Botnet Detection in Network Traffic Based on GBM
Over the past decade botnets have gained the attention of many security teams in the companies and researchers across the globe. Security teams are working tirelessly to develop systems that would detect the botnet with high accuracy in the network traffic. Botnet attacks are unique threats to systems and has high vulnerability these high risks problems naturally attracted researchers and professionals and started applying machine learning (ML) techniques to detect botnet attacks. We would like to evaluate different features and the result impact on detection accuracy for a given machine learning method used. We understand that the network traffic is being analyzed through various classification machine learning models and has given good results but we have not come across any research paper or could be less work done on Gradient Boosting Machine (GBM). We see a scope to work on GBM detecting botnet and hence propose GBM algorithm to classify the botnet traffic. In this paper, we focused only on the preprocessed botnet classified data.
Proceedings of the Seventh International Conference on Business Analytics and Intelligence, December 2019, IISc, India.