Opprentice

Liu, Dapeng; Zhao, Youjian; Xu, Haowen; Sun, Yongqian; Pei, Dan; Luo, Jiao; Jing, Xiaofan; Feng, Ming

doi:10.1145/2815675.2815679

Cited by 198 publications

(20 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because these algorithms typically have simple assumptions for applicable KPIs, expert's efforts need to be involved to pick a suitable detector for a given KPI, and then fine-tune the detector's parameters based on the training data. Simple ensemble of these detectors, such as majority vote [8] and normalization [35], do not help much either according to [25]. As a result, these detectors see only limited use in the practice.…”

Section: Previous Workmentioning

confidence: 99%

“…Supervised ensemble approaches. To circumvent the hassle of algorithm/parameter tuning for traditional statistical anomaly detectors, supervised ensemble approaches, EGADS [21] and Opprentice [25], were proposed. They train anomaly classifiers using the user feedbacks as labels and using anomaly scores output by traditional detectors as features.…”

Section: Previous Workmentioning

confidence: 99%

“…Opprentice [25] is an ensemble supervised framework using Random Forest classifier. On datasets similar to ours, Opprentice is reported to consistently and significantly outperform 14 anomaly detectors based on traditional statistical models (e.g., [6,17,18,24,26,27,31,40,41]), with in total 133 enumerated configurations of hyper-parameters for these detectors.…”

Section: Overall Performancementioning

confidence: 99%

See 2 more Smart Citations

Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications

Chen

Zhao

et al. 2018

Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18

Self Cite

612

327

View full text Add to dashboard Cite

To ensure undisrupted business, large Internet companies need to closely monitor various KPIs (e.g., Page Views, number of online users, and number of orders) of its Web applications, to accurately detect anomalies and trigger timely troubleshooting/mitigation. However, anomaly detection for these seasonal KPIs with various patterns and data quality has been a great challenge, especially without labels. In this paper, we proposed Donut, an unsupervised anomaly detection algorithm based on VAE. Thanks to a few of our key techniques, Donut greatly outperforms a state-of-arts supervised ensemble approach and a baseline VAE approach, and its best F-scores range from 0.75 to 0.9 for the studied KPIs from a top global Internet company. We come up with a novel KDE interpretation of reconstruction for Donut, making it the first VAE-based anomaly detection algorithm with solid theoretical explanation.

show abstract

Section: Previous Workmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

Section: Overall Performancementioning

confidence: 99%

See 1 more Smart Citation

Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications

Chen

Zhao

et al. 2018

Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18

Self Cite

612

327

View full text Add to dashboard Cite

show abstract

“…It is obvious that the selected studies were published in 92 different outlets, where the vast majority of studies were published in IEEE Access (6 publications), Procedia Computer Science (5 publications), IEEE Internet of Things Journal (4 publications), [125], Contagio [112], UPC [112], ISOT [112], MCFP [112], KPI [140], Telemetry [141], WUIL [142], DARPA 1998 [143], GureKDD [144], GSB [145], Intel Lab [145], Indoor WSN [145], RPL-NDDS17 [146], NIMS [90], UNB-CICT [50], Digiturk [147], Labris [147], IoT Botnet [148], Moore [149], ISCXVPN 2016 [149], CICIDS 2018 [52], Malicious URLs [107], TRAbID [53], CIDDS-001 [104] 25…”

Section: Mapping Selected Studies By Publication Typesmentioning

confidence: 99%

Ensemble learning for intrusion detection systems: A systematic mapping study and cross-benchmark evaluation

Tama

Lim

2021

Computer Science Review

View full text Add to dashboard Cite

Intrusion detection systems (IDSs) are intrinsically linked to a comprehensive solution of cyberattacks prevention instruments. To achieve a higher detection rate, the ability to design an improved detection framework is sought after, particularly when utilizing ensemble learners. Designing an ensemble often lies in two main challenges such as the choice of available base classifiers and combiner methods. This paper performs an overview of how ensemble learners are exploited in IDSs by means of systematic mapping study. We collected and analyzed 124 prominent publications from the existing literature. The selected publications were then mapped into several categories such as years of publications, publication venues, datasets used, ensemble methods, and IDS techniques. Furthermore, this study reports and analyzes an empirical investigation of a new classifier ensemble approach, called stack of ensemble (SoE) for anomaly-based IDS. The SoE is an ensemble classifier that adopts parallel architecture to combine three individual ensemble learners such as random forest, gradient boosting machine, and extreme gradient boosting machine in a homogeneous manner. The performance significance among classification algorithms is statistically examined in terms of their Matthews correlation coefficients, accuracies, false positive rates, and area under ROC curve metrics. Our study fills the gap in current literature concerning an up-to-date systematic mapping study, not to mention an extensive empirical evaluation of the recent advances of ensemble learning techniques applied to IDSs.

show abstract

“…Anomaly detection is also a critical task with lots of efforts from both industrial practitioners and academic researchers [19,32,41,48]. Specifically, there is a body of work focusing on anomaly detection in key performance indicators (KPIs) in order to monitor systems and identify incidents [9,23,25,30,46]. For instance, Li et al [46] propose ROCKA, a robust and rapid time series clustering algorithm to cluster KPIs for anomaly detection.…”

Section: Related Work 71 Incident Identificationmentioning

confidence: 99%

Efficient incident identification from multi-dimensional issue reports via meta-heuristic search

Luo

Qin

et al. 2020

Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Softw

View full text Add to dashboard Cite

Opprentice

Cited by 198 publications

References 39 publications

Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications

Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications

Ensemble learning for intrusion detection systems: A systematic mapping study and cross-benchmark evaluation

Efficient incident identification from multi-dimensional issue reports via meta-heuristic search

Contact Info

Product

Resources

About