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Monitor CPU as a behavioral baseline, not as a verdict. Record normal utilization for every host and service, alert when usage rises unexpectedly or stays outside its normal pattern, then identify the process and corroborate it with memory, code, DNS, and network telemetry. High CPU by itself can come from an approved batch job, build, database operation, or high-performance-computing (HPC) workload, so do not label a server compromised until the workload and surrounding evidence have been checked.
What CPU monitoring can—and cannot—tell you
Unauthorized miners need sustained compute. A server that suddenly uses far more CPU than its established pattern, especially outside maintenance or batch windows, deserves investigation. CPU trends are valuable because they are available on nearly every server and can reveal abuse before an operator notices a large bill or an application failure.
That signal is not unique to cryptojacking. Legitimate HPC, media processing, compilation, analytics, backups, encryption, and traffic spikes can produce the same graph. The useful question is not “Is CPU high?” but “Which process caused the change, was that process authorized, and do independent signals support malicious mining?”
- CPU telemetry: utilization by host, core, service, container, or virtual machine over time.
- Process telemetry: process ID, owner, executable path, arguments, start time, parent process, and CPU share.
- Memory and code telemetry: memory-page hashes, hardware performance counters, executed machine code, and detections matching known miner behavior.
- Network telemetry: DNS lookups, outbound connections, destination domains or IP addresses, and container traffic using the Stratum protocol.
- Context: deployments, scheduled jobs, administrative changes, vulnerability findings, service-account activity, and cloud-cost changes.
Google Cloud’s VM Threat Detection documentation summarizes this multi-signal approach: “VM Threat Detection analyzes information about software running on VMs, including a list of application names, per-process CPU usage, hashes of memory pages, CPU hardware performance counters, and information about executed machine code to determine whether any application matches known cryptocurrency mining signatures.”
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Build a baseline for each server and workload
A single fleet-wide CPU limit is unreliable. A web front end, an idle directory server, a database, and an HPC node have different normal ranges. Baselines should therefore be attached to a host and its workload, and should account for time-of-day and day-of-week patterns.
1. Inventory the workload
For every monitored host, record its role, operating system, instance type, assigned services, container or virtual-machine placement, approved compute-heavy jobs, and maintenance windows. Note recurring deployment, backup, indexing, data-transfer, and batch schedules. This context prevents an alert from treating a documented nightly job as an intrusion.
2. Collect enough history to represent normal cycles
Capture CPU utilization at a consistent interval and retain a long enough history to include ordinary traffic peaks, quiet periods, weekends, and scheduled work. Store per-host and, where practical, per-process data. A host average can hide one process consuming a core while other cores remain idle.
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3. Define expected ranges and exceptions
Describe normal behavior in operational terms: “indexing runs from 01:00 to 02:00,” “the build worker is busy only during releases,” or “this service normally stays below its daytime band.” An approved exception should identify an owner and an expiry date. Temporary exceptions that never expire become blind spots.
4. Alert on deviation, persistence, and context
Use a local threshold and duration that fit the workload. The official material does not prescribe a universal percentage or number of minutes that applies to every server. Combine a deviation from baseline with persistence and, when available, a process or network indicator. A short spike during a deployment may be harmless; an unfamiliar process that remains busy after the deployment is more concerning.
| Baseline element | What to record | Why it matters |
|---|---|---|
| Host CPU | Utilization trend, core count, load pattern, and time window | Shows whether the whole host departed from its usual behavior |
| Process CPU | PID, user, executable, arguments, start time, and CPU share | Connects the graph to an owner and a file that can be investigated |
| Scheduled activity | Deployments, backups, indexing, builds, and batch jobs | Separates approved work from unexplained activity |
| Network context | DNS queries, destinations, and connection timing | Tests whether compute activity coincides with mining-related communication |
| Cloud context | Instance changes, service-account actions, and cost anomalies | Can reveal unauthorized capacity or credential use |
Choose a monitoring design that matches your environment
CPU collection and cryptojacking detection are different capabilities. A lightweight host monitor can show a trend, while a provider-managed detector may inspect memory and machine code without installing software in the guest. Select based on signal coverage, supported systems, deployment effort, and response integration.
