@article{MAKHILLJEAS202015519101,
    title = {Intrusion Detection Systems Data Classification by
Possibilistic C-Means Method},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {15},
    number = {5},
    pages = {1170-1174},
    year = {2020},
    issn = {1816-949x},
    doi = {jeasci.2020.1170.1174},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2020.1170.1174},
    author = {Aini Suri and},
    keywords = {Classification,intrusion detection system,possilibilistic C-means,internal resources,integrity,parameters},
    abstract = {Internet Detection System (IDS) can be used to detect a malicious attempt on the network or system
aims to access restricted information or exploit internal resources, monitors and analyze user activity and
maintain data integrity. Based on its detection mechanism, IDS can be grouped into offline and real-time IDS.
On offline IDS, a saved labeled data set as in KDD Cup’99 data set are used to measure the fitness factor of
the rules on the identifier and we can analyze it to prevent some attacks happen in the future. Some
classification methods have been widely used to classify IDS data set. It was used to recognize the pattern of
the attacks so that we can differ between normal and unusual behaviors. On this research, we use Possibillistic
C-Means (PCM) method as a classifier for KDD Cup’99 data set. Based on the experiment, the best
classification results was reach on 13% training data set with accuracy 68,63%. The accuracy is still low since
PCM use several values of parameters and it affects the algorithm performances when the chosen values are
not the best ones.}
    }