performing-malware-ioc-extraction

name performing-malware-ioc-extraction description Malware IOC extraction is the process of analyzing malicious software to identify actionable indicators of compromise including file hashes, network indicators (C2 domains, IP addresses, URLs), regist domain cybersecurity subdomain threat-intelligence tags ["threat-intelligence","cti","ioc","mitre-attack","stix","malware-analysis","yara","reverse-engineering"] version 1.0 author mahipal license Apache-2.0 nist_csf ["ID.RA-01","ID.RA-05","DE.CM-01","DE.AE-02"] mitre_attack ["T1591","T1592","T1593","T1589","T1071"] Performing Malware IOC Extraction Overview Malware IOC extraction is the process of analyzing malicious software to identify actionable indicators of compromise including file hashes, network indicators (C2 domains, IP addresses, URLs), registry modifications, mutex names, embedded strings, and behavioral artifacts. This skill covers static analysis with PE parsing and string extraction, dynamic analysis with sandbox detonation, automated IOC extraction using tools like YARA, and formatting results as STIX 2.1 indicators for sharing. When to Use When conducting security assessments that involve performing malware ioc extraction When following incident response procedures for related security events When performing scheduled security testing or auditing activities When validating security controls through hands-on testing Prerequisites Python 3.9+ with pefile , yara-python , oletools , stix2 libraries Access to malware analysis sandbox (Cuckoo, CAPE, Any.Run, Joe Sandbox) VirusTotal API key for enrichment Isolated analysis environment (VM or container) Understanding of PE file format, common packing techniques Familiarity with YARA rule syntax Key Concepts Static Analysis IOCs File Hashes : MD5, SHA-1, SHA-256 of the sample and any dropped files Import Hash (imphash) : Hash of imported function table, groups malware families Rich Header Hash : PE rich header hash for compiler fingerprinting Strings : Embedded URLs, IP addresses, domain names, registry paths, mutex names PE Metadata : Compilation timestamp, section names, resources, digital signatures Embedded Artifacts : PDB paths, version info, certificate details Dynamic Analysis IOCs Network Activity : DNS queries, HTTP requests, TCP/UDP connections, SSL certificates File System : Created/modified/deleted files and directories Registry : Created/modified registry keys and values Process : Spawned processes, injected processes, service creation Behavioral : API calls, mutex creation, scheduled tasks, persistence mechanisms YARA Rules YARA is a pattern-matching tool for identifying and classifying malware. Rules consist of strings (text, hex, regex) and conditions that define matching logic. Rules can detect malware families, packers, exploit kits, and specific campaign tools. Workflow Step 1: Static Analysis - PE Parsing and Hash Generation import pefile import hashlib import os def analyze_pe ( filepath ): """Extract IOCs from a PE file through static analysis.""" iocs = { "hashes" : {}, "pe_info" : {}, "strings" : [], "imports" : []} # Calculate file hashes with open (filepath, "rb" ) as f: data = f.read() iocs[ "hashes" ][ "md5" ] = hashlib.md5(data).hexdigest() iocs[ "hashes" ][ "sha1" ] = hashlib.sha1(data).hexdigest() iocs[ "hashes" ][ "sha256" ] = hashlib.sha256(data).hexdigest() iocs[ "hashes" ][ "file_size" ] = len (data) # Parse PE headers try : pe = pefile.PE(filepath) iocs[ "hashes" ][ "imphash" ] = pe.get_imphash() iocs[ "pe_info" ][ "compilation_time" ] = str (pe.FILE_HEADER.TimeDateStamp) iocs[ "pe_info" ][ "machine_type" ] = hex (pe.FILE_HEADER.Machine) iocs[ "pe_info" ][ "subsystem" ] = pe.OPTIONAL_HEADER.Subsystem # Extract sections iocs[ "pe_info" ][ "sections" ] = [] for section in pe.sections: iocs[ "pe_info" ][ "sections" ].append({ "name" : section.Name.decode( "utf-8" , errors= "ignore" ).strip( "\x00" ), "virtual_size" : section.Misc_VirtualSize, "raw_size" : section.SizeOfRawData, "entropy" : section.get_entropy(), "md5" : section.get_hash_md5(), }) # Extract imports if hasattr (pe, "DIRECTORY_ENTRY_IMPORT" ): for entry in pe.DIRECTORY_ENTRY_IMPORT: dll_name = entry.dll.decode( "utf-8" , errors= "ignore" ) functions = [ imp.name.decode( "utf-8" , errors= "ignore" ) for imp in entry.imports if imp.name ] iocs[ "imports" ].append({ "dll" : dll_name, "functions" : functions}) # Check for suspicious characteristics iocs[ "pe_info" ][ "is_dll" ] = pe.is_dll() iocs[ "pe_info" ][ "is_driver" ] = pe.is_driver() iocs[ "pe_info" ][ "is_exe" ] = pe.is_exe() # Version info if hasattr (pe, "VS_VERSIONINFO" ): for entry in pe.FileInfo: for st in entry: for item in st.entries.items(): key = item[ 0 ].decode( "utf-8" , errors= "ignore" ) val = item[ 1 ].decode( "utf-8" , errors= "ignore" ) iocs[ "pe_info" ][ f"version_ {key} " ] = val pe.close() except pefile.PEFormatError as e: iocs[ "pe_info" ][ "error" ] = str (e) return iocs Step 2: String Extraction and IOC Pattern Matching import re def extract_ioc_strings ( filepath ): """Extract IOC-relevant strings from binary file.""" patterns = { "ipv4" : re. compile ( r"\b(?:(?:25[0-5]|2[0-4]\d|[01]?