Essential Checklist for Executable File (EXE/DLL) Malware Analysis

By /

Listen to this Post

Featured Image

Introduction

Malware analysis is a critical skill in cybersecurity, especially when dealing with executable files (EXE) and dynamic link libraries (DLLs). Understanding how to dissect these files helps security professionals detect, mitigate, and prevent malicious attacks. This guide provides a structured approach to malware analysis, covering static and dynamic techniques, debugging, and threat intelligence extraction.

Learning Objectives

  • Learn how to perform static and dynamic analysis on executable files.
  • Identify common malware obfuscation techniques like packing and encryption.
  • Extract Indicators of Compromise (IOCs) for threat intelligence.

You Should Know

1️⃣ File Identification & Metadata Extraction

Tool: `PEiD`, `TrID`

Command:

file suspicious.exe

Step-by-Step Guide:

  1. Use `file` (Linux) or `PEiD` (Windows) to determine the file type.

2. Extract metadata (compiler info, timestamps) using `exiftool`:

exiftool suspicious.exe

Why It Matters: Helps verify if the file is masquerading as a legitimate executable.

2️⃣ Static Analysis with PE Headers

Tool: `PEView`, `PEStudio`

Command:

pestudio suspicious.exe

Step-by-Step Guide:

  1. Open the file in `PEStudio` to inspect sections (.text, .data).

2. Check for suspicious imports (e.g., `VirtualAlloc`, `CreateRemoteThread`).

Why It Matters: Malware often manipulates PE headers to evade detection.

3️⃣ String & YARA Analysis

Tool: `strings`, `YARA`

Command:

strings -n 8 suspicious.exe | grep -i "http|reg"

Step-by-Step Guide:

1. Extract ASCII/Unicode strings with `strings`.

  1. Run YARA rules to detect known malware signatures: 
    yara -r malware_rules.yar suspicious.exe

    Why It Matters: Reveals hardcoded URLs, registry keys, or C2 server addresses.

4️⃣ Packing & Entropy Check

Tool: `Detect It Easy (DiE)`, `PEStudio`

Command:

diec suspicious.exe

Step-by-Step Guide:

1. Use `DiE` to detect packers (UPX, Themida).

2. High entropy (>7.0) suggests encryption or compression.

Why It Matters: Packers hide malicious code from static analysis.

5️⃣ Dynamic Analysis in a Sandbox

Tool: `Cuckoo Sandbox`, `Any.run`

Command:

cuckoo submit suspicious.exe

Step-by-Step Guide:

1. Submit the file to `Cuckoo Sandbox`.

  1. Monitor process injection, file drops, and network calls.
    Why It Matters: Captures runtime behavior missed in static analysis.

6️⃣ API & Network Monitoring

Tool: `Wireshark`, `API Monitor`

Command:

tcpdump -i eth0 -w traffic.pcap

Step-by-Step Guide:

1. Capture traffic with `Wireshark` during execution.

  1. Filter for DNS queries or HTTP POST requests.
    Why It Matters: Identifies C2 communication or data exfiltration.

7️⃣ Debugging & Memory Analysis

Tool: `x64dbg`, `Volatility`

Command:

volatility -f memory.dump --profile=Win10 pslist

Step-by-Step Guide:

  1. Debug with `x64dbg` to trace malicious API calls.

2. Dump memory and analyze with `Volatility`.

Why It Matters: Uncovers hidden processes or code injection.

8️⃣ Persistence Mechanism Detection

Tool: `Process Monitor`, `Sysmon`

Command:

sysmon -accepteula -i config.xml

Step-by-Step Guide:

1. Log registry/fs changes with `Sysmon`.

2. Check for scheduled tasks or startup entries.

Why It Matters: Malware often establishes persistence via AutoRun or services.

9️⃣ IOC Extraction & Reporting

Tool: `VirusTotal`, `MISP`

Command:

md5sum suspicious.exe

Step-by-Step Guide:

1. Generate hashes (MD5, SHA-1) for the file.

2. Submit to `VirusTotal` for crowdsourced analysis.

Why It Matters: IOCs enable threat hunting and blacklisting.

What Undercode Say

  • Key Takeaway 1: Malware analysis requires a hybrid approach—combining static and dynamic techniques.
  • Key Takeaway 2: Automation (sandboxes, YARA) speeds up analysis, but manual review is irreplaceable.

Analysis: As malware evolves, analysts must adapt by leveraging AI-driven tools (e.g., CAPE Sandbox) and sharing IOCs via platforms like MISP. The rise of fileless malware demands deeper memory forensics, making tools like Volatility essential. Future threats will likely exploit AI-generated code, requiring advanced behavioral analysis.

Prediction: By 2025, AI-powered malware will bypass traditional signatures, forcing reliance on anomaly detection and runtime monitoring. Investing in threat intelligence sharing will be critical for collective defense.

IT/Security Reporter URL:

Reported By: Ouardi Mohamed – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅

Join Our Cyber World:

💬 Whatsapp | 💬 Telegram

Related Posts: