{'fraction_non_alphanumeric': 0.020134228187919462, 'fraction_numerical': 0.006711409395973154, 'mean_word_length': 4.828125, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
An improved intrusion detection system based on neural network

Neural network approach is an advanced methodology used for intrusion detection. Adaptive Resonance Theory (ART) is one kind of neural networks featuring selforganization of stable recognition categories and on-line learning. ART 2-A is a fast clustering algorithm of the ART family. But ART 2-A cannot process categorical values, while network traffic data collected for intrusion detection always contain this kind of data. An improved model of ART 2-A is thus proposed to deal with categorical data properly. To verify the accuracy of this new model, experiments were carried out on the KDD Cup 99 data set. Results showed that the performance of the new model was satisfactory.