New open access journal publication for Network Traffic Anomaly Detection

The PHOENIX team is happy to announce that our latest work in network intrusion detection has been published online in Information, MDPI. In this work, we propose novel Deep Learning formulations for detecting threats and alerts on network logs acquired by pfSense, an open-source software that acts as firewall on FreeBSD operating system. We employ Convolutional Neural Networks (CNNs) and Long Short Term Memory Networks (LSTMs) in order to effectively classify new network log instances into appropriate categories and thus recognize network-level anomalies. The paper is available online via its DOI: https://doi.org/10.3390/info12050215


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