detecting-ransomware-precursors-in-network

mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026

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$npx skills install mukul975/Anthropic-Cybersecurity-Skills/detecting-ransomware-precursors-in-network
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summary

Detects early-stage ransomware indicators in network traffic before encryption begins, including initial access broker activity, command-and-control beaconing, credential harvesting, reconnaissance scanning, and staging behavior. Uses network detection tools (Zeek, Suricata, Arkime), SIEM correlation rules, and threat intelligence feeds to identify ransomware precursor patterns such as Cobalt Strike beacons, Mimikatz network signatures, and RDP brute-force attempts. Activates for requests involving pre-ransomware detection, network-based ransomware indicators, or early warning ransomware monitoring.

skill.md
name
detecting-ransomware-precursors-in-network
description
'Detects early-stage ransomware indicators in network traffic before encryption begins, including initial access broker activity, command-and-control beaconing, credential harvesting, reconnaissance scanning, and staging behavior. Uses network detection tools (Zeek, Suricata, Arkime), SIEM correlation rules, and threat intelligence feeds to identify ransomware precursor patterns such as Cobalt Strike beacons, Mimikatz network signatures, and RDP brute-force attempts. Activates for requests involving pre-ransomware detection, network-based ransomware indicators, or early warning ransomware monitoring. '
domain
cybersecurity
subdomain
ransomware-defense
tags
- ransomware - detection - network-security - incident-response - defense
version
1.0.0
author
mahipal
license
Apache-2.0
nist_csf
- PR.DS-11 - RS.MA-01 - RC.RP-01 - PR.IR-01

Detecting Ransomware Precursors in Network Traffic

When to Use

  • Building detection rules for pre-ransomware network activity (the average time from Cobalt Strike deployment to encryption is 17 minutes)
  • Monitoring for initial access broker (IAB) indicators that precede ransomware deployment
  • Creating SIEM correlation rules that chain multiple precursor events into high-confidence alerts
  • Tuning network detection systems to distinguish ransomware staging from normal administrative activity
  • Investigating suspicious network patterns that may indicate ransomware operators have established a foothold

Do not use for post-encryption response (see recovering-from-ransomware-attack). This skill focuses on the pre-encryption detection window where containment can prevent data loss.

Prerequisites

  • Network detection platform (Zeek/Bro, Suricata, or Arkime/Moloch) deployed on network TAP or SPAN ports
  • SIEM platform (Splunk, Elastic Security, Microsoft Sentinel, or QRadar) ingesting network logs
  • Threat intelligence feeds covering ransomware IOCs (CISA, abuse.ch, OTX, MISP)
  • Network flow data (NetFlow/IPFIX) from core routers and firewalls
  • DNS query logging from internal resolvers
  • Full packet capture capability for incident investigation

Workflow

Step 1: Identify Ransomware Kill Chain Phases in Network Traffic

Map network-observable indicators to each pre-encryption phase:

Kill Chain PhaseNetwork IndicatorsDetection Source
Initial AccessRDP brute force, VPN credential stuffing, phishing callbackFirewall logs, IDS, proxy logs
C2 EstablishmentCobalt Strike beacons (HTTPS/DNS), Sliver/Brute Ratel callbacksZeek SSL/HTTP logs, DNS logs
Credential HarvestingNTLM relay, Kerberoasting, DCSync trafficZeek Kerberos/NTLM logs, DC logs
ReconnaissanceInternal port scanning, AD enumeration (LDAP/SMB)Zeek conn.log, flow data
Lateral MovementPsExec/WMI/WinRM traffic, RDP pivoting, SMB file copiesZeek SMB/DCE-RPC logs
StagingData aggregation, archive creation, cloud upload prepProxy logs, DNS logs, DLP

Step 2: Deploy Network Detection Rules

Suricata rules for common ransomware precursors:

# Cobalt Strike default HTTPS beacon profile detection
alert tls $HOME_NET any -> $EXTERNAL_NET any (msg:"RANSOMWARE PRECURSOR - Cobalt Strike Default TLS Certificate"; tls.cert_subject; content:"Major Cobalt Strike"; sid:3000001; rev:1;)

# Cobalt Strike DNS beacon
alert dns $HOME_NET any -> any 53 (msg:"RANSOMWARE PRECURSOR - Cobalt Strike DNS Beacon Pattern"; dns.query; pcre:"/^[a-z0-9]{3}\.[a-z]{4,8}\./"; threshold:type both, track by_src, count 50, seconds 60; sid:3000002; rev:1;)