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| Approach | Signals | Deployment model | Best fit | Important limits |
|---|---|---|---|---|
| Host operating-system telemetry | CPU, load, process list, service state, and local logs | Guest agent or scheduled collection | Any server where administrators control the operating system | CPU alone cannot distinguish a miner from legitimate compute; agent overhead and coverage depend on implementation |
| Provider-managed VM detection | Per-process CPU plus memory-page hashes, hardware counters, executed code, and known signatures | Provider-managed, outside-guest scanning where supported | Eligible virtual machines in the provider’s platform | Platform-, VM-, and operating-system-specific; it is not a universal server guarantee |
| DNS and network telemetry | Lookups, outbound destinations, and protocol indicators | Resolver logging, flow logs, sensors, or network controls | Corroborating a suspicious process and finding other affected hosts | Encrypted traffic, shared infrastructure, and spoofed names can complicate attribution |
| SIEM or SOAR integration | Correlated host, cloud, identity, DNS, and endpoint findings | Centralized event routing and automated workflows | Organizations with an established security operations process | Requires usable parsers, responder permissions, ownership, and tuning |
Do not assume that a provider’s agentless design applies to another cloud, bare-metal server, appliance, or container platform. Verify supported operating systems and compute types before relying on a managed detector.
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Implement practical CPU and process checks
The following examples illustrate the evidence to collect on a Linux host. Adapt the same fields to your operating system’s performance-counter and process tools; do not install an unapproved collector on a production server merely to reproduce an example.
Take an immediate process snapshot
ps -eo pid,ppid,user,%cpu,%mem,lstart,args --sort=-%cpu | head -n 20This snapshot puts the highest-CPU processes first and records the parent, account, start time, and command line. Save the output with a timestamp when an alert fires. A process name alone is weak evidence: an attacker can rename a binary, and a legitimate program can contain an alarming word.
Observe whether the behavior persists
top -b -n 1Take repeated samples through your normal monitoring system rather than relying on a single interactive view. Compare the process that remains busy with deployment records and the service inventory. If the host is a multi-core system, confirm whether the displayed percentage is normalized by the tool; interpret it consistently in your baseline.
Inspect ownership and executable provenance
For a suspicious PID, collect its user, parent process, executable path, arguments, open files, package or image identity, and file timestamps. Check whether the path belongs to an approved package or deployment artifact and whether the binary’s hash matches a known-good copy. Record evidence before changing the process so that incident responders can reconstruct what happened.
Search deliberately for miner-related strings
grep -R -E -i 'btc.com|ethminer|xmrig|cpuminer|randomx' /opt /usr/local /tmp 2>/dev/nullLimit searches to sensible paths and be mindful of performance and permissions. Google’s guidance names strings such as btc.com, ethminer, xmrig, cpuminer, and randomx as investigation leads. A match is not proof: it may be documentation, a test fixture, a renamed executable, or an unrelated file. Verify the binary and its surrounding activity.
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Investigate an alert without destroying evidence
- Confirm the alert and time range. Check whether the CPU rise is real across independent samples and note when it began, whether it is continuous, and which cores or processes are affected.
- Identify the process. Record PID, parent PID, account, executable path, arguments, start time, service or container identity, and the process’s CPU and memory use.
- Map it to an owner and change. Ask the service owner whether a deployment, scheduled job, benchmark, or incident explains the activity. Review recent administrative actions, image changes, package installs, and service-account use.
- Validate the executable. Compare its path, package metadata, file hash, permissions, and signer or image digest with a known-good source. Look for persistence through system services, scheduled tasks, startup scripts, container entrypoints, or user profiles.
- Correlate memory and code findings. Review memory-page or YARA-style detections, hardware-counter anomalies, and executed-code findings when your platform supplies them. A CPU alert paired with a miner signature is materially stronger than either signal alone.
- Check DNS and outbound traffic. Review resolver logs and flow records for lookups or connections to known cryptomining domains or IP addresses. Compare timing with the process start and destination ownership; shared hosting and compromised domains require careful attribution.
