Abstract—In today’s world, users and enterprises are facing
a growing number of internet attacks that are causing damage to
their networks. The design and implementation of efficient
intrusion detection algorithms is mandatory to minimise such
damage and to preserve the integrity and availability of
computer networks. Our study, which differs from some of the
approaches in the literature that handle anomaly detection and
misuse detection separately and, then, aggregate the outcomes, is
a novel method for intrusion detection in network traffic based
on a hybrid system that hierarchically combines anomaly
detection, misuse detection and fuzzy rules. Two techniques for
feature selection are used in the training phase, consisting first
of reducing the feature space with an Autoencoder and, then,
using the Weighted Fuzzy C-Mean Clustering Algorithm
(WFCM) to identify the relevant features that are highly
predictive in detecting malicious behaviour. These techniques
are applied to reduce the input data, which influences the
number of fuzzy rules generated. The proposed approach aims
to be an accurate and flexible detection system that minimises
the number of false alarms and increases the intrusion detection
Index Terms—Anomaly detection, deep learning, fuzzy logic, misuse detection.
The authors are with University Mohammed V Faculty of Science IPSS. B.O. 1014, Rabat, Morocco (e-mail: samiradouzi8@ gmail.com, email@example.com, Bouabid.firstname.lastname@example.org).
Cite: Samira Douzi, Ibtissam Benchaji, and Bouabid El Ouahidi, "Hybrid Approach for Intrusion Detection Using Fuzzy Association Rules Plus Anomaly and Misuse Detection," International Journal of Machine Learning and Computing vol. 8, no. 5, pp. 513-517, 2018.