Specification-based anomaly detection: a new approach for detecting network intrusions
Proceedings of the 9th ACM conference on Computer and communications security
Bayesian approaches to failure prediction for disk drives
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Software Reliability as a Function of User Execution Patterns
HICSS '99 Proceedings of the Thirty-second Annual Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Using Execution Trace Data to Improve Distributed Systems
ICSM '02 Proceedings of the International Conference on Software Maintenance (ICSM'02)
Anomalies as Precursors of Field Failures
ISSRE '03 Proceedings of the 14th International Symposium on Software Reliability Engineering
Data mining approaches for intrusion detection
SSYM'98 Proceedings of the 7th conference on USENIX Security Symposium - Volume 7
Software Reliability Models: Assumptions, Limitations, and Applicability
IEEE Transactions on Software Engineering
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How to predict failures from software input is still a tough issue. Two models, namely surface model and structure model, are presented in this paper to predict failure by applying the Maximum Entropy Principle. The surface model forecasts a failure from the statistical co-occurrence between input and failure, while the structure model does from the statistical cause-effect between fault and failure. To evaluate the models, precision is applied and 17 testing experiments are conducted on 5 programs. Based on the experiments, the surface model and structure model get an average precision of 0.876 and 0.858, respectively.