Genetic convolutional neural network for intrusion detection systems
The high quality feature set obtained by the three-layered feature construction using the GA, FCM, CNN extractor, and a hybrid CNN and BG learning method significantly improves the final detection performance.
Abstract
Intrusion detection is the identification of unauthorized access of a computer network. This paper proposes a novel algorithm for a network intrusion detection system (NIDS) using an improved feature subset selected directly by a genetic algorithm (GA)-based exhaustive search and fuzzy C-means clustering (FCM). The algorithm identifies the bagging (BG) classifier and the convolutional neural network (CNN) model as an effective extractor by implementing the GA in combination with 5-fold cross validation (CV) to select the CNN model structure. The deep feature subset extracted by the selected CNN model is put into the BG classifier to validate the performance with the 5-fold CV. The high quality feature set obtained by the three-layered feature construction using the GA, FCM, CNN extractor, and a hybrid CNN and BG learning method significantly improves the final detection performance. Moreover, the highly reliable validation performance results achieved by the 5-fold CV procedure for the proposed algorithm imply a well-fitted application in a practical computer network environment NIDS.