Solving the Impacts of Carpet-Bombing Attacks With Arbor Adaptive DDoS Protection
To quickly and reliably detect and mitigate carpet bombing attacks, NETSCOUT has created Arbor Adaptive DDoS Protection which uses machine learning to monitor, track, and mitigate attacks.
Detecting carpet bombing attacks can be difficult as the attack traffic is spread across a range of IP addresses. This makes it harder to identify patterns, while each individual host might be still below the threshold to trigger an alert. Additionally, attack methods used can adapt and change during the attack to evade both detection and mitigation.
Mitigation presents its own problems. With carpet-bomb attacks, it’s often impractical to try to perform mitigation on an entire network block, both due to the sheer size as well as the potential of over-mitigation and causing impacts on working services. A better approach is to determine the heaviest impacted hosts and networks, and mitigate on these smaller, more precise segments of the network. This alleviates much of the attack without exhausting mitigation capacity or over-mitigation causing problems for legitimate services.
To quickly and reliably detect and mitigate carpet bombing attacks, NETSCOUT® has created Arbor Adaptive DDoS Protection (ADP). ADP uses machine learning to monitor, track, and mitigate attacks throughout their lifecycle, including adapting mitigations to follow attacks as they change.