{'fraction_non_alphanumeric': 0.009564293304994687, 'fraction_numerical': 0.0, 'mean_word_length': 5.408163265306122, '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}}
Detecting Malicious Insider Threats using a Null Affinity Temporal Three Dimensional Matrix Relation

A new approach for detecting malicious access to a database system is proposed and tested in this work. The proposed method relies upon manipulating usage information from database logs into three dimensional nullrelated matrix clusters that reveals new information about which sets of data items should never be related during defined temporal time frames across several applications. If access is detected in these three dimensional null-related clusters, this is an indication of illicit behavior, and further security procedures should occur. In this paper, we describe the null affinity algorithm and illustrate by several examples its use for problem decomposition and access control to data items which should not be accessed together, resulting in a new and novel way to detect malicious access that has never been proposed before.