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On the Improvement of the Isolation Forest Algorithm for Outlier Detection with Streaming Data

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F21%3A43962149" target="_blank" >RIV/49777513:23520/21:43962149 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.3390/electronics10131534" target="_blank" >https://doi.org/10.3390/electronics10131534</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.3390/electronics10131534" target="_blank" >10.3390/electronics10131534</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    On the Improvement of the Isolation Forest Algorithm for Outlier Detection with Streaming Data

  • Original language description

    In recent years, detecting anomalies in real-world computer networks has become a more and more challenging task due to the steady increase of high-volume, high-speed and high-dimensional streaming data, for which ground truth information is not available. Efficient detection schemes applied on networked embedded devices need to be fast and memory-constrained and must be capable of dealing with concept drifts when they occur. Different approaches for unsupervised online outlier detection have been designed to deal with these circumstances in order to reliably detect malicious activity. In this paper, we introduce a novel framework called PCB-iForest, which generalized, is able to incorporate any ensemble-based online OD method to function on streaming data. Carefully engineered requirements are compared to the most popular state-of-the-art online methods with an in-depth focus on variants based on the widely accepted isolation forest algorithm, thereby highlighting the lack of a flexible and efficient solution which is satisfied by PCB-iForest. Therefore, we integrate two variants into PCB-iForest—an isolation forest improvement called extended isolation forest and a classic isolation forest variant equipped with the functionality to score features according to their contributions to a sample’s anomalousness. Extensive experiments were performed on 23 different multi-disciplinary and security-related real-world datasets in order to comprehensively evaluate the performance of our implementation compared with off-the-shelf methods. The discussion of results, including AUC, F1 score and averaged execution time metric, shows that PCB-iForest clearly outperformed the state-of-the-art competitors in 61% of cases and even achieved more promising results in terms of the tradeoff between classification and computational costs.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    <a href="/en/project/LO1506" target="_blank" >LO1506: Sustainability support of the centre NTIS - New Technologies for the Information Society</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2021

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Electronics

  • ISSN

    2079-9292

  • e-ISSN

  • Volume of the periodical

    10

  • Issue of the periodical within the volume

    13

  • Country of publishing house

    CH - SWITZERLAND

  • Number of pages

    26

  • Pages from-to

  • UT code for WoS article

    000671101000001

  • EID of the result in the Scopus database

    2-s2.0-85108447095