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What CPU monitoring can—and cannot—tell you
Cryptojacking is unauthorized cryptocurrency mining on infrastructure you operate or pay for. Mining software is designed to consume compute continuously, so CPU trends can expose an intrusion before an operator notices a new account, modified application, or large bill. The useful question is not “Did CPU cross 80%?” but “Did this host depart from its established pattern, and what caused the departure?”
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There is no percentage or duration that applies to every server. A batch-rendering node may run near saturation during an approved job; an otherwise idle database server may be suspicious at a much lower level. Set thresholds from the host’s own history and workload schedule, and record the conditions that make a deviation legitimate.
- CPU is an early-warning signal, not proof. A spike can come from a deployment, backup, compiler, query, vulnerability scan, or scheduled data-processing job.
- Process identity supplies context. The executable, path, arguments, owner, start time, parent process, and recent file changes explain who is consuming the cycles.
- Network and memory evidence raises confidence. Miner-like DNS lookups, outbound connections, memory signatures, and hardware-counter behavior are more persuasive together than any one CPU number.
- Response must be deliberate. Killing a process immediately can destroy evidence or interrupt a critical service. Follow the incident plan, preserve logs, and contain the workload with an understanding of operational impact.
Build a baseline before writing alerts
A baseline should describe normal CPU behavior for each host and service over enough time to include ordinary traffic cycles, maintenance, deployments, and scheduled jobs. Baselines are per workload: copying one server’s threshold to every machine creates either noise or blind spots.
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Record the dimensions that change CPU demand
- Host, instance type, operating system, and role (web, database, queue, build runner, analytics, or compute worker).
- Service-level CPU, total host CPU, load average, and the number of active processes or threads.
- Time-of-day and day-of-week patterns, including batch windows, backups, log rotation, and autoscaling events.
- Deployment, configuration, and administrative-change times so an expected increase has an owner.
- Approved compute-heavy workloads such as HPC, simulations, media encoding, machine-learning jobs, or large builds.
- Whether a reading comes from a host agent, a hypervisor/provider metric, a container metric, or an application metric; these views have different blind spots.
Choose a baseline window and keep it current
Use a window long enough to capture normal weekly behavior, then update it after material changes such as a resize, new release, workload migration, or seasonal traffic. Retain raw time-series data as well as the baseline summary. A median and high-percentile profile is usually easier to interpret than a single average because short, legitimate bursts should not redefine normal behavior.
Mark exceptions instead of weakening the threshold
Put maintenance, approved jobs, and deployment windows on a calendar that alerting can consult. If a recurring job is legitimate, annotate it and verify that the process, account, and command line match the approved record. Do not permanently raise a threshold merely to silence alerts; that hides a later compromise.
Design CPU alerts around deviations
Alert on an unexpected rise or a sustained departure from the host’s baseline. Tune both the deviation amount and the duration locally. The official guidance reviewed for this topic does not prescribe a universal percentage or a universal number of minutes.
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- Compare the current CPU series with the baseline for the same host, service, and time context.
- Require persistence long enough to distinguish a brief request burst from a continuing workload. Use a shorter persistence period for a normally idle security-sensitive host and a longer one for bursty services.
- Suppress or annotate periods covered by an approved change, but retain the event and the change identifier.
- Increase severity when the CPU deviation is accompanied by a new process, an unknown executable path, a new outbound destination, or a miner-related detection.
- Send the alert to a monitored queue and to an on-call person who can identify the workload owner.
Measure more than utilization
Collect user and system CPU, load average, context switches, process count, and (where available) per-process CPU. A host can show moderate aggregate utilization while one process monopolizes a single core. Conversely, a high load average may reflect blocked I/O rather than computation. Treat these metrics as clues that direct you to process and service telemetry.
Keep alert routing operational
An alert that lands in an unmonitored mailbox is not a control. Google’s guidance recommends integrating Security Command Center findings with existing SIEM or SOAR systems, ensuring responder IAM permissions are in place, and continuously monitoring the security contact channel. Apply the same principle to any monitoring stack: test delivery, escalation, deduplication, and acknowledgment, and document who owns the next action.
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Identify the process behind a CPU spike
When an alert fires, first establish what is consuming CPU and whether that activity is authorized. Capture evidence before making changes that could erase it.
Linux triage commands
Run commands appropriate to your operating system and permissions. Save output with a timestamp and the host identifier.
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ps -eo pid,ppid,user,%cpu,%mem,lstart,etime,args --sort=-%cpu | head -n 25
top -H -b -n 1 -o %CPU | head -n 40
Use the process ID from the list to inspect its executable, arguments, environment, open files, and parent chain. On systems that provide it:
readlink -f /proc/<PID>/exe
tr ' ' ' ' < /proc/<PID>/cmdline; echo
ps -p <PID> -o pid,ppid,user,group,etimes,%cpu,%mem,args
ls -l /proc/<PID>/cwd /proc/<PID>/fd 2>/dev/null
For a sampled view over time, use a process monitor such as pidstat when it is installed:
pidstat -u -p ALL 5 12
These commands do not determine that a process is malicious. They establish identity, ownership, timing, and persistence so you can compare the process with deployment records and approved service definitions.
