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    A Review on Intrusion Detection Systems and Techniques

    Autor: 
    Singh Bhati, Nitesh
    ;
    Khari, Manju
    ;
    García-Díaz, Vicente
    ;
    Verdú, Elena
    Fecha: 
    12/2020
    Palabra clave: 
    Intrusion Detection System (IDS); machine learning; fuzzy logic; population-based algorithm; colony-based algorithm; neural network; JCR; Scopus
    Tipo de Ítem: 
    review
    URI: 
    https://reunir.unir.net/handle/123456789/11198
    DOI: 
    http://dx.doi.org/10.1142/S0218488520400140
    Dirección web: 
    https://www.worldscientific.com/doi/abs/10.1142/S0218488520400140
    Resumen:
    An Intrusion Detection System (IDS) is a network security system that detects, identifies, and tracks an intruder or an invader in a network. As the usage of the internet is growing every day in our society, the IDS is becoming an essential part of the network security system. Therefore, the proper research and implementation of IDSs are required. Today, with the help of improved technologies at our disposal, many solutions have been found to create many intrusion detection systems. However, it is difficult to identify the perfect solution from the vast options we have available. Hence, motivated by the need of a better security system, this paper presents a survey of different published solutions that have been developed and/or researched on the topic of intrusion detection techniques during the period from 2000 to 2019, including the accuracy of the output. With the help of this survey, an all-inclusive view of the different papers would be at one's disposal.
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