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Introduction:
In an era where adversaries operate at machine speed, relying on manual packet analysis is a recipe for missing critical intrusions. Traditional tools like Wireshark are excellent for deep-dive inspection but were never designed for automated threat hunting at scale. This article introduces a new, open-source-capable tool called PCAP Bloodhound, which automates the detection of beaconing C2 traffic, DNS tunneling, cleartext credentials, and NTLM hash extraction from PCAP files, turning hours of analysis into seconds.
Learning Objectives:
- Understand how to detect Command & Control (C2) beaconing using statistical analysis (Coefficient of Variation) on packet inter-arrival times.
- Learn to identify DNS tunneling by calculating Shannon entropy on domain queries.
- Master the process of extracting NTLM hashes and cleartext credentials from network traffic captures.
- Gain practical experience with command-line tools like
tshark,capinfos, and Python scripts for PCAP analysis.
You Should Know:
- Statistical C2 Beacon Detection: Finding the Needle in the Haystack
Most analysts rely on reputation lists to find bad IPs, but sophisticated C2 infrastructure is often brand new or uses compromised legitimate sites. PCAP Bloodhound ignores reputation and focuses on behavior. It calculates the Coefficient of Variation (CV) on the inter-arrival times of packets for every outbound connection pair. A CV below 0.15 indicates that the connection intervals are suspiciously regular—exactly what you would see from Cobalt Strike or Sliver beacons phoning home.
Step‑by‑step guide: Extracting Beacon Intervals with tshark
To manually hunt for beacons, you can use `tshark` (the command-line version of Wireshark) to isolate traffic to a specific IP and calculate the time deltas.
Replace <C2_IP> with the suspicious IP address
tshark -r capture.pcap -Y "ip.dst == <C2_IP>" -T fields -e frame.time_epoch > timestamps.txt
Use a simple Python script to calculate the Coefficient of Variation
python3 -c "
import numpy as np
import sys
times = [float(line.strip()) for line in open('timestamps.txt') if line.strip()]
if len(times) > 1:
intervals = np.diff(times)
cv = np.std(intervals) / np.mean(intervals)
print(f'Coefficient of Variation: {cv:.3f}')
if cv < 0.15:
print('[!] Possible C2 Beaconing Detected (Regular Intervals)')
else:
print('[-] Traffic appears jittery (likely human or normal service)')
"
2. DNS Tunneling Detection: Entropy and Length Analysis
Normal DNS queries are usually short and use dictionary words. Tunneling tools like `iodine` and `dnscat2` encode data into subdomains, resulting in long, high-entropy strings. PCAP Bloodhound scores every DNS query for Shannon entropy. Normal entropy ranges from 2.5 to 3.2 bits; anything above 3.8 is highly suspicious. It also flags subdomain lengths over 50 characters.
Step‑by‑step guide: Calculating DNS Entropy from the CLI
You can use `tshark` to extract DNS query names and a Python one-liner to calculate entropy.
Extract all DNS query names
tshark -r capture.pcap -Y "dns.qry.name" -T fields -e dns.qry.name > dns_queries.txt
Calculate entropy for each unique query
python3 -c "
import math
import collections
def shannon_entropy(data):
if not data:
return 0
entropy = 0
for x in collections.Counter(data).values():
p_x = float(x)/len(data)
entropy -= p_x math.log(p_x, 2)
return entropy
queries = set([line.strip() for line in open('dns_queries.txt') if line.strip()])
for q in queries:
ent = shannon_entropy(q)
if ent > 3.8 or len(q) > 50:
print(f'[!] Suspicious DNS: {q} (Length: {len(q)}, Entropy: {ent:.2f})')
"
3. Extracting NTLM Hashes from the Wire
NTLM authentication is ubiquitous in Windows environments. If an attacker captures traffic on the local network, they can obtain NTLMv2 hashes and crack them offline. PCAP Bloodhound automatically parses NTLMSSP messages to extract server challenges and NTResponse hashes.
Step‑by‑step guide: Manual NTLM Extraction with tshark
To find these hashes manually, you need to filter for the NTLMSSP protocol signature.
