How To Hunt Web And Network-Based Threats From Packet Capture To Payload

Modern cyberattacks increasingly exploit network protocols and web applications to bypass traditional security controls.

To counter these threats, security teams must adopt advanced techniques for analyzing raw network traffic, from packet-level metadata to payload content.

This article provides a technical deep dive into hunting web and network-based threats using packet capture (PCAP) analysis, with practical examples and methodologies for identifying malicious activity.

Fundamentals Of Network Packet Analysis

Packet capture forms the foundation of network-based threat hunting, providing an unalterable record of all traffic traversing a network.

Each packet contains headers, which hold routing information, payloads with the actual data, and trailers for error-checking.

Security analysts use this data to reconstruct communication patterns, identify anomalies, and uncover hidden threats.

Enterprise-grade packet capture solutions deploy sensors at strategic points in the network, such as network taps or SPAN ports, to collect traffic without impacting performance.

For example, a financial institution might capture all traffic to its online banking portal, storing packets for 30 days to enable retrospective analysis.

Key considerations when implementing packet capture include optimizing storage through deduplication and protocol filtering, as well as ensuring accurate time synchronization across distributed sensors for forensic correlation.

A retail company, for instance, reduced incident investigation time by 60 percent after implementing a packet capture system that automatically flagged anomalous SQL database connections.

Flow Analysis For Baseline Establishment

Before hunting threats, analysts must understand what normal network behavior looks like.

Flow analysis tools such as Zeek process packet headers to generate connection logs, which include the five-tuple data of source and destination IPs, ports, and protocol, as well as session duration.

This allows teams to distinguish between long-lived connections, such as eight-hour SSH sessions, and short spikes, such as DNS brute-forcing attempts.

For example, a healthcare provider detected a credential-stuffing attack by flagging 150 SSH login attempts from a single IP within two minutes, far exceeding their baseline of five logins per hour per IP.

Packet Capture Architecture

Enterprise environments require robust packet capture architectures. Sensors are deployed at ingress and egress points, and captured data is streamed to centralized storage for analysis.

Time synchronization is crucial for correlating events across distributed environments.

Storage optimization techniques, such as deduplication and selective protocol capture, help manage the high volume of data.

For example, capturing only HTTP, DNS, and SMB traffic can significantly reduce storage requirements while still providing comprehensive coverage for threat hunting.

Payload Inspection Techniques

While headers reveal who communicated with whom, payloads answer what was communicated.

Attackers increasingly hide malicious content within protocol-compliant traffic, necessitating deep payload analysis.

One effective technique is N-Gram entropy analysis, which detects obfuscated payloads by calculating the randomness of byte sequences.

• To identify encrypted command-and-control (C2) traffic, analysts can extract the TCP payload from a suspected malware packet.
• They calculate the frequency of every two-byte combination (bigrams) in the payload.
• Entropy is then computed using the formula: H = -Σ(p(x) log2 p(x)), where p(x) is the probability of each bigram.
• Legitimate HTTP traffic typically has entropy of 4.5 or lower, while encrypted payloads score 7.0 or higher.
• In one real-world case, a ransomware’s exfiltration channel was uncovered when its TLS-encrypted payloads showed entropy of 7.8, contrasting with normal HTTPS traffic at 5.2.

Each protocol requires tailored inspection rules.

For HTTP and HTTPS, analysts look for header manipulation, such as a C2 server responding with an HTTP 404 status but including a suspiciously large Content-Length header, or parameter obfuscation, such as a compromised e-commerce site sending a GET request with a Base64-encoded search parameter to exfiltrate sensitive documents.

For DNS, tunneling detection is critical. For example, a DNS query for a 63-character subdomain was flagged because it contained hex-encoded exfiltrated data, exploiting the maximum allowed length for subdomains.

Advanced Threat Hunting Methodologies

Sophisticated attackers use multi-stage campaigns that blend into normal traffic, so advanced threat hunting methodologies are needed.

Lateral movement techniques often leave subtle network footprints. In a pass-the-hash attack, for example, a Windows workstation initiated 50 SMB connections to different servers in 10 minutes.

Packet capture showed NTLMv1 authentication with identical login timestamps across hosts. This behavior matched known tactics for lateral movement and triggered an alert.

Preventive rules can be established, such as alerting on SMB authentication spikes greater than 20 attempts in five minutes with reused NTLM hashes.

Detecting data exfiltration requires a combination of baselining normal traffic and identifying deviations. Attackers often disguise exfiltration within common protocols like DNS or HTTP.

For example, DNS tunneling may involve encoding stolen data into subdomains of malicious queries, while HTTP POST requests might transmit large volumes of unstructured data to external servers.

Analysts begin by establishing baselines for typical DNS query sizes or HTTP upload volumes.

Outliers, such as DNS requests with abnormally long subdomains or HTTP sessions transmitting gigabytes of data, are flagged for deeper inspection.

Entropy analysis further refines detection: tools can calculate payload randomness, where high entropy in DNS TXT records or HTTP payloads may indicate Base64-encoded exfiltrated data.

In a university environment, normal DNS queries averaged 45 bytes, but a sudden appearance of 512-byte TXT record queries to a suspicious domain with entropy of 7.6 revealed a covert channel for student record exfiltration.

By correlating these technical indicators with contextual data, such as activity outside business hours, security teams can intercept exfiltration before sensitive data leaves the network.

Effective threat hunting requires correlating packet-level artifacts with an understanding of attacker tactics.

By combining flow analysis, entropy calculations, and protocol-specific rules, security teams can detect threats like ransomware, data exfiltration, and lateral movement even when attackers use encryption or legitimate protocols.

Practical steps for implementation include deploying network sensors at both cloud and on-premises egress points, building automated playbooks to flag entropy anomalies and protocol violations, and conducting regular hunts for SMB authentication spikes and abnormal DNS patterns.

Organizations that master these techniques transform raw packet data into a strategic defense asset, enabling proactive identification of threats before they escalate into breaches.

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