Malware Analysis Automation Playbook

While working on various automation projects over the years, I've realised the transformative power of a comprehensive SOAR program. Imagine if you were building a ship. Would you prefer to focus solely on crafting a polished engine or a pristine deck, leaving the rest unfinished? Of course not. Just as every part of a ship – from its keel to its crow's nest – plays a crucial role in ensuring it sails smoothly, in the world of cybersecurity, building an end-to-end automation and orchestration process is equally vital.

The use case I want to share addresses a ubiquitous yet often overlooked issue: the detection and remediation of malicious files on endpoints and servers. This is not merely about enhancing existing security measures; it's akin to building an entire vessel that's ready to face the high seas. Every component, or in our case, every automation process, needs to be meticulously crafted and integrated, ensuring a resilient and robust defense mechanism.

Leveraging automation, orchestration, and AI/ML technologies, this use case is not just about constructing individual parts of the ship, but about revolutionising your entire cybersecurity posture. It promises a substantial ROI by drastically reducing manual labor, improving incident response times, and increasing detection accuracy. Just as a shipbuilder would prioritize building a full, seaworthy vessel over isolated parts, we must emphasise creating comprehensive cybersecurity solutions over fragmented efforts.

Objective

To proactively and intelligently identify and neutralise malicious files on endpoints and servers.

Actors and Platforms (these are just examples not recommendations)

EDR Tool: CrowdStrike Falcon, SentinelOne, CarbonBlack

SOAR Platform/HyperAutomation: Torq, Palo Alto XSOAR, IBM Resilient, Splunk SOAR, Swimlane, Chronicle SOAR

Malware Analysis Tools: VirusTotal, Cuckoo Sandbox, Intezer, ReversingLabs

SIEM: Splunk, ArcSight, Qradar, Chronicle, Sentinel

Data Lake: AWS Lake Formation, Azure Data Lake Storage, Databricks

Threat Intelligence Platform: Anomali ThreatStream, ThreatConnect, ThreatQ, Eclectic IQ

AI/ML Models: Decision Trees, LSTM, Random Forest, Clustering algorithms

Workflow

1.Endpoint Monitoring/Malware Detection

Description: EDR tools continuously monitor activities on all endpoint devices. Configure the EDR tool to flag and automatically upload new binaries to an S3 bucket or Azure Blob within your data lake.

Technical Insight: Look for unusual patterns or behaviours, such as unauthorised file downloads or unusual network requests.

AI/ML Use: ML algorithms can adapt over time to reduce false positives based on unique organizational behavior.

Challenges: Ensuring real-time monitoring without impacting endpoint performance; false positives due to non-standard but benign user behaviour.

2. Data Lake Storage

Description: Upon detecting a new binary file on an endpoint, the EDR tool automatically sends this file to a centralised data lake or cloud storage.

Technical Insight: Utilise high-capacity storage solutions that can handle large volumes of data and offer quick retrieval functionalities.

AI/ML Use: AI algorithms optimise data storage, retrieval speeds, and prioritise data indexing.

Challenges: Data transfer delays; ensuring data integrity during transfer; storage scalability as data grows.

3.Malware Analysis

Description: The data lake communicates with the SOAR platform, initiating the analysis process for the suspicious file. Automation platform dispatches the file to various malware analysis engines:

Technical Insight: Implement API integrations for seamless communication between the data lake and the SOAR platform/Hyperautomation.

AI/ML Use: ML-driven sandbox environments evolve to trick sophisticated malware into revealing their behavior.

Malware Analysis Tools:

Static Analysis: Examines the file's code without executing it.

Dynamic Analysis: Observes the file's behaviour when executed in a sandbox environment.

DNA Analysis: Scans for known malware characteristics and traits.

Challenges: Integration complexities; version compatibility between different tools; ensuring secure communication. Evading sophisticated malware that detects sandboxed environments; time-consuming analysis for deeply obfuscated threats; keeping DNA analysis updated with latest threat intelligence.

4.Results Aggregator

Description: All analysis feedback is aggregated, consolidated, and processed to make a definitive decision on the file's nature.

Technical Insight: Utilize algorithms that weigh the feedback from different tools and determine if a file is malicious based on combined insights.

AI/ML Use: AI consolidates feedback, weighing results based on the reliability and past accuracy of each tool.

Challenges: Handling conflicting analysis results; potential for false negatives if one tool's feedback is inaccurately weighted.

5.Decision Engine

Description: If the aggregated results confirm the file's malicious nature, the engine triggers the next steps. Also you would need configure Human element for outliers.

Technical Insight: Incorporate conditional logic that drives the decision-making process.

AI/ML Use: Deep learning algorithms can identify and categorize new, previously unseen malware variants.

Challenges: Ensuring rapid decision-making without compromising accuracy; accounting for emerging threats that might not be fully understood yet.

6.Threat Intel Sharing & Network-Wide Search

Description: IOCs and YARA rules are disseminated to SIEM, Data Lake, and EDR tools. These tools then scan their datasets to identify other potential threats.

Technical Insight: Speed is crucial. Use optimised search queries and prioritise high-risk IOCs.

AI/ML Use: AI-enhanced SIEM systems can make real-time threat predictions based on live data streams.

Challenges: Avoiding overwhelming systems with massive queries; keeping the threat intelligence feeds updated; handling false positives in large datasets.

7.Incident Response

Description: Affected hosts are isolated, malicious files removed, and the system restored.

Technical Insight: Automated scripts can speed up the isolation and remediation processes. Ensure a backup system is in place for quick restoration.

AI/ML Use: AI tools automate the selection of restoration backups, choosing the most recent clean state.

Challenges: Minimising downtime during the response phase; ensuring thorough cleanup of threats; potential data loss during system restoration.

8.Reporting and Learning

Description: Update internal threat databases, generate detailed incident reports, and refine configurations for future threat detection.

Technical Insight: Always update threat intelligence feeds and use AI-driven algorithms to learn from each incident.

AI/ML Use: Reinforcement learning allows the system to evolve its defense mechanisms based on past incidents

Challenges: Ensuring lessons learned are effectively communicated and incorporated into future defenses; keeping the organisation updated with the latest threat landscape.