Ole Andreas; Landrø, Nils Inge; Westlye, Lars Tjelta (Biological Psychiatry;Available online 29 October 2019, Journal article; Peer reviewed, 2019-10-18)
      Background: Mental disorders and individual characteristics such as intelligence and personality are complex traits sharing a largely unknown neuronal basis. Their genetic architectures are highly polygenic and overlapping, ...
    • Detection of DNS tunneling in mobile networks using machine learning 

      Do, Van Thuan; Engelstad, Paal E.; Feng, Boning; Do, van Thanh (Lecture Notes in Electrical Engineering;Information Science and Applications 2017, Volume 424, Journal article; Peer reviewed, 2017)
      Lately, costly and threatening DNS tunnels on the mobile networks bypassing the mobile operator’s Policy and Charging Enforcement Function (PCEF), has shown the vulnerability of the mobile networks caused by the Domain ...
    • Do User (Browse and Click) Sessions Relate to Their Questions in a Domain-Specific Collection? 

      Steinhauer, Jeremy; Delcambre, Lois M.L.; Lykke, Marianne; Ådland, Marit Kristine (Lecture Notes in Computer Science;8092, Journal article; Peer reviewed, 2013)
      We seek to improve information retrieval in a domain-specific collection by clustering user sessions from a click log and then classifying later user sessions in real-time. As a preliminary step, we explore the main ...
    • Dyadic Aggregated Autoregressive Model (DASAR) for Automatic Modulation Classification 

      Pinto-Orellana, Marco Antonio; Hammer, Hugo Lewi (IEEE Access;Volume 8, Journal article; Peer reviewed, 2020-08-27)
      In this article, we presented a novel spectral estimation method, the dyadic aggregated autoregressive model (DASAR), that characterizes the spectrum dynamics of a modulated signal. DASAR enhances automatic modulation ...
    • Fog computing for sustainable smart cities in the IoT era: Caching techniques and enabling technologies - an overview 

      Zahmatkesh, Hadi; Al-Turjman, Fadi (Sustainable Cities and Society;Volume 59, August 2020, 102139, Journal article; Peer reviewed, 2020-04-29)
      In recent decade, the number of devices involved with the Internet of Things (IoT) phenomena has increased dramatically. Parallel to this, fog computing paradigm has been introduced in order to support the computational ...
    • 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 ...
    • Improving Cellular IoT Security with Identity Federation and Anomaly Detection 

      Santos, Bernardo; Dzogovic, Bruno; Feng, Boning; Jacot, Niels; Do, Thuan Van; Do, van Thanh (International Conference on Computers, Communications, and Systems (ICCCS);2020 5th International Conference on Computer and Communication Systems (ICCCS), Chapter; Conference object, 2020-06-16)
      As we notice the increasing adoption of Cellular IoT solutions (smart-home, e-health, among others), there are still some security aspects that can be improved as these devices can suffer various types of attacks that can ...
    • A Learning Automaton-based Scheme for Scheduling Domestic Shiftable Loads in Smart Grids 

      Thapa, Rajan; Lei, Jiao; Oommen, John; Yazidi, Anis (Journal article; Peer reviewed, 2017)
      In this paper, we consider the problem of scheduling shiftable loads, over multiple users, in smart electrical grids. We approach the problem, which is becoming increasingly pertinent in our present energy-thirsty society, ...
    • 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 ...
    • MachinelLearning to classify and recommend physical activity based on wearable technology 

      Shrestha, Dipak (MAUU;2020, Master thesis, 2020)
      Different wearable technology has been used to measure the physical activities of people with movement disorder through activity classification as well as recommending suitable physical activity level. A physical activity ...
    • Method to Obtain Neuromorphic Reservoir Networks from Images of in Vitro Cortical Networks 

      Mello, Gustavo; Pontes-Filho, Sidney; Sandvig, Ioanna; Valderhaug, Vibeke Devold; Zouganeli, Evi; Huse Ramstad, Ola; Sandvig, Axel; Nichele, Stefano (IEEE Symposium Series on Computational Intelligence (SSCI);, Chapter; Peer reviewed, 2020-02-20)
      In the brain, the structure of a network of neurons defines how these neurons implement the computations that underlie the mind and the behavior of animals and humans. Provided that we can describe the network of neurons ...
    • Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis 

      Maglanoc, Luigi Angelo; Kaufmann, Tobias; Jonassen, Rune; Hilland, Eva; Beck, Dani; Landrø, Nils Inge; Westlye, Lars Tjelta (Human Brain Mapping;Volume 41, Issue 1, January 2020, Journal article; Peer reviewed, 2019-09-09)
      Previous structural and functional neuroimaging studies have implicated distributed brain regions and networks in depression. However, there are no robust imaging biomarkers that are specific to depression, which may be ...
    • Toadstool: a dataset for training emotional intelligent machines playing Super Mario Bros 

      Svoren, Henrik; Thambawita, Vajira; Halvorsen, Pål; Jakobsen, Petter; Garcia-Ceja, Enrique; Noori, Farzan Majeed; Hammer, Hugo Lewi; Lux, Mathias; Riegler, Michael; Hicks, Steven (MMSys: Multimedia Systems;MMSys '20: Proceedings of the 11th ACM Multimedia Systems Conference, Chapter; Conference object; Peer reviewed, 2020)
      Games are often defined as engines of experience, and they are heavily relying on emotions, they arouse in players. In this paper, we present a dataset called Toadstool as well as a reproducible methodology to extend ...
    • VISEM: A Multimodal Video Dataset of Human Spermatozoa 

      Haugen, Trine B.; Hicks, Steven; Andersen, Jorunn Marie; Witczak, Oliwia; Hammer, Hugo Lewi; Borgli, Rune Johan; Halvorsen, Pål; Riegler, Michael (ACM MMSys conference series;, Chapter; Peer reviewed, 2019)
      Real multimedia datasets that contain more than just images or text are rare. Even more so are open multimedia datasets in medicine. Often, clinically related datasets only consist of image or videos. In this paper, we ...