1 citations · 1 across the 4 of their papers we have counts for
4 papers
Is Score Suboptimal for Cybersecurity Models? Introducing , a Cost-Aware Alternative for Model Assessment
Manish Marwah, Asad Narayanan, Stephan Jou +2
The cost of errors related to machine learning classifiers, namely, false positives and false negatives, are not equal and are application dependent. For example, in cybersecurity…
STAN: Synthetic Network Traffic Generation with Generative Neural Models
Shengzhe Xu, Manish Marwah, Martin Arlitt +1
Deep learning models have achieved great success in recent years but progress in some domains like cybersecurity is stymied due to a paucity of realistic datasets. Organizations ar…
Attention-Based Self-Supervised Feature Learning for Security Data
I-Ta Lee, Manish Marwah, Martin Arlitt
While applications of machine learning in cyber-security have grown rapidly, most models use manually constructed features. This manual approach is error-prone and requires domain…
ACE -- An Anomaly Contribution Explainer for Cyber-Security Applications
Xiao Zhang, Manish Marwah, I-ta Lee +2
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…