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A hybrid heuristics artificial intelligence feature selection for intrusion detection classifiers in cloud of things

Sangaiah, A. K ; Sharif University of Technology | 2022

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  1. Type of Document: Article
  2. DOI: 10.1007/s10586-022-03629-9
  3. Publisher: Springer , 2022
  4. Abstract:
  5. Cloud computing environments provide users with Internet-based services and one of their main challenges is security issues. Hence, using Intrusion Detection Systems (IDSs) as a defensive strategy in such environments is essential. Multiple parameters are used to evaluate the IDSs, the most important aspect of which is the feature selection method used for classifying the malicious and legitimate activities. We have organized this research to determine an effective feature selection method to increase the accuracy of the classifiers in detecting intrusion. A Hybrid Ant-Bee Colony Optimization (HABCO) method is proposed to convert the feature selection problem into an optimization problem. We examined the accuracy of HABCO with BHSVM, IDSML, DLIDS, HCRNNIDS, SVMTHIDS, ANNIDS, and GAPSAIDS. It is shown that HABCO has a higher accuracy compared with the mentioned methods. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature
  6. Keywords:
  7. Ant-colony ; CoT ; IDS classifier ; Intrusion detection ; Ant colony optimization ; Artificial intelligence ; Classification (of information) ; Cloud security ; Feature extraction ; Ant colonies ; Bee colony optimizations ; Bee-colony ; Cloud-computing ; Feature selection methods ; Intrusion detection system classifier ; Intrusion Detection Systems ; Intrusion-Detection ; Optimization algorithms ; Cloud computing
  8. Source: Cluster Computing ; 2022 ; 13867857 (ISSN)
  9. URL: https://link.springer.com/article/10.1007/s10586-022-03629-9