Abstract: Embodiments described herein are directed to securing network-based compute resources. The foregoing may be achieved by determining a tag representative of non-malicious network addresses. The tag is determined by analyzing network data traffic received by a plurality of compute resources. Machine-learning based techniques may be used to automatically classify each network address that communicates with a particular compute resource as being malicious or non-malicious. Determined non-malicious network addresses for a particular compute resource are automatically associated with a tag. The tag is used to configure a firewall application to prevent access to a corresponding compute resource by malicious network addresses not represented by the tag. The number of non-malicious network addresses associated with a tag may be expanded by clustering compute resources having a similar set of network addresses that communicate therewith.

Abstract: Embodiments described herein are directed to securing network-based compute resources. The foregoing may be achieved by determining a tag representative of non-malicious network addresses. The tag is determined by analyzing network data traffic received by a plurality of compute resources. Machine-learning based techniques may be used to automatically classify each network address that communicates with a particular compute resource as being malicious or non-malicious. Determined non-malicious network addresses for a particular compute resource are automatically associated with a tag. The tag is used to configure a firewall application to prevent access to a corresponding compute resource by malicious network addresses not represented by the tag. The number of non-malicious network addresses associated with a tag may be expanded by clustering compute resources having a similar set of network addresses that communicate therewith.