Training classifiers for network intrusion detection is hindered by two types of problems: data challenges (lack of labels, class imbalance, non-IID data, and concept drift) and engineering challenges (memory & compute efficiency, data ingestion, parallel training, and hyperparameter optimization). Existing ad-hoc scripts make it hard to reproduce results or compare models systematically across these conditions. An extendable machine learning pipeline is developed to address both, targeting malicious network flow classifiers for the Stratosphere Linux IPS (Slips). The output is a set of best-performing models at different FPR and F1 thresholds suitable for deployment in Slips.
First Workshop on Attackers and Cyber-Crime Operations (WACCO) 2019
On June 20th will take place in Stockholm the First Workshop on Attackers and Cyber-Crime Operations (WACCO) as part of the IEEE European Symposium on Security and Privacy (EuroSP). WACCO is a great initiative that provides the opportunity for research, discussions, and sharing on cyber-criminal activities.