# Mimikatz network signature (DCSync - DRS GetNCChanges)
alert tcp $HOME_NET any -> $HOME_NET 135 (msg:"RANSOMWARE PRECURSOR - Possible DCSync/Mimikatz"; content:"|05 00 0b|"; offset:0; depth:3; content:"|e3 51 4d 2b 4b 47 15 d2|"; sid:3000003; rev:1;)

# Internal network scanning (many connections, few bytes)
alert tcp $HOME_NET any -> $HOME_NET any (msg:"RANSOMWARE PRECURSOR - Internal Port Scan"; flags:S; threshold:type both, track by_src, count 100, seconds 10; sid:3000004; rev:1;)

# PsExec service installation over SMB
alert tcp $HOME_NET any -> $HOME_NET 445 (msg:"RANSOMWARE PRECURSOR - PsExec Service Install"; content:"|ff|SMB"; content:"PSEXESVC"; nocase; sid:3000005; rev:1;)

# RDP brute force from internal host (lateral movement)
alert tcp $HOME_NET any -> $HOME_NET 3389 (msg:"RANSOMWARE PRECURSOR - Internal RDP Brute Force"; flow:to_server,established; threshold:type both, track by_src, count 20, seconds 60; sid:3000006; rev:1;)

# Large SMB file transfer (data staging)
alert tcp $HOME_NET any -> $HOME_NET 445 (msg:"RANSOMWARE PRECURSOR - Large SMB Transfer Possible Staging"; flow:to_server,established; dsize:>60000; threshold:type both, track by_src, count 100, seconds 300; sid:3000007; rev:1;)

Zeek scripts for behavioral detection:

# detect_ransomware_precursors.zeek
# Detect high volume of failed SMB connections (credential testing)

@load base/protocols/smb

module RansomwarePrecursor;

export {
    redef enum Notice::Type += {
        SMB_Brute_Force,
        Suspicious_Internal_Scan,
        Excessive_DNS_Queries,
        SMB_Admin_Share_Access,
    };

    const smb_fail_threshold = 10 &redef;
    const scan_threshold = 50 &redef;
    const dns_query_threshold = 200 &redef;
}

global smb_fail_count: table[addr] of count &default=0 &create_expire=5min;
global conn_count: table[addr] of set[addr] &create_expire=1min;

event smb2_message(c: connection, hdr: SMB2::Header, is_orig: bool) {
    if (hdr$status != 0) {
        ++smb_fail_count[c$id$orig_h];
        if (smb_fail_count[c$id$orig_h] >= smb_fail_threshold) {
            NOTICE([$note=SMB_Brute_Force,
                    $msg=fmt("Host %s has %d failed SMB attempts", c$id$orig_h, smb_fail_count[c$id$orig_h]),
                    $src=c$id$orig_h,
                    $identifier=cat(c$id$orig_h)]);
        }
    }
}

event new_connection(c: connection) {
    if (c$id$orig_h in Site::local_nets && c$id$resp_h in Site::local_nets) {
        if (c$id$orig_h !in conn_count)
            conn_count[c$id$orig_h] = set();
        add conn_count[c$id$orig_h][c$id$resp_h];
        if (|conn_count[c$id$orig_h]| >= scan_threshold) {
            NOTICE([$note=Suspicious_Internal_Scan,
                    $msg=fmt("Host %s connected to %d internal hosts in 1 min", c$id$orig_h, |conn_count[c$id$orig_h]|),
                    $src=c$id$orig_h,
                    $identifier=cat(c$id$orig_h)]);
        }
    }
}

Step 3: Create SIEM Correlation Rules

Splunk correlation for ransomware precursor chain:

| tstats count FROM datamodel=Network_Traffic
  WHERE earliest=-24h All_Traffic.dest_port IN (445, 135, 139, 3389, 5985, 5986)
    AND All_Traffic.src_ip IN 10.0.0.0/8
    AND All_Traffic.dest_ip IN 10.0.0.0/8
  BY All_Traffic.src_ip, All_Traffic.dest_port, _time span=1h
| stats dc(All_Traffic.dest_port) as port_count,
        values(All_Traffic.dest_port) as ports,
        count as total_conns
  BY All_Traffic.src_ip
| where port_count >= 3 AND total_conns > 50
| rename All_Traffic.src_ip as src_ip
| lookup threat_intel_ioc ip as src_ip OUTPUT threat_type
| eval risk_score = case(
    port_count >= 5 AND total_conns > 200, "CRITICAL",
    port_count >= 3 AND total_conns > 50, "HIGH",
    1=1, "MEDIUM")
| table src_ip, ports, port_count, total_conns, risk_score, threat_type