Filter for NTLM negotiation traffic and extract the payload tshark -r capture.pcap -Y "ntlmssp.messagetype == 3" -T fields -e ntlmssp.auth.username -e ntlmssp.auth.domain -e ntlmssp.ntlmv2_response.ntproofstr -e ntlmssp.ntlmv2_response Alternatively, use a dedicated Python library like 'pyshark' or 'scapy' to parse the binary data. For quick analysis, you can use 'strings' to look for the "NTLMSSP" header: strings capture.pcap | grep -A 10 -B 5 "NTLMSSP"
Note: The extracted hashes can be saved in a format compatible with Hashcat (mode 5600 for NTLMv2).
4. Cleartext Credential Hunting
Despite best practices, many protocols still transmit credentials in plaintext. PCAP Bloodhound scans for HTTP Basic Auth (base64 encoded), FTP USER/PASS commands, and SMTP AUTH LOGIN.
Step‑by‑step guide: Using ngrep and tshark for Cleartext Creds
Using tshark to find HTTP Basic Auth (base64 encoded) tshark -r capture.pcap -Y "http.authorization" -T fields -e http.authorization Decode a captured base64 credential (e.g., "Basic YWRtaW46cGFzc3dvcmQ=") echo "YWRtaW46cGFzc3dvcmQ=" | base64 -d Using ngrep to sniff FTP credentials live (or from a file with -I) ngrep -I capture.pcap -q 'USER|PASS' port 21
5. Data Exfiltration Profiling
Attackers often steal data in large, asymmetric flows. PCAP Bloodhound flags outbound flows exceeding 1MB with a high send/receive ratio. This helps identify large data dumps over HTTP/HTTPS or FTP.
Step‑by‑step guide: Analyzing Flow Volumes with tshark and Python
Generate a conversation statistics report tshark -r capture.pcap -z conv,ip -q > conversations.txt Parse the output to find flows with high asymmetry Alternatively, use capinfos for summary statistics capinfos -c -y capture.pcap Use SiLK tools (if available) for advanced flow analysis, or a Python script: tshark -r capture.pcap -T fields -e ip.src -e ip.dst -e frame.len -Y "ip.src == 192.168.1.100" > flows.txt
6. Manual PCAP Validation with Linux CLI Arsenal
If you suspect the tool has flagged something, always validate manually. Here are essential commands every analyst should know:
– capinfos capture.pcap: Get a summary of the capture (duration, bitrate, packet size).
– tcpdump -nn -r capture.pcap -c 100 -A -s 0 | grep -i "password\|user\|NTLMSSP": Dump raw packet contents looking for keywords.
– tshark -r capture.pcap -q -z io,stat,1,"COUNT(frame) frame": View packet rate statistics to spot beaconing visually.
7. Mitigation and Hardening
Based on what Bloodhound finds, here are immediate hardening steps:
– For NTLM Exposure: Disable NTLMv1, enforce NTLMv2, and consider SMB signing. Use Group Policy: Network security: Restrict NTLM: Incoming NTLM traffic.
– For DNS Tunneling: Monitor DNS logs for high entropy. Implement threat intelligence feeds for known tunneling domains. Block outbound DNS over TCP except from authorized servers.
– For Cleartext Creds: Enforce HTTPS everywhere, disable Basic Auth, use SMTP-TLS, and migrate from FTP to SFTP/FTPS.
What Undercode Say:
PCAP Bloodhound represents a necessary shift from reactive inspection to proactive hunting. By automating statistical analysis, it removes the human latency that attackers exploit.
- Key Takeaway 1: Behavioral analysis (like CV on packet times) is superior to signature-based detection for finding unknown malware.
- Key Takeaway 2: Sensitive data like NTLM hashes and credentials are still shockingly common on internal networks; assume your internal wire is not secure.
- Analysis: The tool democratizes advanced threat hunting. What once required a decade of experience and intuition can now be scripted, allowing junior analysts to operate at senior levels. However, it also generates noise that requires careful tuning. As we move deeper into 2026, expect AI to further augment these engines, reducing false positives by understanding “normal” network behavior on a per-company basis.
Prediction:
The future of network security lies in “Trained Models for Traffic.” We will see a rise in open-source tools like PCAP Bloodhound being integrated into SIEMs and EDRs as native apps. As encryption (TLS 1.3, ECH) becomes ubiquitous, threat hunting will pivot away from payload inspection and toward encrypted traffic analysis (ETA) —using machine learning to fingerprint malware families solely by packet lengths, timings, and direction, regardless of encryption. The arms race will move entirely to the metadata layer.
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