Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Masquerade Detection Using Truncated Command Lines
DSN '02 Proceedings of the 2002 International Conference on Dependable Systems and Networks
User re-authentication via mouse movements
Proceedings of the 2004 ACM workshop on Visualization and data mining for computer security
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Masquerade detection based upon GUI user profiling in linux systems
ASIAN'07 Proceedings of the 12th Asian computing science conference on Advances in computer science: computer and network security
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Masquerade attack refers to an attack that uses a fake identity, to gain unauthorized access to personal computer information through legitimate access identification. Automatic discovery of masqueraders is sometimes undertaken by detecting significant departures from normal user behavior. If a user's normal profile deviates from their original behavior, it could potentially signal an ongoing masquerade attack. In this paper we proposed a new framework to capture data in a comprehensive manner by collecting data in different layers across multiple applications. Our approach generates feature vectors which contain the output gained from analysis across multiple layers such as Window Data, Mouse Data, Keyboard Data, Command Line Data, File Access Data and Authentication Data. We evaluated our approach by several experiments with a significant number of participants. Our experimental results show better detection rates with acceptable false positives which none of the earlier approaches has achieved this level of accuracy so far.