OpenDNS has enhanced its cloud-based network security service Umbrella with new capabilities designed to protect organizations against targeted attacks, the company announced on Tuesday.
The company says its monitoring systems are capable of detecting malicious traffic from the first stages of a potential targeted attack by comparing customers’ traffic to activity on OpenDNS’s global network. By providing predictive intelligence on the attackers’ network infrastructure, OpenDNS enables organizations to block attacks before any damage is caused.
Many organizations are capable of identifying single-stage, high-volume cyberattacks, but the “noise” generated by these types of attacks makes it more difficult to detect highly targeted operations, the company explained.
According to OpenDNS, its services address this issue by providing real-time reports on global activity and detailed information for each significant event. The reports can be used by enterprises to identify ongoing or emerging targeted attacks based on whether or not the threats have a large global traffic footprint, or if they’re detected for the first time.
In order to make it easier for security teams to investigate an incident, OpenDNS provides information on the users, devices and networks from which malicious requests are sent. Information on the attackers’ infrastructure can be useful for predicting future threats and for blocking components that are being prepared for new attacks.
“Enterprises today are challenged to keep up with the volume of attacks that are targeting their networks. Not only is the efficacy of today’s security tools declining, but when they do identify a threat they lack the context that is critical to blocking it,” said Dan Hubbard, CTO of OpenDNS. “The ability to determine the relevance and prevalence of an attack is key to prioritizing response, remediating infected hosts, and understanding the scope of the threat.”
The new capabilities are available as part of the Umbrella service based on a per user, per year subscription.