1,333 research outputs found
One-Class Adversarial Nets for Fraud Detection
Many online applications, such as online social networks or knowledge bases,
are often attacked by malicious users who commit different types of actions
such as vandalism on Wikipedia or fraudulent reviews on eBay. Currently, most
of the fraud detection approaches require a training dataset that contains
records of both benign and malicious users. However, in practice, there are
often no or very few records of malicious users. In this paper, we develop
one-class adversarial nets (OCAN) for fraud detection using training data with
only benign users. OCAN first uses LSTM-Autoencoder to learn the
representations of benign users from their sequences of online activities. It
then detects malicious users by training a discriminator with a complementary
GAN model that is different from the regular GAN model. Experimental results
show that our OCAN outperforms the state-of-the-art one-class classification
models and achieves comparable performance with the latest multi-source LSTM
model that requires both benign and malicious users in the training phase.Comment: Update Fig 2, add Fig 7, and add reference
GAN Augmented Text Anomaly Detection with Sequences of Deep Statistics
Anomaly detection is the process of finding data points that deviate from a
baseline. In a real-life setting, anomalies are usually unknown or extremely
rare. Moreover, the detection must be accomplished in a timely manner or the
risk of corrupting the system might grow exponentially. In this work, we
propose a two level framework for detecting anomalies in sequences of discrete
elements. First, we assess whether we can obtain enough information from the
statistics collected from the discriminator's layers to discriminate between
out of distribution and in distribution samples. We then build an unsupervised
anomaly detection module based on these statistics. As to augment the data and
keep track of classes of known data, we lean toward a semi-supervised
adversarial learning applied to discrete elements.Comment: 5 pages, 53rd Annual Conference on Information Sciences and Systems,
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