Selected papers of the sixth annual Oregon workshop on Software metrics
Temporal sequence learning and data reduction for anomaly detection
ACM Transactions on Information and System Security (TISSEC)
Transient fault detection via simultaneous multithreading
Proceedings of the 27th annual international symposium on Computer architecture
Exploring the relationship between design measures and software quality in object-oriented systems
Journal of Systems and Software
Characterizing the behavior of a program using multiple-length N-grams
Proceedings of the 2000 workshop on New security paradigms
Anomaly Detection in Embedded Systems
IEEE Transactions on Computers - Special issue on fault-tolerant embedded systems
Dual use of superscalar datapath for transient-fault detection and recovery
Proceedings of the 34th annual ACM/IEEE international symposium on Microarchitecture
Accuracy of software quality models over multiple releases
Annals of Software Engineering
Machine Learning
Predicting Fault-Proneness using OO Metrics: An Industrial Case Study
CSMR '02 Proceedings of the 6th European Conference on Software Maintenance and Reengineering
A Study on Fault-Proneness Detection of Object-Oriented Systems
CSMR '01 Proceedings of the Fifth European Conference on Software Maintenance and Reengineering
Fault Prediction Modeling for Software Quality Estimation: Comparing Commonly Used Techniques
Empirical Software Engineering
METRICS '01 Proceedings of the 7th International Symposium on Software Metrics
Building Software Quality Classification Trees: Approach, Experimentation, Evaluation
ISSRE '97 Proceedings of the Eighth International Symposium on Software Reliability Engineering
Longest prefix matching using bloom filters
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
Developing Fault Predictors for Evolving Software Systems
METRICS '03 Proceedings of the 9th International Symposium on Software Metrics
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
SWIFT: Software Implemented Fault Tolerance
Proceedings of the international symposium on Code generation and optimization
Learning from little: comparison of classifiers given little training
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Design and Evaluation of Hybrid Fault-Detection Systems
Proceedings of the 32nd annual international symposium on Computer Architecture
Capturing, indexing, clustering, and retrieving system history
Proceedings of the twentieth ACM symposium on Operating systems principles
Software-controlled fault tolerance
ACM Transactions on Architecture and Code Optimization (TACO)
Static analysis of executables to detect malicious patterns
SSYM'03 Proceedings of the 12th conference on USENIX Security Symposium - Volume 12
Anomaly-based Fault Detection System in Distributed System
SERA '07 Proceedings of the 5th ACIS International Conference on Software Engineering Research, Management & Applications
An Innovative Self-Configuration Approach for Networked Systems and Applications
AICCSA '06 Proceedings of the IEEE International Conference on Computer Systems and Applications
Anagram: a content anomaly detector resistant to mimicry attack
RAID'06 Proceedings of the 9th international conference on Recent Advances in Intrusion Detection
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The increased complexity of hardware and software resources and the asynchronous interaction among components (such as servers, end devices, network, services and software) make fault detection and recovery very challenging. In this paper, we present innovative concepts for fault detection, root cause analysis and self-healing architectures analyzing the duration of pattern transition sequences during an execution window. In this approach, all interactions among components of Pervasive Computing Systems (PCS) are monitored and analyzed. We use three-dimensional array of features to capture spatial and temporal variability to be used by an anomaly analysis engine to immediately generate an alert when abnormal behavior pattern is captured indicating some kind of software or hardware failure. The main contributions of this paper include the innovative analysis methodology and feature selection to detect and identify anomalous behavior. Evaluating the effectiveness of this approach to detect faults injected asynchronously shows a detection rate of above 99.9% with no occurrences of false alarms for a wide range of scenarios.