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  1. Anomaly detection is a critical task in applications like preventing financial fraud, system malfunctions, and cybersecurity attacks. While previous research has offered a plethora of anomaly detection algorithms, effective anomaly detection remains challenging for users due to the tedious manual tuning process. Currently, model developers must determine which of these numerous algorithms is best suited for their particular domain and then must tune many parameters by hand to make the chosen algorithm perform well. This demonstration showcases AutoOD, the first unsupervised selftuning anomaly detection system which frees users from this tedious manual tuning process. AutoOD outperforms the best unsupervised anomaly detection methods it deploys, with its performance similar to those of supervised anomaly classification models, yet without requiring ground truth labels. Our easy-to-use visual interface allows users to gain insights into AutoOD’s self-tuning process and explore the underlying patterns within their datasets.
  2. Local outlier techniques are known to be effective for detecting outliers in skewed data, where subsets of the data exhibit diverse distribution properties. However, existing methods are not well equipped to support modern high-velocity data streams due to the high complexity of the detection algorithms and their volatility to data updates. To tackle these shortcomings, we propose local outlier semantics that operate at an abstraction level by leveraging kernel density estimation (KDE) to effectively detect local outliers from streaming data. A strategy to continuously detect top-N KDE-based local outliers over streams is designed, called KELOS – the first linear time complexity streaming local outlier detection approach. The first innovation of KELOS is the abstract kernel center-based KDE (aKDE) strategy. aKDE accurately yet efficiently estimates the data density at each point – essential for local outlier detection. This is based on the observation that a cluster of points close to each other tend to have a similar influence on a target point’s density estimation when used as kernel centers. These points thus can be represented by one abstract kernel center. Next, the KELOS’s inlier pruning strategy early prunes points that have no chance to become top-N outliers. This empowers KELOS tomore »skip the computation of their data density and of the outlier status for every data point. Together aKDE and the inlier pruning strategy eliminate the performance bottleneck of streaming local outlier detection. The experimental evaluation demonstrates that KELOS is up to 6 orders of magnitude faster than existing solutions, while being highly effective in detecting local outliers from streaming data.« less