Threat Detection: How Real-Time Monitoring Stops Cyberattacks
How does threat detection work? Behavioral analysis, anomaly detection and machine learning — the methods that spot cyberattacks and bot abuse in real time.
Detect Automated Threats
Stop bots before they cause damage
What is Threat Detection?
Threat detection is noticing that an attack is in progress. It is the counterpart to prevention: some attacks get past the controls, and detection is how long it takes you to find out.
The measure that matters is time — how long between an attack starting and somebody acting on it. Published figures for that interval are routinely measured in weeks, and the gap is rarely caused by a missing tool. It is caused by nobody reading what the tools produced.
The two detection strategies
| Strategy | How it works | Where it fails |
|---|---|---|
| Signature-based | Matches activity against known-bad patterns: file hashes, request payloads, IP reputation | Anything novel, and anything that re-encodes itself |
| Anomaly-based | Compares against a learned baseline of normal and flags deviation | Needs a clean baseline, and generates false positives |
Both are necessary, and the second has a trap specific to bot traffic that is worth spelling out: if part of your historical traffic was already automated, it is inside the baseline, and the system has learned that automation is normal. On a site with a long-standing scraping problem that is the default state.
Detecting automated abuse specifically
Attacks against your public surface do not look like intrusions. Nothing is exploited and no malware runs — a credential stuffing run is a large number of ordinary login requests. So the indicators are different from the usual compromise indicators:
- The aggregate failed-login ratio on an endpoint, which no amount of proxy rotation conceals
- Sessions that complete forms faster than the content could be read
- Responses arriving sooner than human reaction time after a page paints
- Clients whose claimed browser contradicts their observable capabilities
- Add-to-basket or reservation volume with no matching purchases
Each of these is a property of the aggregate or of the session, not of a single request. That is the central difference between detecting intrusion and detecting abuse, and it is why request-level inspection tools miss it entirely.
Making detection operational
- Alert on rates, not events. One failed login is noise; a change in the endpoint's failure ratio is a signal.
- Keep the alert volume inside what you can triage. An unread queue is not detection, and this is the failure mode in practice far more often than a blind spot in coverage.
- Log the business event. "Checkout attempted and declined" supports a judgement; HTTP 200 does not.
- Write the response down in advance. Detection without a decided response produces a meeting, not a containment.
Prosopo's contribution is at the point of detection for one class of threat: identifying whether a session is automated as it arrives, so the abuse is stopped rather than discovered later in a log review.