  With a growing increase in botnet attacks, computer networks are constantly
under threat from attacks that cripple cyber-infrastructure. Detecting these
attacks in real-time proves to be a difficult and resource intensive task. One
of the pertinent methods to detect such attacks is signature based detection
using machine learning models. This paper explores the efficacy of these models
at detecting botnet attacks, using data captured from large-scale network
attacks. Our study provides a comprehensive overview of performance
characteristics two machine learning models --- Random Forest and Multi-Layer
Perceptron (Deep Learning) in such attack scenarios. Using Big Data analytics,
the study explores the advantages, limitations, model/feature parameters, and
overall performance of using machine learning in botnet attacks /
communication. With insights gained from the analysis, this work recommends
algorithms/models for specific attacks of botnets instead of a generalized
model.
