A detailed analysis of CICIDS2017 dataset for designing Intrusion Detection Systems

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Many Intrusion Detection Systems (IDS) has been proposed in the current decade. To evaluate the effectiveness of the IDS Canadian Institute of Cybersecurity presented a state of art dataset named CICIDS2017, consisting of latest threats and features. The dataset draws attention of many researchers as it represents threats which were not addressed by the older datasets. While undertaking an experimental research on CICIDS2017, it has been found that the dataset has few major shortcomings. These issues are sufficient enough to biased the detection engine of any typical IDS. This paper explores the detailed characteristics of CICIDS2017 dataset and outlines issues inherent to it.Finally, it also presents a combined dataset by eliminating such issues for better classification and detection of any future intrusion detection engine.

     

     


     

  • Keywords


    CICIDS2017, Intrusion Detection Systems, IDS, Class Imbalance Problem, Recent Dataset for IDS

  • References


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Article ID: 22797
 
DOI: 10.14419/ijet.v7i3.24.22797




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