[CWS] introduce FIM meta rules

[paulcacheux]

What does this PR do?

This PR introduces the support for FIM meta rules. Basically this allows you to write:

(fim.write.file.path == "/tmp/test" || fim.write.file.name == "abc") && process.file.name == "def" && container.id != ""

and the evaluator will load the following rules:

open: ((open.file.path == "/tmp/test" || open.file.name == "abc") && process.file.name == "def" && container.id != "") && open.flags & (O_CREAT|O_TRUNC|O_APPEND|O_RDWR|O_WRONLY) > 0
chmod: (chmod.file.path == "/tmp/test" || chmod.file.name == "abc") && process.file.name == "def" && container.id != ""
chown: (chown.file.path == "/tmp/test" || chown.file.name == "abc") && process.file.name == "def" && container.id != ""
link: (link.file.path == "/tmp/test" || link.file.name == "abc") && process.file.name == "def" && container.id != ""
rename: (rename.file.path == "/tmp/test" || rename.file.name == "abc") && process.file.name == "def" && container.id != ""
rename: (rename.file.destination.path == "/tmp/test" || rename.file.destination.name == "abc") && process.file.name == "def" && container.id != ""
unlink: (unlink.file.path == "/tmp/test" || unlink.file.name == "abc") && process.file.name == "def" && container.id != ""
utimes: (utimes.file.path == "/tmp/test" || utimes.file.name == "abc") && process.file.name == "def" && container.id != ""

The original ID of the rule will be propagated to the serialized event and will be correct one when viewed in the UI.

Motivation

Describe how you validated your changes

Possible Drawbacks / Trade-offs

Additional Notes

[agent-platform-auto-pr]

Uncompressed package size comparison

Comparison with ancestor 7f64411d20274e2ee9a65e683e9ceda41a5da5ed

Diff per package

package	diff	status	size	ancestor	threshold
datadog-agent-x86_64-rpm	0.03MB	⚠️	1197.24MB	1197.21MB	140.00MB
datadog-agent-x86_64-suse	0.03MB	⚠️	1197.24MB	1197.21MB	140.00MB
datadog-agent-amd64-deb	0.03MB	⚠️	1187.98MB	1187.95MB	140.00MB
datadog-heroku-agent-amd64-deb	0.00MB	✅	504.88MB	504.88MB	70.00MB
datadog-dogstatsd-amd64-deb	0.00MB	✅	78.57MB	78.57MB	10.00MB
datadog-dogstatsd-x86_64-rpm	0.00MB	✅	78.65MB	78.65MB	10.00MB
datadog-dogstatsd-x86_64-suse	0.00MB	✅	78.65MB	78.65MB	10.00MB
datadog-dogstatsd-arm64-deb	0.00MB	✅	55.77MB	55.77MB	10.00MB
datadog-iot-agent-amd64-deb	0.00MB	✅	113.34MB	113.34MB	10.00MB
datadog-iot-agent-x86_64-rpm	0.00MB	✅	113.41MB	113.41MB	10.00MB
datadog-iot-agent-x86_64-suse	0.00MB	✅	113.41MB	113.41MB	10.00MB
datadog-iot-agent-arm64-deb	0.00MB	✅	108.80MB	108.80MB	10.00MB
datadog-iot-agent-aarch64-rpm	0.00MB	✅	108.87MB	108.87MB	10.00MB
datadog-agent-aarch64-rpm	-0.01MB	✅	943.13MB	943.14MB	140.00MB
datadog-agent-arm64-deb	-0.01MB	✅	933.89MB	933.90MB	140.00MB

Decision

⚠️ Warning

[agent-platform-auto-pr]

Test changes on VM

Use this command from test-infra-definitions to manually test this PR changes on a VM:

inv aws.create-vm --pipeline-id=51708417 --os-family=ubuntu

Note: This applies to commit aaa3255

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: bc7fd00d-05ab-4c77-bcd2-9b64f29ef86c

