1 code implementation • 27 Sep 2020 • Shengzhe Xu, Manish Marwah, Martin Arlitt, Naren Ramakrishnan
We evaluate the performance of STAN in terms of the quality of data generated, by training it on both a simulated dataset and a real network traffic data set.
no code implementations • 24 Mar 2020 • I-Ta Lee, Manish Marwah, Martin Arlitt
While applications of machine learning in cyber-security have grown rapidly, most models use manually constructed features.
no code implementations • 1 Dec 2019 • Xiao Zhang, Manish Marwah, I-Ta Lee, Martin Arlitt, Dan Goldwasser
In this paper, we introduce Anomaly Contribution Explainer or ACE, a tool to explain security anomaly detection models in terms of the model features through a regression framework, and its variant, ACE-KL, which highlights the important anomaly contributors.
no code implementations • 20 Oct 2016 • Alexander Ulanov, Andrey Simanovsky, Manish Marwah
It is implemented in a distributed fashion in order to address these scalability issues.