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
A pioneer in scholarly open access publishing, MDPI has supported academic communities since 1996. Based in Basel, Switzerland, MDPI has the mission to foster open scientific exchange in all forms, across all disciplines. Our 338 diverse, peer-reviewed, open access journals are supported by more than 84,200 academic editors. We serve scholars from around the world to ensure the latest research is freely available and all content is distributed under a Creative Commons Attribution License (CC BY).