- Preserve evidence and assess scope. Export relevant logs, process details, DNS records, cloud audit events, and disk or memory evidence according to your incident plan. Search for the same executable, account, destination, or credential activity on neighboring hosts.
- Contain proportionally. Coordinate isolation, credential rotation, firewall changes, or workload shutdown with the incident owner. Do not indiscriminately kill a process before considering business impact and collecting the evidence needed to determine entry and persistence.
Use DNS, network, and container signals to corroborate CPU
Mining software normally needs a pool or coordination endpoint. A process that consumes unexpected CPU and repeatedly resolves or connects to known cryptomining infrastructure warrants priority investigation. Google recommends enabling Cloud DNS logging so lookups can be correlated with VM findings and routed to security operations.
What to examine
- DNS names and resolved addresses queried shortly before or during the CPU anomaly.
- Long-lived outbound connections from a process that normally has no external network role.
- Destination changes after a deployment, privilege escalation, or credential event.
- Repeated connection attempts, unusual ports, or traffic that persists after the owning service is stopped.
Network indicators are supporting evidence, not a verdict. Domains can be hijacked, IP addresses can be shared, and a miner may use an encrypted or private endpoint that does not appear on a simple blocklist.
Container-specific interpretation
Google’s container guidance treats known Stratum protocol communication as a suspicious indicator. Legitimate container workloads typically do not use Stratum, but the finding still requires context: identify the container image, owner, command, service account, and deployment source before containment. A host-level CPU graph can miss which container is responsible, so collect per-container CPU and network identity when available.
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Google Cloud documents VM Threat Detection as an agentless, outside-the-guest scanning capability for supported virtual machines. It periodically scans guest memory and disk clones from the hypervisor without pausing the guest, requiring a guest agent, consuming guest CPU cycles for the scan, or requiring guest network connectivity. Its analysis includes application names, per-process CPU usage, memory-page hashes, CPU hardware performance counters, and executed machine code.
That architecture is specific to the supported Google Cloud implementation. It should not be generalized to other providers, bare-metal servers, or every container service. Confirm the current support matrix and service configuration for the projects and VM types you operate.
Operational setup sequence
- Enable VM Threat Detection for the relevant projects and enable Event Threat Detection where those findings are needed.
- Turn on Cloud DNS logging for the networks whose VM activity you need to correlate.
- Route Security Command Center findings into the SIEM or SOAR system that responders already monitor.
- Verify that responders have the IAM permissions required to view findings, inspect affected resources, and carry out approved containment actions.
- Keep the security contact channel continuously monitored; an alert that no person sees is not an effective control.
- Test the notification path with a documented, non-destructive exercise and confirm ownership, escalation, and evidence-retention procedures.
Understand the Cryptomining Protection Program’s boundaries
Google’s Security Command Center Cryptomining Protection Program describes coverage for undetected, unauthorized cryptomining on eligible Linux-based Compute Engine instances supported by VM Threat Detection. The documented exclusions include Windows VMs, Confidential Compute VMs, Google Kubernetes instances, App Engine, Cloud Run, and Cloud Functions. Eligibility and program terms can change, so verify the current official page before relying on them.
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The program does not outsource incident response. Google states: “Google’s responsibility is limited to detecting and notifying customers of cryptomining attacks. Attack response and remediation remain the customer’s responsibility.” Treat any notification or credit language as subject to the program’s current eligibility and terms, not as guaranteed reimbursement.
Route alerts to people who can act
An alert should contain the host or VM, first-seen and last-seen times, CPU trend, process identity, executable path, account, relevant DNS or network evidence, and links or identifiers for the underlying logs. Send it to the service owner and security operations team when both need to act. Include a severity rationale based on persistence, privilege, external communication, and whether production capacity is being consumed.
Integrate findings with existing SIEM/SOAR workflows instead of creating a separate inbox that nobody checks. Define who can isolate a workload, revoke a service account, block an endpoint, preserve evidence, approve downtime, and communicate with stakeholders. Review the security contact inbox continuously, and periodically test that the escalation reaches an available responder.