\d\d?)\.){3}" r"(?:25[0-5]|2[0-4]\d|[01]?\d\d?)\b" ), "domain" : re. compile ( r"\b(?:[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?\.)+" r"(?:com|net|org|io|ru|cn|tk|xyz|top|info|biz|cc|ws|pw)\b" ), "url" : re. compile ( r"https?://[^\s\"'<>]{5,200}" ), "email" : re. compile ( r"\b[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}\b" ), "registry" : re. compile ( r"(?:HKEY_[A-Z_]+|HKLM|HKCU|HKU|HKCR|HKCC)" r"\\[\\a-zA-Z0-9_ .{}-]+" ), "filepath_windows" : re. compile ( r"[A-Z]:\\(?:[^\\/:*?\"<>|\r\n]+\\)*[^\\/:*?\"<>|\r\n]+" ), "mutex" : re. compile ( r"(?:Global\\|Local\\)[a-zA-Z0-9_\-{}.]{4,}" ), "useragent" : re. compile ( r"Mozilla/[45]\.0[^\"']{10,200}" ), "bitcoin" : re. compile ( r"\b[13][a-km-zA-HJ-NP-Z1-9]{25,34}\b" ), "pdb_path" : re. compile ( r"[A-Z]:\\[^\"]{5,200}\.pdb" ), } with open (filepath, "rb" ) as f: data = f.read() # Extract ASCII strings (min length 4) ascii_strings = re.findall( rb"[\x20-\x7e]{4,}" , data) # Extract Unicode strings unicode_strings = re.findall( rb"(?:[\x20-\x7e]\x00){4,}" , data ) all_strings = [s.decode( "ascii" , errors= "ignore" ) for s in ascii_strings] all_strings += [ s.decode( "utf-16-le" , errors= "ignore" ) for s in unicode_strings ] extracted = {category: set () for category in patterns} for string in all_strings: for category, pattern in patterns.items(): matches = pattern.findall(string) for match in matches: extracted[category].add( match ) # Convert sets to sorted lists return {k: sorted (v) for k, v in extracted.items() if v} Step 3: YARA Rule Scanning import yara def scan_with_yara ( filepath, rules_path ): """Scan file with YARA rules for malware classification.""" rules = yara. compile (filepath=rules_path) matches = rules. match (filepath) results = [] for match in matches: result = { "rule" : match .rule, "namespace" : match .namespace, "tags" : match .tags, "meta" : match .meta, "strings" : [], } for offset, identifier, data in match .strings: result[ "strings" ].append({ "offset" : hex (offset), "identifier" : identifier, "data" : data. hex () if len (data) < 100 else data[: 100 ]. hex () + "..." , }) results.append(result) return results # Example YARA rule for common malware indicators SAMPLE_YARA_RULE = """ rule Suspicious_Network_Indicators { meta: description = "Detects suspicious network-related strings" author = "CTI Analyst" severity = "medium" strings: $ua1 = "Mozilla/5.0" ascii $cmd1 = "cmd.exe /c" ascii nocase $ps1 = "powershell" ascii nocase $wget = "wget" ascii nocase $curl = "curl" ascii nocase $b64 = "base64" ascii nocase $reg1 = "HKLM\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Run" ascii nocase condition: uint16(0) == 0x5A4D and (2 of ($ua1, $cmd1, $ps1, $wget, $curl, $b64)) or $reg1 } rule Packed_Binary { meta: description = "Detects potentially packed binary" author = "CTI Analyst" condition: uint16(0) == 0x5A4D and for any section in pe.sections : ( section.entropy >= 7.0 ) } """ Step 4: Generate STIX 2.1 Indicators from stix2 import ( Bundle, Indicator, Malware, Relationship, File as STIXFile, DomainName, IPv4Address, ObservedData, ) from datetime import datetime def create_stix_bundle ( pe_iocs, string_iocs, yara_results, sample_name ): """Create STIX 2.1 bundle from extracted IOCs.""" objects = [] # Create Malware SDO malware = Malware( name=sample_name, is_family= False , malware_types=[ "unknown" ], description= f"Malware sample analyzed: {pe_iocs[ 'hashes' ][ 'sha256' ]} " , allow_custom= True , ) objects.append(malware) # File hash indicator sha256 = pe_iocs[ "hashes" ][ "sha256" ] hash_indicator = Indicator( name= f"Malware hash: {sha256[: 16 ]} ..." , pattern= f"[file:hashes.'SHA-256' = ' {sha256} ']" , pattern_type= "stix" , valid_from=datetime.now().strftime( "%Y-%m-%dT%H:%M:%SZ" ), indicator_types=[ "malicious-activity" ], allow_custom= True , ) objects.append(hash_indicator) objects.append(Relationship( relationship_type= "indicates" , source_ref=hash_indicator. id , target_ref=malware. id , )) # Network indicators from strings for ip in string_iocs.get( "ipv4" , []): if not ip.startswith(( "10." , "172." , "192.168." , "127." )): ip_indicator = Indicator( name= f"C2 IP: {ip} " , pattern= f"[ipv4-addr:value = ' {ip} ']" , pattern_type= "stix" , valid_from=datetime.now().strftime( "%Y-%m-%dT%H:%M:%SZ" ), indicator_types=[ "malicious-activity" ], allow_custom= True , ) objects.append(ip_indicator) objects.append(Relationship( relationship_type= "indicates" , source_ref=ip_indicator. id , target_ref=malware. id , )) for domain in string_iocs.get( "domain" , []): domain_indicator = Indicator( name= f"C2 Domain: {domain} " ,