Baseline: 7f64411
Comparison: aaa3255
Diff

Optimization Goals: ✅ No significant changes detected

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+0.98	[+0.30, +1.65]	1	Logs
➖	quality_gate_logs	% cpu utilization	+0.52	[-2.70, +3.73]	1	Logs
➖	otel_to_otel_logs	ingress throughput	+0.37	[-0.31, +1.06]	1	Logs
➖	file_tree	memory utilization	+0.23	[+0.10, +0.36]	1	Logs
➖	file_to_blackhole_1000ms_latency_linear_load	egress throughput	+0.15	[-0.31, +0.60]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	+0.05	[-0.73, +0.83]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	+0.05	[-0.79, +0.88]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	+0.02	[-0.69, +0.73]	1	Logs
➖	file_to_blackhole_0ms_latency_http2	egress throughput	+0.01	[-0.84, +0.87]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.01, +0.01]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	-0.01	[-0.12, +0.10]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	-0.02	[-0.66, +0.62]	1	Logs
➖	file_to_blackhole_0ms_latency_http1	egress throughput	-0.03	[-0.94, +0.88]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	-0.10	[-0.92, +0.72]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	-0.22	[-0.29, -0.14]	1	Logs bounds checks dashboard
➖	quality_gate_idle	memory utilization	-0.32	[-0.35, -0.29]	1	Logs bounds checks dashboard
➖	tcp_syslog_to_blackhole	ingress throughput	-0.67	[-0.74, -0.61]	1	Logs

Bounds Checks: ✅ Passed

perf	experiment	bounds_check_name	replicates_passed	links
✅	file_to_blackhole_0ms_latency	lost_bytes	10/10
✅	file_to_blackhole_0ms_latency	memory_usage	10/10
✅	file_to_blackhole_0ms_latency_http1	lost_bytes	10/10
✅	file_to_blackhole_0ms_latency_http1	memory_usage	10/10
✅	file_to_blackhole_0ms_latency_http2	lost_bytes	10/10
✅	file_to_blackhole_0ms_latency_http2	memory_usage	10/10
✅	file_to_blackhole_1000ms_latency	memory_usage	10/10
✅	file_to_blackhole_1000ms_latency_linear_load	memory_usage	10/10
✅	file_to_blackhole_100ms_latency	lost_bytes	10/10
✅	file_to_blackhole_100ms_latency	memory_usage	10/10
✅	file_to_blackhole_300ms_latency	lost_bytes	10/10
✅	file_to_blackhole_300ms_latency	memory_usage	10/10
✅	file_to_blackhole_500ms_latency	lost_bytes	10/10
✅	file_to_blackhole_500ms_latency	memory_usage	10/10
✅	quality_gate_idle	memory_usage	10/10	bounds checks dashboard
✅	quality_gate_idle_all_features	memory_usage	10/10	bounds checks dashboard
✅	quality_gate_logs	lost_bytes	10/10
✅	quality_gate_logs	memory_usage	10/10

Explanation

Confidence level: 90.00%
Effect size tolerance: |Δ mean %| ≥ 5.00%

Performance changes are noted in the perf column of each table:

• ✅ = significantly better comparison variant performance
• ❌ = significantly worse comparison variant performance
• ➖ = no significant change in performance

A regression test is an A/B test of target performance in a repeatable rig, where "performance" is measured as "comparison variant minus baseline variant" for an optimization goal (e.g., ingress throughput). Due to intrinsic variability in measuring that goal, we can only estimate its mean value for each experiment; we report uncertainty in that value as a 90.00% confidence interval denoted "Δ mean % CI".

For each experiment, we decide whether a change in performance is a "regression" -- a change worth investigating further -- if all of the following criteria are true:

1. Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.

2. Its 90.00% confidence interval "Δ mean % CI" does not contain zero, indicating that if our statistical model is accurate, there is at least a 90.00% chance there is a difference in performance between baseline and comparison variants.

3. Its configuration does not mark it "erratic".

CI Pass/Fail Decision

✅ Passed. All Quality Gates passed.

• quality_gate_idle, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_idle_all_features, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_logs, bounds check lost_bytes: 10/10 replicas passed. Gate passed.
• quality_gate_logs, bounds check memory_usage: 10/10 replicas passed. Gate passed.

[spikat]

I wonder if we should set the "groupID" to the baseID to keep track of it?

[YoannGh]

I think the user-facing ID will still be the baseID because the rest of the code uses the policy-defined ID and not the expanded one, so there's no need to set the groupID to the baseID @paulcacheux let me know if I'm wrong here
Also know that the policy-defined ID and the eval.RuleID can be different what do we think about renaming the eval.Rule.ID field to eval.Rule.ExpandedID so that we don't mix up the two?

[YoannGh]

(same question for the eval.Rule.Expression field)

[paulcacheux]

yes but I think it would be nice to include the group id in the expanded ID as well for unicity

agreed on ExpandedID

[paulcacheux]

If it's ok for you I will do the renaming in a follow up PR, otherwise this adds a lot of review noise