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Questions to answer for every unfamiliar process
- Which user and service account started it?
- What executable path and file hash are present, and when was the file created or modified?
- What are the complete arguments, parent process, working directory, open files, listening sockets, and outbound connections?
- Did it start after a new release, a scheduled job, a remote login, a privilege change, or a configuration edit?
- Is the binary part of an approved package or container image, and does its signature or provenance verify?
Search for miner-related clues without treating names as proof
Google’s investigation guidance calls out strings including btc.com, ethminer, xmrig, cpuminer, and randomx. Search process arguments, scripts, service files, startup configuration, container layers, and recently changed files for these leads. A name can be spoofed, copied into a benign test, or absent from a renamed binary, so verify the executable and its surrounding activity.
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Limit recursive searches to paths you can safely inspect; scanning an entire mounted filesystem during an incident can create additional load and alter timing evidence.
Correlate CPU with memory, DNS, and network telemetry
Cryptomining detection becomes stronger when independent signals agree. Google Cloud’s VM Threat Detection analyzes application names, per-process CPU usage, hashes of memory pages, CPU hardware performance counters, and executed machine code to determine whether an application matches known cryptocurrency-mining signatures. Its overview describes this multi-signal approach explicitly; CPU alone is only one input.
Memory and execution evidence
Review memory-page hash or YARA-style detections, loaded libraries, and executed code when your platform provides them. A process that changes its in-memory code, loads an unexpected library, or matches a known miner pattern deserves priority even if its current CPU use has fallen. Preserve the finding and the related process metadata before restarting the host.
DNS and outbound connections
Look for DNS lookups and connections to known cryptomining domains or IP addresses, especially when they originate from the same process or workload that produced the CPU alert. Google recommends enabling Cloud DNS logging so lookups can be correlated with VM findings. DNS evidence is not conclusive by itself: shared infrastructure, sinkholes, testing, or a compromised resolver can produce misleading matches.
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Containers and Stratum traffic
In containers, known Stratum-protocol communication is a suspicious indicator. Google notes that legitimate container workloads typically do not use Stratum, but a Stratum finding still requires investigation of the image, command, owner, and destination. Map the container to its pod, node, deployment, image digest, and service account before containment; otherwise a replacement container may recreate the activity.
Correlate time, identity, and destination
Build a timeline containing the CPU deviation, process start, file modification, DNS lookup, network connection, deployment, and authentication events. A process that began immediately after an exposed credential was used and then contacted a mining endpoint is materially different from a compiler launched by a documented build.
What Google Cloud VM Threat Detection covers
Google Cloud documents VM Threat Detection as a provider-managed, agentless capability for supported virtual machines. It periodically scans from the hypervisor into guest-memory views and disk clones without pausing the guest, requiring a guest agent, using guest CPU cycles, or requiring guest network connectivity. That architecture is specific to the supported Google Cloud service; it is not a promise that every cloud monitor or physical server can inspect a guest from outside.
| Monitoring approach | Signals | Deployment model | Operational trade-off |
|---|---|---|---|
| Host CPU metrics | Aggregate and, if collected, per-process CPU and load | Usually a guest agent or host exporter | Broad coverage and low complexity, but CPU-only alerts create false positives and miss low-CPU or dormant miners. |
| Process and endpoint telemetry | Executable, arguments, owner, start time, file and memory evidence | Guest agent, EDR, or equivalent endpoint collection | Better attribution and investigation detail; requires installation, permissions, storage, and tuning. |
| DNS and network monitoring | Lookups, destinations, connection metadata, and protocol indicators | Resolver logging, flow logs, sensors, or network controls | Useful corroboration and fleet visibility; encrypted traffic and shared infrastructure can limit attribution. |
| Provider-managed VM detection | Application names, per-process CPU, memory-page hashes, hardware counters, executed code, and known mining signatures | Agentless, outside-guest scanning for supported VMs | Reduces guest deployment overhead, but coverage and findings depend on the provider, supported VM types, enabled services, and account configuration. |
Enable the relevant VM and event threat-detection services across the projects you intend to protect, configure DNS logging, and route findings into the incident systems your responders already use. Confirm that the security contact inbox is continuously monitored and that responders can view findings and take containment actions.
