  Security is a major concern for organizations who wish to leverage cloud
computing. In order to reduce security vulnerabilities, public cloud providers
offer firewall functionalities. When properly configured, a firewall protects
cloud networks from cyber-attacks. However, proper firewall configuration
requires intimate knowledge of the protected system, high expertise and
on-going maintenance.
  As a result, many organizations do not use firewalls effectively, leaving
their cloud resources vulnerable. In this paper, we present a novel supervised
learning method, and prototype, which compute recommendations for firewall
rules. Recommendations are based on sampled network traffic meta-data (NetFlow)
collected from a public cloud provider. Labels are extracted from firewall
configurations deemed to be authored by experts. NetFlow is collected from
network routers, avoiding expensive collection from cloud VMs, as well as
relieving privacy concerns.
  The proposed method captures network routines and dependencies between
resources and firewall configuration. The method predicts IPs to be allowed by
the firewall. A grouping algorithm is subsequently used to generate a
manageable number of IP ranges. Each range is a parameter for a firewall rule.
  We present results of experiments on real data, showing ROC AUC of 0.92,
compared to 0.58 for an unsupervised baseline. The results prove the hypothesis
that firewall rules can be automatically generated based on router data, and
that an automated method can be effective in blocking a high percentage of
malicious traffic.

Cryptography and Security;Machine Learning