Renato Machado

TL;DR: This paper presents a novel intrusion detection model based on artificial immune and mobile agent paradigms for network intrusion detection, which is host-based and adopts the anomaly detection paradigm.

TL;DR: The introduced method aims at minimizing the false alarms caused by the elongated structures and is based on the well-known adaptive processing mechanism, i.e., the so-called adaptive noise canceler (ANC) where a separate reference signal is required.

TL;DR: In this letter, it is found that, for “good” image pairs, the subtracted image has Gaussian distributed clutter backscattering and that the noise mainly consists of the thermal noise and, therefore, speckle noise does not have to be considered.

TL;DR: In this paper , the authors presented a two-step approach for intrusion detection and identification in IoT environments, where the first step performs a traffic analysis with an extra tree binary classifier and events detected as intrusive are analyzed in the second stage by an ensemble approach consisting of Extra Tree, Random Forest, and Deep Neural Network.

TL;DR: The experimental results show that RPCA via principal component pursuit (PCP) can provide a good trade-off between PD and FAR, and a comparison between the results obtained and a classical CD algorithm based on the likelihood ratio test provides the pros and cons of the proposed method.