Microsoft Sentinel KQL - Ransomware precursor correlation:

let timeframe = 24h;
let RDPBruteForce = SecurityEvent
| where TimeGenerated > ago(timeframe)
| where EventID == 4625
| where LogonType == 10
| summarize FailedRDP = count() by TargetAccount, IpAddress, bin(TimeGenerated, 1h)
| where FailedRDP > 10;
let SuspiciousSMB = SecurityEvent
| where TimeGenerated > ago(timeframe)
| where EventID == 5145
| where ShareName has "ADMIN$" or ShareName has "C$" or ShareName has "IPC$"
| summarize AdminShareAccess = count() by SubjectUserName, IpAddress, bin(TimeGenerated, 1h)
| where AdminShareAccess > 5;
let ServiceInstalls = SecurityEvent
| where TimeGenerated > ago(timeframe)
| where EventID == 7045
| where ServiceName has_any ("PSEXESVC", "meterpreter", "beacon");
RDPBruteForce
| join kind=inner SuspiciousSMB on IpAddress
| project TimeGenerated, IpAddress, TargetAccount, FailedRDP, SubjectUserName, AdminShareAccess
| extend AlertTitle = "Ransomware Precursor: RDP Brute Force + Admin Share Access"

Step 4: Integrate Threat Intelligence

Configure automated IOC feeds for known ransomware infrastructure:

# Download and update ransomware C2 blocklists
# abuse.ch Feodo Tracker (Cobalt Strike, TrickBot, BazarLoader C2s)
curl -s https://feodotracker.abuse.ch/downloads/ipblocklist.csv | \
  grep -v "^#" | cut -d, -f2 > /opt/threat-intel/feodo_ips.txt

# abuse.ch URLhaus (malware distribution URLs)
curl -s https://urlhaus.abuse.ch/downloads/csv_recent/ | \
  grep -v "^#" | cut -d, -f3 > /opt/threat-intel/urlhaus_urls.txt

# abuse.ch ThreatFox (ransomware IOCs)
curl -s https://threatfox.abuse.ch/export/csv/recent/ | \
  grep -i "ransomware" | cut -d, -f3 > /opt/threat-intel/ransomware_iocs.txt

# CISA Known Exploited Vulnerabilities (initial access vectors)
curl -s https://www.cisa.gov/sites/default/files/feeds/known_exploited_vulnerabilities.json | \
  python3 -c "import json,sys; data=json.load(sys.stdin); [print(v['cveID'],v['vendorProject'],v['product']) for v in data['vulnerabilities'] if 'ransomware' in v.get('knownRansomwareCampaignUse','').lower()]"

Step 5: Establish Alert Triage and Escalation

Define triage procedures based on precursor confidence level:

Alert TypeConfidenceResponse TimeAction
Confirmed Cobalt Strike beaconHigh15 minutesIsolate host immediately, trigger IR
DCSync/Kerberoasting from non-DCHigh15 minutesDisable account, isolate host, trigger IR
Internal port scan + admin share accessMedium-High30 minutesInvestigate source host, check EDR telemetry
RDP brute force from internal hostMedium1 hourVerify if legitimate admin activity, check host
Unusual DNS query volumeLow-Medium4 hoursCheck for DNS tunneling, correlate with other alerts

Key Concepts

TermDefinition
Ransomware PrecursorNetwork activity that precedes ransomware encryption, including C2 communication, lateral movement, and data staging
Dwell TimeTime between initial compromise and ransomware deployment, averaging 21 days but sometimes as short as 17 minutes
Initial Access Broker (IAB)Threat actors who sell compromised network access to ransomware operators on dark web markets
BeaconingPeriodic C2 callbacks from implants (Cobalt Strike, Sliver) that can be detected by analyzing connection timing patterns
KerberoastingCredential harvesting technique requesting Kerberos service tickets for offline cracking, detectable via unusual TGS-REQ patterns
DCSyncTechnique using Directory Replication Service to extract password hashes from domain controllers, critical ransomware precursor

Tools & Systems

  • Zeek (formerly Bro): Network analysis framework generating structured logs for SMB, Kerberos, DNS, HTTP, and TLS connections
  • Suricata: High-performance IDS/IPS with protocol analysis and multi-threading support for ransomware signature detection
  • Arkime (formerly Moloch): Full packet capture and search platform for deep forensic investigation of network events
  • RITA (Real Intelligence Threat Analytics): Open-source tool for detecting beaconing, DNS tunneling, and long connections in Zeek logs
  • AC-Hunter: Network threat hunting platform from Active Countermeasures for beacon detection and C2 identification

Common Scenarios

Scenario: Detecting LockBit Precursors in a Manufacturing Network

Context: A manufacturing company's SOC receives an alert for unusual SMB traffic from a workstation (10.1.5.42) in the engineering department. The workstation connected to 47 internal hosts on port 445 within 5 minutes at 2:00 AM.

