Reverse engineering helps security analysts understand how malware works internally, what damage it can cause, and how it spreads. This knowledge allows organizations to build better defense strategies, detect similar threats, and improve threat intelligence systems.
The objective is to download or simulate known malware samples, use disassemblers or debuggers to analyze their execution, identify behavior such as registry manipulation or file encryption, and extract Indicators of Compromise (IOCs) like IP addresses, domains, and file hashes.
Disassemble malware binaries using tools like Ghidra or IDA Pro to inspect instructions and functions.
Run malware in a sandbox or VM and trace system calls, file activity, and network requests.
Detect embedded URLs, IPs, mutexes, and dropped files, and document them in a structured report.
Map malware behavior to MITRE ATT&CK techniques (e.g., persistence, lateral movement).
Malware samples are obtained from trusted research repositories. These binaries are analyzed in isolated environments where static disassembly is done to understand logic flow. Dynamic execution tracks real-time activity like file access, registry edits, and C2 communication.
Ghidra, x64dbg, Radare2, IDA Free — for binary static analysis.
Cuckoo Sandbox, Remnux VM, or FLARE VM for real-time execution monitoring.
Volatility for memory analysis, Wireshark for packet capture, custom scripts for parsing logs.
Markdown, PDF report templates, or tools like OpenCTI to store IOC data.
Download real-world or simulated malware binaries from safe research portals.
Use disassemblers to identify key code sections, obfuscation, and embedded strings.
Use a sandbox to observe runtime behavior like registry edits or C2 beaconing.
Identify hashes, dropped files, IPs, domains, and commands used by the malware.
Create a complete technical analysis report with threat classification and prevention suggestions.
Dive deep into malware internals with reverse engineering — a vital skill for defenders, researchers, and ethical hackers alike.
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