DDoS and Anomaly Diagnostics
Machine Learning and AI diagnostics for DDoS and network anomalies
Ultimate Network and Cloud Visibility. CySight turns granular flow visibility into predictive AI baselining and diagnostic analytics that detect abnormal traffic fast, qualify seriousness, and preserve evidence for response, compliance, and audit.
CySight = Foresight from Hindsight + Insight from Deepsight
Deep tech that makes full use of granular flow visibility and scales up or down to your architecture.
Predictive AI baselining
Continuously learns per asset and service
Evidence-grade retention
Keep the flow detail others discard
Fast diagnostics
Detect, qualify, report, alert
Predictive AI baselining and diagnostics built on full-fidelity flow visibility.
Turn anomalous traffic into defensible decisions
DDoS and anomaly diagnostics fail when tools drop telemetry or cannot explain change. CySight preserves granular visibility, learns normal behavior, and produces evidence-grounded diagnostics that network and security teams can validate and act on.
Faster time to diagnosis
Move from spike to root cause by scoring deviation against baselines and isolating the contributing entities.
Lower noise, higher confidence
Reduce false positives by comparing across time windows and qualifying seriousness based on learned patterns.
Audit and response readiness
Preserve evidence-grade flow context for reporting, investigations, and post-incident defensibility.
Scales with your environment
Scale up or down to your network and cloud architecture without probe fleets and without losing investigative scope.
What Machine Learning and AI diagnostics means in CySight
CySight combines predictive AI baselining with high-speed flow analytics to detect abnormal behaviors, explain what changed, and generate reporting and alerting outputs with forensic context.
Predictive AI baselining
Continuously learns per asset and service footprint across time windows, so deviation is measurable and reproducible.
Anomaly qualification
Categorize abnormal patterns and qualify seriousness using context such as endpoints, ports, protocols, direction, and segmentation boundaries.
DDoS style volume and burst patterns
Detect floods and record explosions using count-style analytics, rate shifts, and comparative timelines.
Granular traffic accounting
Attribute change to devices, interfaces, IPs, services, applications, and organizational ownership with evidence intact.
Reporting and alerting
Generate alerts and scheduled outputs in formats used by operations, SOC, and compliance workflows.
Optional vector store and SLM
Add semantic linking across time and entities, and generate clear, evidence-grounded explanations without changing the underlying detection.
Broad flow device support
Designed for professional environments using the broadest flow-capable devices in the market and enriched sources where available.
Engineering built for scale
Unsurpassed collection, correlation, retention, and diagnostics to extract deep intelligence from huge flow data without losing fidelity.
Open workflow - from anomaly to diagnosis
CySight keeps scope as you pivot from detection to baselines to forensic context and reporting, so results remain evidence-grade and repeatable.
Detect deviation Baseline first
Score behavior against continuously learned baselines per asset and service footprint to surface what is unusual with evidence-grade context.
Explain what changed Compare windows
Compare minute vs recent minutes, hour vs recent hours, weekday vs historical weekdays, and month vs historical months to pinpoint when the shift started and which entity drove it.
Qualify seriousness Classify patterns
Distinguish volume bursts, floods, misuse, and low-and-slow anomalies by correlating direction, ports, protocols, endpoints, and segmentation context.
Attribute responsibility Ownership and scope
Tie traffic and anomalies to accounts, cost centers, business units, and boundaries so response and reporting are accountable and defensible.
Preserve evidence Forensics
Keep the retained flow context needed to reproduce what happened and support investigation, audit, and insurance-grade reporting.
Operationalize Alerts and exports
Schedule reports, export in standard formats, and forward high-fidelity alerts to SIEM, SOAR, XDR, or policy tooling when required.
Deep dive - DDoS and anomaly diagnostics feature set
Flow-powered monitoring Network and cloud visibility
- Use granular flow visibility as the primary evidence source for diagnostics across network, cloud, host, and application layers.
- Scale up or down to match your architecture while keeping consistent investigative scope.
Predictive AI baselining Learn normal, detect abnormal
- Continuously learn behavior per asset and service footprint across time windows.
- Score deviation with contextual pivots so anomalies are explainable, not just visible.
DDoS and burst diagnostics Volume and record explosion
- Identify floods and abnormal bursts using count-style analytics, rate shifts, and comparative timelines.
- Preserve the context needed to confirm whether impact is external, internal, or lateral.
Contextual traffic accounting Who, what, where, when
- Attribute anomalous traffic to devices, interfaces, IPs, protocols, ports, applications, and ownership.
- Qualify seriousness by categorizing and correlating indicators across retained telemetry.
Reporting and alerting Fast outputs teams can use
- Generate reporting and alerts grounded in retained evidence rather than partial summaries.
- Export in formats used by operations, SOC, and compliance workflows.
Optional vector store and SLM Speed and clarity
- Link related signals across time, entities, and segments to accelerate investigations.
- Generate clear, evidence-grounded explanations without changing the baseline detection logic.
DDoS and Anomaly Diagnostics - evaluation pack
Use this overview for evaluation, procurement, and internal alignment on how CySight applies Machine Learning and predictive AI baselining to anomaly diagnostics.