{'fraction_non_alphanumeric': 0.021671826625386997, 'fraction_numerical': 0.0, 'mean_word_length': 4.626086956521739, '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}}
Fixed- vs. Variable-Length Patterns for Detecting Suspicious Process Behavior

This paper addresses the problem of creating patterns that can be used to model the normal behavior of a given process. The models can be used for intrusion-detection purposes. First, we present a novel method to generate input data sets that enable us to observe the normal behavior of a process in a secure environment. Second, we propose various techniques to derive either fixed-length or variable-length patterns from the input data sets. We show the advantages and drawbacks of each technique, based on the results of the experiments we have run on our testbed.