{'fraction_non_alphanumeric': 0.014492753623188406, 'fraction_numerical': 0.0, 'mean_word_length': 5.580952380952381, '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}}
Segmenting LRD Traffic with Wavelets and the Schwarz Information Criterion

Network traffic is expected to change its fractal parameters over time. An algorithm for segmenting traffic into regions with homogeneous characteristics would be useful for the development of fractalaware network algorithms. Starting from the wavelet-based Abry-Veitch estimator, we have added a change-point detection algorithm based on information theory concepts, a redundant wavelet transform, and an automatic alignment procedure based on the Hough transform in order to detect changes in the variance structure of network traffic. We present the results obtained with some synthetic and real traffic traces.