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Rumors spread on Online Social Networks sometimes can lead to serious social issues. To accurately identify them from normal posts is proved to be of great value. Users' behaviors are different when they post rumors and normal posts. Since rumors only account for a small percentage of all posts, they can be regarded as anomalies. Therefore, we propose an anomaly detection method based on autoencoder...
Anomaly detection is critical given the raft of cyber attacks these days. It is thus essential to identify the network anomalies more accurately. In this paper, we propose a novel network anomaly detection system which combines random projections (sketches) and feature-based MSPCA to detect anomalous source IP addresses. By combining PCA and wavelet analysis, MSPCA can separate anomalous data efficiently...
In this paper, we propose a network anomaly detection system which consists of a Maximal Information Coefficient based feature selection algorithm and a feature-based MSPCA detection algorithm, which can separate the anomalous information more efficiently. Maximal Information Coefficient can provide a good information measurement of any dependency between two random variables. MSPCA combines the benefit...
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