Investigating Cyber Intrusions Through Adversary Recon

Executive Summary

Goal: Provide a step‑by‑step methodology for analyzing, classifying, and evaluating threat reports, campaigns, and adversary behavior, with a focus on the reconnaissance phase.

Core Activities:

1. Threat‑report analysis (campaigns, TTPs, indicators).
2. End‑to‑end narrative reconstruction (from tasking to execution).
3. Noise‑vs‑signal discrimination (identifying genuine attacker activity).

Foundations of Reconnaissance

Concept	Description
Reconnaissance & Precursors	Initial preparatory work that includes tasking, tool acquisition, infrastructure set‑up, target identification, account creation, and lateral compromise.
Tasking	Strategic decision-making that drives the attack’s purpose. Crucial to motive but often unseen.
Attack‑Typologies	Non‑discriminating actors skip deep recon. targeted actors conduct extensive OSINT and social‑engineering.
Threat Hunting	Proactive search for indicators of compromise, ideally guided by threat intelligence.
Memory Forensics	Extraction of in‑memory artifacts (processes, network sockets, registry keys, browser history) to uncover hidden or deleted evidence.

Analytical Workflow

3.1 Gather & Classify Threat Reports

Sources:

1. Commercial threat‑intelligence feeds.
2. Open‑source OSINT portals (Shodan, Maltego).
3. Vendor incident reports.

Classification Criteria:

1. Campaign level: Grouped by actor, goal, or technique.
2. Indicator type: IPs, domains, file hashes, TTP signatures.
3. Evidence strength: Verified vs. unverified (e.g., cross‑checked with multiple feeds).

3.2 Evaluate Indicators & TTPs

Indicator Validation:

1. Check for repetition across reports.
2. Correlate with known MITRE ATT&CK tactics.

TTP Mapping:

1. Map indicators to ATT&CK techniques (e.g. T1059 PowerShell execution, T1078 Valid Accounts).
2. Identify pre‑attack footprints: DNS queries, SMB scans, credential dumping artifacts.

3.3 Reconstruct End‑to‑End Narratives

1. Tasking – Identify motive (e.g., hacktivism, espionage).
2. Reconnaissance – OSINT, phishing, network scanning.
3. Infrastructure – Proxy, bot‑net, compromised host.
4. Execution – Payload delivery, lateral movement, persistence.
5. Outcome – Data exfiltration, ransomware, sabotage.

Use timeline stitching from logs, memory artifacts, and threat‑intel feeds to produce a coherent narrative.

3.4 Distinguish Real Behavior (Signal from Noise)

Technique	How It Helps	Example
Cross‑source Correlation	Verify an indicator appears in multiple independent feeds.	IP seen in both commercial and open‑source reports.
Behavioral Consistency	Confirm TTPs match known actor patterns.	A group known for phishing also uses credential dumping.
Anomaly Scoring	Use SIEM anomaly detectors to flag unusual activity.	Sudden spike in SMB traffic to an internal server.
Artifact Forensics	Validate in‑memory evidence (processes, registry keys).	Memory snapshot shows PowerShell with obfuscated arguments.

Case Example: “Operation Dawnbreak”

Phase	Activity	Evidence	Analytical Insight
Tasking	Phishing email with malicious attachment.	Email gateway logs, user‑report.	Indicates phishing TTP (T1566.001).
Reconnaissance	External scanner probes internal SMB shares.	Netflow logs show high‑volume, short‑duration SMB traffic.	Consistent with T1046 Network Service Scanning.
Infrastructure	Adversary sets up a reverse proxy inside the network.	SIEM logs show new proxy process with unusual configuration.	Suggests T1090 Connection Proxy.
Account Creation	New service account with Domain Admin rights.	Active Directory logs.	Matches T1136 Create Account.
Execution	Ransomware payload dropped via compromised workstation.	File hash matches known ransomware family.	End‑to‑end narrative confirms a ransomware campaign.
Memory Forensics	Volatility analysis reveals hidden PowerShell session and encrypted payload.	Memory artifact confirms stealthy execution.	Validates that the attack was not a false positive.

The above investigation follows the SANS recon model and leverages memory‑forensic indicators.

Recommendations for Continuous Improvement

1. Integrate Threat‑Intel Feeds into SIEM – Automate correlation of indicators with live logs.
2. Deploy Memory‑Snapshot Automation – Trigger memory capture on high‑risk events (e.g. new admin account).
3. Establish a Threat‑Hunting Playbook – Include steps for verifying TTPs and building narratives.
4. Regular Training on Noise Filtering – Use cases to sharpen skills in distinguishing real signals.
5. Conduct Table‑top Exercises – Simulate reconnaissance scenarios to test detection and response workflows.

Takeaways

Analysts should carefully analyze threat reports, validate indicators and reconstruct attack narratives, this helps track adversary reconnaissance behavior with high fidelity. The combination of structured classification, behavioral validation, and memory‑based evidence enables analysts to distinguish genuine attacker activity from noise, ensuring that resources are focused on real threats.