Conference paper Embargoed Access

Classifying Security Attacks in IoT Networks Using Supervised Learning

Ioannou Christiana; Vasos Vasiliou


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    "embargo_date": "2021-10-31", 
    "doi": "10.1109/DCOSS.2019.00118", 
    "version": "Accepted pre-print", 
    "language": "eng", 
    "title": "Classifying Security Attacks in IoT Networks Using Supervised Learning", 
    "license": {
      "id": "CC-BY-NC-ND-4.0"
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    "notes": "This work has been partly supported by the project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 739578 (RISE \u2013 Call: H2020-WIDESPREAD-01-2016-2017-TeamingPhase2) and the Government of the Republic of Cyprus through the Directorate General for European Programmes, Coordination and Development.", 
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        "title": "Research Center on Interactive Media, Smart System and Emerging Technologies", 
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    "publication_date": "2019-10-31", 
    "creators": [
      {
        "affiliation": "Department of Computer Science, University of Cyprus and RISE - Research Center on Interactive Media, Smart Systems and Emerging Technologies Nicosia, Cyprus", 
        "name": "Ioannou Christiana"
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      {
        "affiliation": "Department of Computer Science, University of Cyprus and RISE - Research Center on Interactive Media, Smart Systems and Emerging Technologies Nicosia, Cyprus", 
        "name": "Vasos Vasiliou"
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    "description": "<p>Machine learning models have long be proposed to detect the presence of unauthorized activity within computer networks. They are used as anomaly detection techniques to detect abnormal behaviors within the network. We propose to use Support Vector Machine (SVM) learning anomaly detection model to detect abnormalities within the Internet of Things. SVM creates its normal profile hyperplane based on both benign and malicious local sensor activity. An important aspect of our work is the use of actual IoT network traffic with specific network layer attacks implemented by us. This is in contrast to other works creating supervised learning models, with generic datasets. The proposed detection model achieves up to 100% accuracy when evaluated with unknown data taken from the same network topology as it was trained and 81% accuracy when operating in an unknown topology.</p>"
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