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Erschienen in:
Risk Assessment Framework: ADRIM Process Model for Global Software Development Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Comparative Evaluation of Machine Learning Algorithms for Network Intrusion Detection Using Weka

verfasst von : Nureni Ayofe Azeez, Obinna Justin Asuzu, Sanjay Misra, Adewole Adewumi, Ravin Ahuja, Rytis Maskeliunas

Erschienen in: Towards Extensible and Adaptable Methods in Computing

Verlag: Springer Singapore

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Abstract

For the past few years, it has been seen that the computer intrusion attacks are becoming more sophisticated, and the volume, velocity, and variance of traffic data have greatly increased. Because the conventional methods and tools have become impotent in the detection of intrusion attacks, most intrusion detection systems now embrace the use of machine learning tools and algorithms for efficiency. This is because of their ability to process large volume, velocity, and very high variance data. This work reviews and analyzes the performance of three out of the most commonly used machine learning algorithms in network intrusion. In this work, the performance of Naïve Bayes, decision tree, and random forest algorithms were evaluated as they were being trained and tested with the KDD CUP 1999 dataset from DARPA using a big data and machine learning tool called Weka. These classification algorithms are evaluated based on their precision, sensitivity, and accuracy.

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Vorheriges Kapitel A Proposed Method for Disruption Classification in Tokamak Using Convolutional Neural Network
Nächstes Kapitel Super-Intelligent Machine Operations in Twenty-First-Century Manufacturing Industries: A Boost or Doom to Political and Human Development?
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Metadaten
Titel
Comparative Evaluation of Machine Learning Algorithms for Network Intrusion Detection Using Weka
verfasst von
Nureni Ayofe Azeez
Obinna Justin Asuzu
Sanjay Misra
Adewole Adewumi
Ravin Ahuja
Rytis Maskeliunas
Copyright-Jahr
2018
Verlag
Springer Singapore
Buch
Towards Extensible and Adaptable Methods in Computing

Print ISBN: 978-981-13-2347-8

Electronic ISBN: 978-981-13-2348-5

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-981-13-2348-5_15

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