Volume 16 (2024)
Volume 15 (2023)
Volume 14 (2022)
Volume 13 (2021)
Volume 12 (2020)
Volume 11 (2019)
Volume 10 (2018)
Volume 9 (2017)
Volume 8 (2016)
Volume 7 (2015)
Volume 6 (2014)
Volume 5 (2013)
Volume 4 (2012)
Volume 3 (2011)
Volume 2 (2010)
Volume 1 (2009)
A Graph-based Online Feature Selection to Improve Detection of New Attacks

Hajar Dastanpour; Ali Fanian

Volume 14, Issue 2 , July 2022, , Pages 115-130

https://doi.org/10.22042/isecure.2022.14.2.1

Abstract
  Today, intrusion detection systems are used in the networks as one of the essential methods to detect new attacks. Usually, these systems deal with a broad set of data and many features. Therefore, selecting proper features and benefitting from previously learned knowledge is suitable for efficiently ...  Read More

OT-Feature Extraction on Scrambled Images with Instantaneous Clustering for CBIR Scheme in Cloud Computing

K. Nalini Sujantha Bel; I.Shatheesh Sam

Volume 13, Issue 1 , January 2021, , Pages 1-17

https://doi.org/10.22042/isecure.2020.209056.497

Abstract
  A novel feature extraction algorithm using Otsu’s Threshold (OT-features) on scrambled images and the Instantaneous Clustering (IC-CBIR) approach is proposed for Content-Based Image Retrieval in cloud computing. Images are stored in the cloud in an encrypted or scrambled form to preserve the privacy ...  Read More

CEMD: A Cluster-based Ensemble Motif Discovery Tool

Sumayia Al-Anazi; Isra Al-Turaiki; Najwa Altwaijry

Volume 12, Issue 3 , November 2020, , Pages 29-36

https://doi.org/10.22042/isecure.2021.271073.623

Abstract
  Motif discovery is a challenging problem in bioinformatics. It is an essential step towards understanding gene regulation. Although numerous algorithms and tools have been proposed in the literature, the accuracy of motif finding is still low. In this paper, we tackle the motif discovery problem using ...  Read More

A novel local search method for microaggregation

R. Mortazavi; S. Jalili

Volume 7, Issue 1 , January 2015, , Pages 15-26

https://doi.org/10.22042/isecure.2015.7.1.3

Abstract
  In this paper, we propose an effective microaggregation algorithm to produce a more useful protected data for publishing. Microaggregation is mapped to a clustering problem with known minimum and maximum group size constraints. In this scheme, the goal is to cluster n records into groups of at least ...  Read More