Anomaly User Detection via Comprehensive Keystroke Features Optimization
Meng Li, Bin Lin Wu, Zhengcai Qin · 2018
This paper aims at the problem of anomaly user detection, in which a novel, effective and comprehensive feature extraction method is proposed. Instead of existing keystroke timing information from the dataset, three types of new features are extracted in our approach for a better description of user keystroke characteristics. Moreover, an AdaBoost based algorithm is used to generate an optimized anomaly user detection model based on comprehensive keystroke features. This model contains the optimal weights of multiple alternative weak classifiers acquired through training. What's more, it effectively integrates several types of features in the form of combinations, each of which describes different inherent characteristics of keystroke processes. Both the proposed features and method are examined on CMU keystroke dynamics benchmark dataset which ensures the comparison on same grounds. Through extensive comparisons on the same dataset, the results illustrate that the proposed method has a great performance against the state-of-the-art techniques in terms of average equal error rate (EER). Furthermore, the CPU time during our optimized model generation and testing were recorded to demonstrate that the time spent is acceptable.