%0 Journal Article %A Chuanjia HOU %A Jing HAN %A Tong JIA %A Yifan WU %A Ying LI %T Feedback‑Aware Anomaly Detection Through Logs for Large‑Scale Software Systems %D 2021 %R 10.12142/ZTECOM.202103011 %J ZTE Communications %P 88-94 %V 19 %N 3 %X

One particular challenge for large?scale software systems is anomaly detection. System logs are a straightforward and common source of information for anomaly detection. Existing log?based anomaly detectors are unusable in real?world industrial systems due to high false?positive rates. In this paper, we incorporate human feedback to adjust the detection model structure to reduce false positives. We apply our approach to two industrial large?scale systems. Results have shown that our approach performs much better than state?of?the-art works with 50% higher accuracy. Besides, human feedback can reduce more than 70% of false positives and greatly improve detection precision.

%U http://zte.magtechjournal.com/EN/10.12142/ZTECOM.202103011