Anomaly Detection based on GS-OCSVM Classification

Kittidachanan, Kittikun; Minsan, Watha; Pornnopparath, Donlapark; Taninpong, Phimphaka

doi:10.1109/kst48564.2020.9059326

Cited by 5 publications

(1 citation statement)

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“…For example, Isolation Forest (IFF) uses a random hyperplane to cut the data space, where anomalous samples or outliers with sparse distribution density are more easily sliced into a subspace [14,15]. The One-Class Support Vector Machine (OCSVM) does not rely on density partitioning to find anomalies, by improving the support vector machine and using classification techniques for anomaly detection on time series data with extreme class imbalance [16][17][18]. Essentially, it converts binary classification into a single classification and marks the data as anomalous samples whenever they do not be-long to the normal class.…”

Section: Time Series Anomaly Detection Methodsmentioning

confidence: 99%

AC-LSTM: Anomaly State Perception of Infrared Point Targets Based on CNN+LSTM

et al. 2022

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Anomaly perception of infrared point targets has high application value in many fields, such as maritime surveillance, airspace surveillance, and early warning systems. This kind of abnormality includes the explosion of the target, the separation between stages, the disintegration caused by the abnormal strike, etc. By extracting the radiation characteristics of continuous frame targets, it is possible to analyze and warn the target state in time. Most anomaly detection methods adopt traditional outlier detection, which has the problems of poor accuracy and a high false alarm rate. Driven by data, this paper proposes a new network structure, called AC-LSTM, which combines Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM), and embeds the Periodic Time Series Data Attention module (PTSA). The network can better extract the spatial and temporal characteristics of one-dimensional time series data, and the PTSA module can consider the periodic characteristics of the target in the process of continuous movement, and focus on abnormal data. In addition, this paper also proposes a new time series data enhancement method, which slices and re-amplifies the long time series data. This method significantly improves the accuracy of anomaly detection. Through a large number of experiments, AC-LSTM has achieved higher scores on our collected datasets than other methods.

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Section: Time Series Anomaly Detection Methodsmentioning

confidence: 99%