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- abstractAugust 2021
Data Science on Blockchains
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data MiningPages 4025–4026https://doi.org/10.1145/3447548.3470800Blockchain technology garners an ever-increasing interest of researchers in various domains that benefit from scalable cooperation among trust-less parties. As blockchains and their applications proliferate, so do the complexity and volume of data ...
- abstractAugust 2021
Toward Explainable Deep Anomaly Detection
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data MiningPages 4056–4057https://doi.org/10.1145/3447548.3470794Anomaly explanation, also known as anomaly localization, is as important as, if not more than, anomaly detection in many real-world applications. However, it is challenging to build explainable detection models due to the lack of anomaly-supervisory ...
- abstractAugust 2021
Anomaly and Novelty Detection, Explanation, and Accommodation (ANDEA)
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data MiningPages 4145–4146https://doi.org/10.1145/3447548.3469453The detection of, explanation of, and accommodation to anomalies and novelties are active research areas in multiple communities, including data mining, machine learning, and computer vision. They are applied in various guises including anomaly ...
- research-articleAugust 2021
Fast One-class Classification using Class Boundary-preserving Random Projections
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data MiningPages 66–74https://doi.org/10.1145/3447548.3467440Several applications, like malicious URL detection and web spam detection, require classification on very high-dimensional data. In such cases anomalous data is hard to find but normal data is easily available. As such it is increasingly common to use a ...
- research-articleAugust 2021
Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Data
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data MiningPages 1298–1308https://doi.org/10.1145/3447548.3467417We consider the problem of anomaly detection with a small set of partially labeled anomaly examples and a large-scale unlabeled dataset. This is a common scenario in many important applications. Existing related methods either exclusively fit the ...
- research-articleAugust 2021
PETGEN: Personalized Text Generation Attack on Deep Sequence Embedding-based Classification Models
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data MiningPages 575–584https://doi.org/10.1145/3447548.3467390What should a malicious user write next to fool a detection model? Identifying malicious users is critical to ensure the safety and integrity of internet platforms. Several deep learning based detection models have been created. However, malicious users ...
- research-articleAugust 2021
Fast and Accurate Partial Fourier Transform for Time Series Data
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data MiningPages 1309–1318https://doi.org/10.1145/3447548.3467293Given a time-series vector, how can we efficiently detect anomalies? A widely used method is to use Fast Fourier transform (FFT) to compute Fourier coefficients, take first few coefficients while discarding the remaining small coefficients, and ...
- research-articleAugust 2021
Deep Clustering based Fair Outlier Detection
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data MiningPages 1481–1489https://doi.org/10.1145/3447548.3467225In this paper, we focus on the fairness issues regarding unsupervised outlier detection. Traditional algorithms, without a specific design for algorithmic fairness, could implicitly encode and propagate statistical bias in data and raise societal ...
- research-articleAugust 2021
Practical Approach to Asynchronous Multivariate Time Series Anomaly Detection and Localization
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data MiningPages 2485–2494https://doi.org/10.1145/3447548.3467174Engineers at eBay utilize robust methods in monitoring IT system signals for anomalies. However, the growing scale of signals, both in volumes and dimensions, overpowers traditional statistical state-space or supervised learning tools. Thus, state-of-...
- research-articleAugust 2021
Time Series Anomaly Detection for Cyber-physical Systems via Neural System Identification and Bayesian Filtering
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data MiningPages 2858–2867https://doi.org/10.1145/3447548.3467137Recent advances in AIoT technologies have led to an increasing popularity of utilizing machine learning algorithms to detect operational failures for cyber-physical systems (CPS). In its basic form, an anomaly detection module monitors the sensor ...
- research-articleAugust 2021
Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data MiningPages 3726–3734https://doi.org/10.1145/3447548.3467125Discrete event sequences are ubiquitous, such as an ordered event series of process interactions in Information and Communication Technology systems. Recent years have witnessed increasing efforts in detecting anomalies with discrete event sequences. ...
- research-articleAugust 2021
Multivariate Time Series Anomaly Detection and Interpretation using Hierarchical Inter-Metric and Temporal Embedding
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data MiningPages 3220–3230https://doi.org/10.1145/3447548.3467075Anomaly detection is a crucial task for monitoring various status (i.e., metrics) of entities (e.g., manufacturing systems and Internet services), which are often characterized by multivariate time series (MTS). In practice, it's important to precisely ...