Detecting a DDoS Attack
Is Only Half the Job.
Detection without automatic blocking is just an alert someone reads after the outage already happened. Neurowall pairs an 18-rule heuristic detection engine with enforcement right at the network card, so identified attack traffic is blocked in the same pipeline that spotted it.
Every minute an attack runs
is measured in outage cost.
DDoS attacks don't just cause downtime — they cascade. Exhausted connection tables starve legitimate users. Overloaded backends slow down unrelated services on the same infrastructure. A detection system that only alerts a human still leaves the attack running for as long as it takes someone to notice, investigate, and act. Automatic blocking closes that gap to milliseconds.
For SaaS, e-commerce, and API providers, uptime during an attack window directly translates to revenue and customer trust — not just an engineering metric.
Volumetric floods can saturate a link or exhaust connection state in seconds. A human-in-the-loop response is too slow by definition.
An unmitigated attack against one service can degrade shared infrastructure — routers, load balancers, upstream links — affecting services that weren't even the target.
Every minute traffic isn't being filtered close to the edge is a minute closer to needing (and paying for) emergency cloud-based DDoS scrubbing.
Pattern recognition,
not just rate counting.
18 built-in detection rules spanning 5 threat categories run over aggregated security events — including a dedicated DDoS category covering high-volume traffic, SYN floods, UDP floods, ICMP floods, and sudden traffic spikes.
The engine uses lightweight counting techniques to estimate how many distinct sources or destinations are involved in traffic — spotting scan-like or flood-like patterns without the cost of tracking every single one individually.
Detection compares live traffic against a learned baseline rather than a single static threshold, so what counts as "abnormal" adjusts to a network's actual traffic profile.
Once a flood pattern is confirmed, mitigation is pushed to the earliest possible point in the network stack for per-packet flood-type blocking (SYN/ACK/ICMP/FIN/RST) and to the firewall's connection-limiting layer — both at line rate.
Detection means nothing
without a fast path to drop.
| Mechanism | Where it runs |
|---|---|
| Flood-type packet drops | At the network card level, before the operating system spends any memory on the packet, per SYN/ACK/ICMP/FIN/RST type |
| Per-source rate limiting | Token bucket, evaluated per source IP before requests reach application logic |
| Per-IP connection limiting | Firewall rule layer — caps concurrent connections from a single source |
| Blocklist enforcement | Threat-intel-fed IP/CIDR blocks applied at the fast lookup table used for allow/deny decisions |
| Policy trace verification | Confirms a mitigation rule actually took effect across both enforcement tiers before trusting it |
Detection without fast blocking
is just a very expensive alert.
Most DDoS defenses fall into one of three camps — each of which either detects too slowly, blocks too indiscriminately, or requires a human to close the loop.
| Common approach | The gap |
|---|---|
| SIEM/log-based detection, manual response | Logs are ingested, parsed, and alerted on minutes after the fact — by the time a human reads the alert and manually blocks the source, the attack has already run its course |
| Static rate limits with no adaptive baseline | A single fixed threshold either false-positives on legitimate traffic spikes (sales events, patch-day logins) or fails to catch attacks that stay just under the static line |
| Cloud scrubbing with automatic failover | Effective against very large volumetric attacks, but re-routing traffic takes time to trigger and adds latency to all traffic once active — not a fit for latency-sensitive workloads |
| Userspace anomaly detection | Even accurate detection logic is undermined if the actual block has to cross the kernel/userspace boundary — the flood can still exhaust resources faster than the block can be applied |
The cost compounds
every minute the attack runs.
Detection and blocking
are the same pipeline.
Once the heuristics engine confirms a flood pattern, mitigation is pushed straight to the operating system's networking layer — no alert queue, no ticket, no waiting for someone to be paged and respond.
Detection compares live traffic to a learned baseline rather than one fixed number, catching both obvious floods and attacks that would slip under a static threshold.
Because enforcement runs in the same fast packet filter and connection-limiting layers that generate the telemetry the heuristics engine reads, there's no cross-boundary hop between "detected" and "blocked."
Policy trace simulation can confirm a mitigation rule actually took effect across both enforcement tiers — so you're not trusting that a block worked, you're verifying it.