ctf-malware

To analyze malware samples for CTF challenges, you can apply static and dynamic analysis techniques including PE file analysis, .NET application inspection, network traffic parsing, and memory forensics. This approach uncovers malware functionality and intent.

Memory forensics for malware analysis involves examining volatile memory dumps to extract artifacts and trace malicious behavior. Using frameworks like volatility3, this process reveals runtime modifications, injected code, and active network connections within the sample.

Yes, malware analysis requires a filesystem-based agent with bash, Python 3, and internet access for tool installation. It relies on dependencies like yara-python, pefile, capstone, unicorn, and volatility3 to execute static and dynamic inspection.

Analyzing network traffic and C2 protocols involves inspecting packet captures to identify communication patterns and command structures. This technique extracts indicators of compromise and reveals the malware's remote control mechanisms.

Yes, you can use pefile to parse PE headers and sections, and capstone to disassemble the extracted machine code. Combined, they provide a comprehensive static analysis workflow to reverse engineer executable malware logic.

You should avoid dynamic analysis when lacking an isolated filesystem-based agent environment, as executing malware requires strict containment. If internet access for tool installation is unavailable, deploying dynamic inspection methods will fail.