Negative Selection and Knuth Morris Pratt Algorithm for Anomaly Detection
作者： Cesar Byron Guevara Maldonado , Matilde Santos Penas , Maria Victoria Lopez Lopez
关键词: Hidden Markov model 、 Artificial intelligence 、 Computer science 、 Set (abstract data type) 、 Data mining 、 Identification (information) 、 ENCODE 、 Knuth–Morris–Pratt algorithm 、 Anomaly detection 、 Detector 、 Machine learning 、 Simple (abstract algebra)
摘要: In this paper an algorithm for detecting anomalous behavior on computer systems is proposed. The work based information from the of authorized users who have performed various tasks a system over two years. study uses dynamic data structure that can encode current activities and their behaviors. identification most least frequent tasks, historical database each user, provides simple way creating single profile behavior. With profile, we apply negative selection techniques to obtain reasonable computational size set detectors. We then Knuth-Morris-Pratt locating detectors anomalies as indicators fraudulent This procedure has been tested real results prove effectiveness proposal motivate further research improve existing detection system.
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