The five most common DDoS attack vectors for Q2 2024, according to Cloudflare research, were DNS, SYN, RST, User Datagram Protocol and Generic Routing Encapsulation.

Detecting the aforementioned signs quickly and accurately is key to mitigating DDoS attacks. It is vital to build automated DDoS detection methods into cloud and on-premises infrastructure so preventative measures can be taken immediately and before excessive damage is done.

Two methods to detect DDoS attacks are inline packet inspection and out-of-band detection via traffic flow analysis. Both can be deployed on-premises or via the cloud.

Inline packet examination tools sit in front of an IT infrastructure and monitor all traffic. Devices such as load balancers, firewalls and intrusion prevention systems can provide inline detection and mitigation. These tools, however, are easily overwhelmed by today's hypervolumetric attacks. It is better to deploy dedicated inline packet examination DDoS mitigation appliances that use machine learning to spot abnormal traffic and activity. As soon as a DDoS attack is detected, dedicated DDoS mitigation tools adjust volumetric and protocol protection configurations to filter out malicious traffic. Note, however, this runs the danger of false positives and blocking genuine requests. Inspecting every data packet also causes increased latency.

Out-of-band tools overcome the difficulties of deep packet inspection at scale and unwanted false alarms. These tools passively analyze flow data from NetFlow, J-Flow, sFlow and IP Flow Information Export-enabled routers and switches to detect attacks. Although they can't automatically adjust protection configurations, they can send alerts or automatically trigger steps to mitigate the attack via routing traffic to a centralized data cleansing station that filters legitimate traffic.