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.
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.
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.
| 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 |
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 |
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.