~ 5 min read

Actionable Network Observability

Kubeshark automates the detection of suspicious network behaviors and triggers actions by using Javascript in conjunction with L4 and L7 hooks.

Kubernetes network contains significant information that can be critical for use-cases such as incident investigation, API debugging, threat hunting and threat detection. Kubernetes’ highly dynamic and distributed nature make K8s network a blindspot.

kubernetesnetwork is a blindspot

Kubeshark is a new open-source tool that provides real-time protocol-level visibility into K8s network, capturing and monitoring all traffic and payloads going in, out and across containers, pods, nodes and clusters.

You can think of Kubeshark as having a one-click, fully automated installation of Wireshark and TCPdump across all of your K8s pods.

Actionable Automation

The network is vast and it happens all the time. While the information in the network is likely to provide clear indications of breaches and problems, it’s not realistic to process the entire network at all times.

Kubeshark automates the detection of suspicious network behaviors and triggers actions by using Javascript in conjunction with OSI L4 and L7 hooks.

The following short script example uses an L7 hook to detect a 500 response code in real-time and trigger a Slack alert:

function onItemCaptured(data) {
  if (data.response.status === 500)
      env.SLACK_WEBHOOK,      // Webhook URL
      "Server-side Error",    // Pretext (title)
      JSON.stringify(data),   // Message text
      "#ff0000"               // Color code of the message

Kubeshark supports three action categories:


The information in the network in conjunction with actionable automation can help devops and security engineers leverage the power of the network in the following areas:

You can read more about each use-case in the documentation.


See below some selected examples:

Dashboard Alerts

Use the L7 hook onItemQueried in conjunction with the test.* helpers to detect response code 500 and show alerts in the dashboard:

function onItemQueried(data) {
  if (data.response.status === 500)
    return test.fail(data);
    return test.pass(data);

Dashboard Alerts

Build Custom Metrics

Write custom-logic scripts in Javascript in conjunction with the L4/L7 hooks to create your own metrics.

The following example uses an L4 hook onPacketCaptured, some Javascript calculation and the job helper to log the L4 packet count and aggregated size per minute:

var packetCount = 0;
var totalKB = 0;

function onPacketCaptured(info) {
  totalKB += info.length / 1000;

function logPacketCountTotalBytes() {
  console.log("Captured packet count per minute:", packetCount);
  packetCount = 0;
  console.log("Total KB captured per minute:", totalKB);
  totalKB = 0;

jobs.schedule("log-packet-count-total-bytes", "0 */1 * * * *", logPacketCountTotalBytes);

Upload PCAPs to AWS S3

Generate custom network traces (PCAPs) based on a rich filtering language and upload to AWS S3. Each network trace can consolidate numerous network fragments into a single custom network snapshot.

Here’s an example of a script that continuously monitors traffic, matching the traffic against two KFL queries:

  • http and response.status == 500 - HTTP traffic with 500 response code
  • dns - DNS traffic

Matching L4 streams will be added into a PCAP repository, compressed and uploaded to AWS S3.

  "http and response.status == 500",

function onItemCaptured(data) {
  wrapper.kflPcapS3(data, {
      kflArr:             KFL_PCAP_S3_KFL_ARR, // Mandatory

PCAP files matching the KFL queries are uploaded and available in AWS S3: S3 FIles

Read more in the Cloud Forensics section.

Stream Metrics to Grafana

Stream metrics and schema-free documents.

Grafana InfluxDB Query

Read more in the Telemetry & Observability section.

Real-time Alerts and Forensics Using Slack

Use Slack to send real-time alerts that include event information and forensics (e.g network trace in PCAP format).

Read more in the Slack Integration section.