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Contain the affected workload
Follow the organization’s incident plan to isolate the VM, node, container, or account while preserving evidence. Restrict egress to the minimum needed for investigation, remove unauthorized access, and protect neighboring systems from lateral movement. If production availability requires a replacement workload, deploy from a known-good image rather than copying unverified files from the compromised host.
Remove persistence and close the entry path
Inspect startup services, scheduled jobs, container definitions, deployment pipelines, exposed administration interfaces, vulnerable packages, weak credentials, and over-permissive service accounts. Rotate credentials and secrets that may have been readable by the malicious process. Patch the exploited component and correct the misconfiguration before reconnecting the workload.
Restore and watch for recurrence
Rebuild from trusted sources when integrity cannot be established. Restore only required data, validate the resulting process and network inventory, and keep enhanced CPU, DNS, identity, and cost monitoring in place during the recovery window. A falling CPU graph after termination does not prove that persistence was removed.
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Use cloud-cost anomalies as supporting evidence
Unauthorized miners can increase instance hours, machine size, disk activity, or network charges. Compare billing and resource-utilization anomalies with CPU, process, identity, and network timelines. A spending rise can prioritize an investigation, but it is not proof of cryptojacking: legitimate scale-outs and approved experiments can cost more as well.
Prevent the next CPU anomaly
- Patch operating systems, VM images, container bases, runtimes, and exposed services on a defined schedule.
- Continuously review vulnerabilities and cloud misconfigurations, especially public administration interfaces and excessive permissions.
- Restrict service-account access to the minimum resources and actions required by each workload.
- Protect secrets in managed secret stores or equivalent controls; do not place long-lived credentials in images, source repositories, or world-readable files.
- Use network egress controls and DNS visibility so unexpected pool communication is visible and containable.
- Keep an inventory of approved compute-heavy jobs and their owners so monitoring can distinguish planned work from abuse.
- Exercise the alert-to-response path, including evidence capture, isolation authority, credential rotation, rebuild, and communications.
Troubleshoot noisy or missed alerts
Too many alerts during legitimate work
Compare the alert with deployment and scheduler records, then create a narrowly scoped, time-limited exception tied to the workload owner. Improve the baseline for that host or job rather than raising a fleet-wide threshold. Keep process, DNS, and identity indicators active so an exception does not suppress unrelated activity.
No alert despite an obvious CPU increase
Check collection permissions, host coverage, sampling interval, exporter health, clock synchronization, and whether the monitor reports host or guest CPU. Confirm that the process-level data is retained long enough for investigation and that alerts are actually routed to a monitored destination.
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A process name looks malicious but the host is legitimate
Verify the executable path, package or image provenance, hash, owner, arguments, network behavior, and change record. Names and strings are leads that can be spoofed or appear in benign tools; rely on corroboration before declaring an incident.
A managed detector reports nothing
Confirm that the VM type, operating system, project, and service are in scope, and that the required detection and event services are enabled. Do not infer that an excluded Windows VM, confidential VM, managed container, or server outside the provider platform is protected by the same detector.
Quick Recap
Administrator checklist
- Document each host’s role, normal CPU pattern, approved heavy jobs, and maintenance windows.
- Collect host and process CPU with timestamps and enough history to show recurring cycles.
- Alert on unexpected or sustained deviation using locally tuned thresholds and durations.
- Capture PID, parent, account, path, arguments, start time, binary or image identity, and recent changes.
- Search for miner-related strings as investigation leads, then verify the binary and activity.
- Correlate CPU with memory/code detections, DNS, outbound traffic, container identity, and cloud audit events.
- Preserve evidence before containment when operationally safe; avoid indiscriminate process termination.
- Route findings to monitored SIEM/SOAR and security contacts with the IAM permissions needed to respond.
- Patch, restrict service accounts, protect secrets, and rebuild from known-good sources when integrity is uncertain.
- Review CPU, resource, and cost anomalies together, without treating any single one as proof.
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