Open Access
Peer-Reviewed
Original Research
A Feature Clustering Based Dimensionality Reduction for Intrusion Detection (fcbdr)
Abstract
This work discusses the ap proach for intrusion detection and classification by devising a membership function, inspired from Yung, Jung, & Shie-Jue (2014) and used in this work to carry the dimensionality reduction of processes present in the training set in evolutionary approach . The reduced process representation may then be used to perform classification and prediction for detecting intrusion. It is seen that the reduced representation of processes retains the system call distribution of the initial process. Experiment results show the proposed approach is better compared to existing approaches and helps in effective identification of U2R and R2L attacks.
Keywords
Classifier
Malicious
Intrusion
System Call
Fuzzy Feature
Declarations & Ethics
Funding:
This research received academic dissemination support through ESCAP / JournalsHub publishing programs.
Conflicts of Interest:
The authors declare no competing financial or institutional interests.
Peer Review:
Double-blind peer reviewed by international subject specialists.
License:
Creative Commons Attribution 4.0 International (CC BY 4.0).
How to Cite This Article
APA / MLA / BibTeX
Kumar, et al. (2017). A Feature Clustering Based Dimensionality Reduction for Intrusion Detection (fcbdr). IADIS International Journal on Computer Science and Information Systems, 12(1). https://doi.org/10.33965/ijcsis_2017_v12i1_04
Kumar, et al. "A Feature Clustering Based Dimensionality Reduction for Intrusion Detection (fcbdr)." IADIS International Journal on Computer Science and Information Systems, vol. 12, no. 1, 2017. https://doi.org/10.33965/ijcsis_2017_v12i1_04
Kumar, et al. "A Feature Clustering Based Dimensionality Reduction for Intrusion Detection (fcbdr)." IADIS International Journal on Computer Science and Information Systems 12, no. 1 (2017). https://doi.org/10.33965/ijcsis_2017_v12i1_04