Approach:

  1. Zeek conn.log analysis shows 10.1.5.42 initiated connections to 47 unique internal IPs on port 445, 135, and 3389 between 01:55-02:05
  2. Zeek ssl.log reveals an outbound HTTPS connection to 185.x.x.x every 60 seconds with consistent 48-byte payloads (Cobalt Strike beacon pattern)
  3. RITA beacon analysis confirms high beacon score (0.96) for the external IP with 60-second jitter
  4. Zeek kerberos.log shows TGS-REQ for multiple SPN accounts from 10.1.5.42 (Kerberoasting)
  5. SMB tree_connect events show access to ADMIN$ shares on 12 hosts (lateral movement staging)
  6. Containment: Host isolated, credentials for engineering user reset, blocking rule for C2 IP deployed
  7. Full IR initiated before ransomware deployment could begin

Pitfalls:

  • Dismissing internal port scans as vulnerability scanner activity without verifying the source is an authorized scanner
  • Not correlating individual low-severity alerts (DNS anomaly + SMB access + failed logins) into a high-severity chain
  • Setting detection thresholds too high to avoid false positives, missing low-and-slow reconnaissance
  • Ignoring encrypted traffic analysis (JA3/JA4 fingerprinting) that can identify Cobalt Strike even in TLS tunnels

Output Format

## Ransomware Precursor Detection Alert

**Alert ID**: [SIEM-generated ID]
**Detection Time**: [Timestamp]
**Source Host**: [IP / Hostname]
**Confidence**: [High / Medium / Low]
**Kill Chain Phase**: [Initial Access / C2 / Credential Harvest / Recon / Lateral Movement / Staging]

### Indicators Detected
| Indicator | Source | Detail | MITRE ATT&CK |
|-----------|--------|--------|--------------|
| [Type] | [Zeek/Suricata/SIEM] | [Description] | [T-ID] |

### Correlation Chain
1. [Timestamp] - [Event 1]
2. [Timestamp] - [Event 2]
3. [Timestamp] - [Event 3]

### Recommended Actions
- [ ] Isolate source host from network
- [ ] Check EDR telemetry for host-based indicators
- [ ] Reset credentials for affected user accounts
- [ ] Block identified C2 infrastructure
- [ ] Escalate to incident response team
how to use detecting-ransomware-precursors-in-network

How to use detecting-ransomware-precursors-in-network on Cursor

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1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add detecting-ransomware-precursors-in-network
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills install mukul975/Anthropic-Cybersecurity-Skills/detecting-ransomware-precursors-in-network

The skills CLI fetches detecting-ransomware-precursors-in-network from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.

3

Select Cursor when prompted

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4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/detecting-ransomware-precursors-in-network

Reload or restart Cursor to activate detecting-ransomware-precursors-in-network. Access the skill through slash commands (e.g., /detecting-ransomware-precursors-in-network) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

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Use Cases

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Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

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Ratings

4.642 reviews
  • Isabella Sharma· Dec 28, 2024

    We added detecting-ransomware-precursors-in-network from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Shikha Mishra· Dec 24, 2024

    Keeps context tight: detecting-ransomware-precursors-in-network is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Maya Park· Dec 24, 2024

    I recommend detecting-ransomware-precursors-in-network for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Ama Shah· Dec 24, 2024

    Keeps context tight: detecting-ransomware-precursors-in-network is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Sakura Liu· Dec 20, 2024

    Useful defaults in detecting-ransomware-precursors-in-network — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Hiroshi Thomas· Nov 19, 2024

    Solid pick for teams standardizing on skills: detecting-ransomware-precursors-in-network is focused, and the summary matches what you get after install.

  • Yash Thakker· Nov 15, 2024

    Registry listing for detecting-ransomware-precursors-in-network matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Kwame Tandon· Nov 15, 2024

    Registry listing for detecting-ransomware-precursors-in-network matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Hana Perez· Oct 10, 2024

    detecting-ransomware-precursors-in-network has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Dhruvi Jain· Oct 6, 2024

    detecting-ransomware-precursors-in-network reduced setup friction for our internal harness; good balance of opinion and flexibility.

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