Malware analysis methodologies involve systematic approaches to understanding the behavior, purpose, and impact of malicious software (malware), like viruses, worms, Trojans, and ransomware. It is like dissecting malware samples to reveal their inner workings and uncovering their intended targets and actions. Here’s an outline of a typical methodology, which is often refined and adapted based on the specific goals of the analysis and the type of malware being examined;

1. Preparation;

1. Define Objectives: We need to determine the goals of the analysis, such as identifying the malware's functionality, origins, propagation methods, or potential countermeasures.
   
2. Establish Environment: We need to set up a controlled and isolated environment for analysis, such as a virtual machine or a dedicated sandbox, to prevent the malware from affecting our production systems.

2. Static Analysis:

1. File Identification: We have to identify the malware file(s) under investigation, which could be an executable, document, script, or other forms of malicious code.
   
2. File Metadata: We then need to extract metadata such as file size, creation/modification timestamps, digital signatures, and embedded resources to gather initial insights.
   
3. Hashing and Signature Matching: We generate file hashes next and compare them against known malware signatures to determine if the file is already documented as malicious.
   
4. File Structure Analysis: After that, we examine the structure of the file, including headers, sections, and embedded components, to understand its internal organization.
   
5. String Analysis: We then extract and analyze strings within the file, including plaintext strings, encoded/encrypted data, URLs, and API function calls, for potential indicators of malicious behavior.
   
6. Code Disassembly/Decompilation: Lastly, we disassemble or decompile the executable code to analyze the assembly language or high-level code, respectively, for suspicious or malicious instructions.

3. Dynamic Analysis:

1. Execution Environment Setup: We have to execute the malware sample in a controlled environment while monitoring its behavior to capture runtime activities without affecting the host system.
2. Behavioral Monitoring: Then we observe and record the malware's actions during execution, such as file system changes, network communications, process interactions, registry modifications, and system calls.
3. Network Traffic Analysis: We capture and analyze network traffic generated by the malware to identify communication protocols, command-and-control (C2) servers, data exfiltration, or other network-based behaviors.
4. Memory Analysis: We need to analyze the malware's runtime memory usage, including loaded modules, API calls, data structures, and potential injection techniques, using tools like memory forensics frameworks.
5. Malware Interaction: We have to interact with the malware dynamically, such as providing simulated inputs or observing responses to specific stimuli, to trigger different behaviors or stages of operation.

4. Code Analysis:

1. Reverse Engineering: We need to perform in-depth reverse engineering of the malware's code to understand its algorithms, encryption schemes, obfuscation techniques, and anti-analysis mechanisms.
2. Functionality Mapping: We have to identify and map the malware's functionality, such as keylogging, data theft, privilege escalation, or persistence mechanisms, by analyzing code segments and control flow.
3. Code Logic Reconstruction: We will then reconstruct higher-level logic and operational flows from the disassembled/decompiled code to comprehend the malware's operational patterns and decision-making processes.

5. Reporting and Documentation:

1. Analysis Report: We will compile a detailed report summarizing the findings from static and dynamic analysis, including malware characteristics, behavior patterns, IOCs (Indicators of Compromise), potential impact assessments, and recommended mitigation strategies.
2. Forensic Artifacts: We then document and preserve forensic artifacts, such as captured network traffic, memory dumps, registry snapshots, and file system changes, for future reference, research, or legal purposes.
3. Collaboration and Sharing: Lastly will share analysis results, IOCs, and relevant information with cybersecurity communities, threat intelligence platforms, or law enforcement agencies to contribute to collective defense efforts and improve cybersecurity awareness.