Most current network intrusion detection systems employ signature-based methods or data mining-based methods which rely on labelled training data. This training data is typically ...
Abstract: Fusion of information from graph features and content can provide superior inference for an anomaly detection task, compared to the corresponding content-only or graph fe...
John Grothendieck, Carey E. Priebe, Allen L. Gorin
—Protecting and securing sensitive information are critical challenges for businesses. Deliberate and intended actions such as malicious exploitation, theft or destruction of dat...
We present a new approach for activity modelling and anomaly detection based on non-parametric Gaussian Process (GP) models. Specifically, GP regression models are formulated to l...
Abstract-- We investigate statistical anomaly detection algorithms for detecting SYN flooding, which is the most common type of Denial of Service (DoS) attack. The two algorithms c...