Toward Modeling Lightweight Intrusion Detection System Through Correlation-Based Hybrid Feature Selection

Modeling IDS have been focused on improving detection model(s) in terms of (i)

detection model design

based on classification algorithm, clustering algorithm, and soft computing techniques such as Artificial Neural Networks (ANN), Hidden Markov Model (HMM), Support Vector Machines (SVM), K-means clustering, Fuzzy approaches and so on and (ii)

feature selection

through wrapper and filter approaches. However these approaches require large overhead due to heavy computations for both feature selection and cross validation method to minimize generalization errors. In addition selected

feature set

varies according to detection model so that they are inefficient for modeling lightweight IDS. Therefore this paper proposes a new approach to model lightweight Intrusion Detection System (IDS) based on a new feature selection approach named Correlation-based Hybrid Feature Selection (CBHFS) which is able to significantly decrease training and testing times while retaining high detection rates with low false positives rates as well as stable feature selection results. The experimental results on KDD 1999 intrusion detection datasets show the feasibility of our approach to enable one to modeling lightweight IDS.