Understand the Cryptomining Protection Program’s limits
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 virtual machines, Confidential Compute VMs, Google Kubernetes instances, App Engine, Cloud Run, and Cloud Functions.
| Question | Documented position |
|---|---|
| Which workloads are described as eligible? | Eligible Linux-based Compute Engine instances supported by VM Threat Detection; verify current eligibility and terms before relying on the program. |
| Which listed workloads are excluded? | Windows VMs, Confidential Compute VMs, Google Kubernetes instances, App Engine, Cloud Run, and Cloud Functions. |
| Who responds to an attack? | Google states: “Google’s responsibility is limited to detecting and notifying customers of cryptomining attacks. Attack response and remediation remain the customer’s responsibility.” |
| Are credits or reimbursement automatic? | Do not assume so. Eligibility, terms, and program coverage can change; consult the current official program conditions. |
Do not generalize this program to other providers, server types, or container platforms. A provider finding is an additional control, not a substitute for host monitoring, identity controls, patching, and an incident-response plan.
Investigate and contain an alert safely
Use a staged response that preserves evidence and limits damage. Adapt the order to your organization’s incident plan and the service’s availability requirements.
- Open an incident record. Note the host, account, alert time, baseline comparison, process ID, executable path, command line, and affected service.
- Preserve volatile evidence. Export process listings, relevant system and authentication logs, DNS records, flow data, provider findings, and timestamps. Record the commands and tools used.
- Validate ownership. Ask the service owner to confirm whether the process, deployment, job, image, or administrative change is approved. Check the change system and CI/CD history rather than relying on a process name.
- Scope the event. Search for the same executable hash, command pattern, DNS destination, service account, image digest, or persistence mechanism on other hosts.
- Contain according to impact. Options may include isolating a VM, restricting egress, cordoning a node, disabling a compromised credential, or moving traffic to a known-good instance. Coordinate with the owner before disruptive actions when the service is critical.
- Remove persistence and remediate the entry point. Patch the exploited component, rotate exposed secrets, correct permissions, remove unauthorized startup entries, and rebuild from a trusted image when integrity is uncertain.
- Recover and verify. Restore service from known-good sources, monitor CPU and network behavior closely, and confirm that the original process and destinations do not return.
- Improve detection. Add the confirmed indicators, timeline, and missed signal to alert rules, baseline exceptions, and response playbooks.
Do not indiscriminately terminate every high-CPU process. A forced kill may destroy memory evidence, interrupt a legitimate calculation, or trigger an automated restart that obscures the original cause.
Use cloud-cost anomalies as supporting evidence
Mining consumes resources, so an unexpected increase in instance hours, CPU-optimized capacity, or network egress can support a CPU investigation. Compare billing and usage timelines with process and DNS events. A cost increase alone does not prove cryptojacking: traffic growth, autoscaling, a new workload, or a pricing change can produce the same symptom. Conversely, a miner may run on spare capacity without creating an obvious bill spike.
Prevent the next unauthorized miner
- Patch operating systems, runtimes, container images, and internet-facing services; track exceptions with an owner and expiry.
- Review VM and container vulnerabilities, exposed management interfaces, permissive firewall rules, and dangerous startup scripts.
- Restrict service-account permissions to the minimum required, remove unused keys, and monitor unusual token use.
- Protect secrets in managed stores or sealed deployment systems rather than source code, images, shell history, or instance metadata.
- Limit outbound access where practical, log DNS and flow data, and alert on unexpected mining protocols or destinations.
- Require image provenance and deployment review, including the image digest, entrypoint, service account, and resource limits.
- Keep CPU, process, memory, DNS, network, identity, and billing telemetry searchable together for the retention period your incident plan requires.
- Exercise the response process: test alert delivery, security-contact monitoring, IAM access, isolation procedures, rebuilds, and secret rotation.
Example: separating an approved job from cryptojacking
Suppose a normally quiet API VM rises from its usual low utilization to sustained near-saturation overnight. The CPU alert is appropriate, but it is not a verdict. Process telemetry shows a newly started binary in a temporary directory under an unexpected service account, with arguments containing randomx. File and memory telemetry match a miner pattern, and DNS logs show lookups to a mining-related destination. The combined timeline justifies containment and credential review.
Now consider a build worker with the same CPU graph during a scheduled release. Its process is the approved compiler, the parent is the CI runner, the executable hash matches the managed image, and no unusual DNS or outbound connections appear. The baseline annotation and corroborating evidence support closing the alert as expected activity while retaining the event for tuning.
Quick Recap
Operational checklist
- Baseline CPU by host, service, schedule, and approved workload.
- Alert on unexplained deviations with locally tuned persistence and maintenance exceptions.
- Capture process identity, executable path, arguments, owner, parent, start time, and file provenance.
- Search for miner-related strings such as
btc.com,ethminer,xmrig,cpuminer, andrandomx; verify matches rather than treating names as proof. - Correlate memory or code detections, DNS lookups, outbound destinations, Stratum indicators in containers, identity events, and recent changes.
- Route findings to monitored SIEM/SOAR and human contacts with the permissions needed to respond.
- Preserve evidence, scope other hosts, contain under the incident plan, rebuild or remediate, and verify that activity stays gone.
- Review CPU and cloud-cost anomalies together, while treating neither as conclusive alone.
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