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[CWS] Introduce CWS network flow monitor events #32350

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What does this PR do?

This PR tackles 5 things:

  • Improves our pid <---> flow mapping in kernel space so that we more accurately attribute each network packet to the correct process. Some of the improvements require new eBPF features (like the BPF_MAP_TYPE_SK_STORAGE map type) and are therefore gated by the running kernel version (TL;DR kernel 5.11+)
  • Introduces logic to monitor, aggregate and periodically flush network flows back to user space for further processing. This requires some new eBPF features and is therefore gated by the running kernel version (TL;DR kernel 5.13+)
  • Introduces a new event type called network_flow_monitor to send network flows to user space. This event exposes the full 5 tuple, along with network telemetry like packet count and data size.
  • Inserts network_flow_monitor events in activity dumps for further processing.
  • Makes some clean up in various parts of the agent, such as: improve our activity dump graphs generation to make them more readable, improve how we organize our kernel space network code, improve our accessor generation logic for iterators.

dump

2 new agent configuration parameters were added:

  • event_monitoring_config.network.flow_monitor.enabled: controls if the network flow monitor should be enabled (when the kernel is recent enough to run it).
  • event_monitoring_config.network.flow_monitor.period: controls how often flows should be flushed to user space for long running processes.

Motivation

This PR generates the events we'll use to address 2 use cases in upcoming PRs:

  • We want to enrich the context of our events with network telemetry.
  • We want to detect network scanners and more specifically (for now) when a process on a host monitored by CWS attempts to scan another machine (for example, with lateral mouvement in mind).

This PR is only a first step towards those 2 goals, and provides a way to continuously monitor network activity and stream it back to user space with performance in mind. Upcoming PRs will take care of the rest !

Describe how you validated your changes

Testing this feature in activity dumps

First, turn on the feature with:

event_monitoring_config:
  network:
    flow_monitor:
      enabled: true

runtime_security_config:
  activity_dump:
    enabled: true
    local_storage:
      formats:
        - ...
        - dot
    traced_event_types:
      - ...
      - network_flow_monitor

Start the agent. Once it's running, start a new container and generate some network activity. Stop the dump and open the output .dot file. You should see network flows in the graph. Note that only the dot export contain the flows for now

Testing this feature with a rule

First, turn on the feature with:

event_monitoring_config:
  network:
    flow_monitor:
      enabled: true

You can use a rule like:

network_flow_monitor.flows.length > 0

Start the agent. Once it's running, generate some network activity. You should start seeing events.

@Gui774ume Gui774ume added changelog/no-changelog component/system-probe team/agent-security qa/done QA done before merge and regressions are covered by tests labels Dec 18, 2024
@Gui774ume Gui774ume added this to the 7.63.0 milestone Dec 18, 2024
@Gui774ume Gui774ume requested review from a team as code owners December 18, 2024 15:59
@github-actions github-actions bot added the long review PR is complex, plan time to review it label Dec 18, 2024
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Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: 019a34ce-cae6-40cc-83b3-89a4394ceb64

Baseline: 52f0517
Comparison: a5253a0
Diff

Optimization Goals: ✅ No significant changes detected

Fine details of change detection per experiment

perf experiment goal Δ mean % Δ mean % CI trials links
tcp_syslog_to_blackhole ingress throughput +2.28 [+2.23, +2.33] 1 Logs
quality_gate_logs % cpu utilization +0.56 [-2.42, +3.55] 1 Logs
quality_gate_idle_all_features memory utilization +0.43 [+0.31, +0.55] 1 Logs bounds checks dashboard
file_to_blackhole_1000ms_latency egress throughput +0.39 [-0.39, +1.16] 1 Logs
quality_gate_idle memory utilization +0.22 [+0.17, +0.27] 1 Logs bounds checks dashboard
file_tree memory utilization +0.20 [+0.06, +0.34] 1 Logs
file_to_blackhole_100ms_latency egress throughput +0.13 [-0.58, +0.83] 1 Logs
uds_dogstatsd_to_api ingress throughput +0.02 [-0.07, +0.11] 1 Logs
tcp_dd_logs_filter_exclude ingress throughput -0.00 [-0.02, +0.01] 1 Logs
file_to_blackhole_0ms_latency_http2 egress throughput -0.02 [-0.86, +0.83] 1 Logs
file_to_blackhole_0ms_latency egress throughput -0.03 [-0.86, +0.81] 1 Logs
file_to_blackhole_500ms_latency egress throughput -0.05 [-0.82, +0.73] 1 Logs
file_to_blackhole_300ms_latency egress throughput -0.06 [-0.69, +0.56] 1 Logs
file_to_blackhole_0ms_latency_http1 egress throughput -0.09 [-0.94, +0.76] 1 Logs
otel_to_otel_logs ingress throughput -0.13 [-0.80, +0.54] 1 Logs
file_to_blackhole_1000ms_latency_linear_load egress throughput -0.34 [-0.80, +0.12] 1 Logs
uds_dogstatsd_to_api_cpu % cpu utilization -0.42 [-1.13, +0.30] 1 Logs

Bounds Checks: ❌ Failed

perf experiment bounds_check_name replicates_passed links
file_to_blackhole_0ms_latency_http1 lost_bytes 9/10
file_to_blackhole_0ms_latency lost_bytes 10/10
file_to_blackhole_0ms_latency memory_usage 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_logs, bounds check lost_bytes: 10/10 replicas passed. Gate passed.
  • quality_gate_logs, 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.

@janine-c janine-c self-assigned this Dec 18, 2024
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