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A membership function for intrusion and anomaly detection of low frequency attacks

Published: 01 October 2018 Publication History

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Abstract

The ultimate objective of intrusion detection problem is to identify surprising intrusions that compromise networks. Determining intrusions through the application of classifiers or detection algorithm requires, finding similarity as one of the important operations. This paper brings to the discussion a membership function that can be used for the learning process to attain better accuracies for low-frequency attack classes in the given dataset. Two membership functions are proposed in this work for unsupervised learning. The first one is utilized for prior learning and the second one is utilized for post-learning. The learning process is an un-supervised technique that aims at dimensionality transformation.

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  • (2021)Design and Analysis of activation functions used in deep learning modelsThe 7th International Conference on Engineering & MIS 202110.1145/3492547.3492575(1-5)Online publication date: 11-Oct-2021
  • (2021)A Survey of Similarity Measures for Time stamped Temporal DatasetsInternational Conference on Data Science, E-learning and Information Systems 202110.1145/3460620.3460754(193-197)Online publication date: 5-Apr-2021
  • (2021)Similarity Association Pattern Mining in Transaction DatabasesInternational Conference on Data Science, E-learning and Information Systems 202110.1145/3460620.3460752(180-184)Online publication date: 5-Apr-2021
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cover image ACM Other conferences
DATA '18: Proceedings of the First International Conference on Data Science, E-learning and Information Systems
October 2018
274 pages
ISBN:9781450365369
DOI:10.1145/3279996
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 01 October 2018

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Author Tags

  1. anomaly
  2. classification
  3. detection
  4. intrusion
  5. literature survey
  6. membership
  7. new approach for intrusion detection

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Cited By

View all
  • (2021)Design and Analysis of activation functions used in deep learning modelsThe 7th International Conference on Engineering & MIS 202110.1145/3492547.3492575(1-5)Online publication date: 11-Oct-2021
  • (2021)A Survey of Similarity Measures for Time stamped Temporal DatasetsInternational Conference on Data Science, E-learning and Information Systems 202110.1145/3460620.3460754(193-197)Online publication date: 5-Apr-2021
  • (2021)Similarity Association Pattern Mining in Transaction DatabasesInternational Conference on Data Science, E-learning and Information Systems 202110.1145/3460620.3460752(180-184)Online publication date: 5-Apr-2021
  • (2019)UTTAMA: An Intrusion Detection System Based on Feature Clustering and Feature TransformationFoundations of Science10.1007/s10699-019-09589-525:4(1049-1075)Online publication date: 5-Mar-2019

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