Automated diagnosis of known and unknown soft-failure in user devices using transformed Signatures and single classifier architecture

We present an automated solution for rapid diagnosis of both known and unknown “soft-failures” in network User Devices (UDs). A multiclass classifier is first trained with the known faults and during diagnosis, the unknown faults are clustered to determine the existence of a new fault. Then, in an iterative process, the classifier is re-trained with the newly detected fault. The system relies on 410 features long Normalized Statistical Signature (NSSs) for fault characterization. Since, the high dimensionality of the NSS can create model overfitting, we propose EigenNSS, a transformed signature with lower dimensions and minimum information loss. The system is evaluated with live network data of 17 emulated UD faults. The results show an overall detection accuracy of 97.2%with minimum false positives and dimensionality reduction of 93.9%. Also, compared with the NSS, the EigenNSS has faster training and diagnosis times suitable for on-demand as well as real-time diagnostic applications.